Speed Limits: Orientation and Semantic Context Interactions Constrain Natural Scene Discrimination Dynamics

ERIC Educational Resources Information Center

Rieger, Jochem W.; Kochy, Nick; Schalk, Franziska; Gruschow, Marcus; Heinze, Hans-Jochen

2008-01-01

The visual system rapidly extracts information about objects from the cluttered natural environment. In 5 experiments, the authors quantified the influence of orientation and semantics on the classification speed of objects in natural scenes, particularly with regard to object-context interactions. Natural scene photographs were presented in an…

Object links in the repository

NASA Technical Reports Server (NTRS)

Beck, Jon; Eichmann, David

1991-01-01

Some of the architectural ramifications of extending the Eichmann/Atkins lattice-based classification scheme to encompass the assets of the full life-cycle of software development are explored. In particular, we wish to consider a model which provides explicit links between objects in addition to the edges connecting classification vertices in the standard lattice. The model we consider uses object-oriented terminology. Thus, the lattice is viewed as a data structure which contains class objects which exhibit inheritance. A description of the types of objects in the repository is presented, followed by a discussion of how they interrelate. We discuss features of the object-oriented model which support these objects and their links, and consider behavior which an implementation of the model should exhibit. Finally, we indicate some thoughts on implementing a prototype of this repository architecture.

Woodland Mapping at Single-Tree Levels Using Object-Oriented Classification of Unmanned Aerial Vehicle (uav) Images

NASA Astrophysics Data System (ADS)

Chenari, A.; Erfanifard, Y.; Dehghani, M.; Pourghasemi, H. R.

2017-09-01

Remotely sensed datasets offer a reliable means to precisely estimate biophysical characteristics of individual species sparsely distributed in open woodlands. Moreover, object-oriented classification has exhibited significant advantages over different classification methods for delineation of tree crowns and recognition of species in various types of ecosystems. However, it still is unclear if this widely-used classification method can have its advantages on unmanned aerial vehicle (UAV) digital images for mapping vegetation cover at single-tree levels. In this study, UAV orthoimagery was classified using object-oriented classification method for mapping a part of wild pistachio nature reserve in Zagros open woodlands, Fars Province, Iran. This research focused on recognizing two main species of the study area (i.e., wild pistachio and wild almond) and estimating their mean crown area. The orthoimage of study area was consisted of 1,076 images with spatial resolution of 3.47 cm which was georeferenced using 12 ground control points (RMSE=8 cm) gathered by real-time kinematic (RTK) method. The results showed that the UAV orthoimagery classified by object-oriented method efficiently estimated mean crown area of wild pistachios (52.09±24.67 m2) and wild almonds (3.97±1.69 m2) with no significant difference with their observed values (α=0.05). In addition, the results showed that wild pistachios (accuracy of 0.90 and precision of 0.92) and wild almonds (accuracy of 0.90 and precision of 0.89) were well recognized by image segmentation. In general, we concluded that UAV orthoimagery can efficiently produce precise biophysical data of vegetation stands at single-tree levels, which therefore is suitable for assessment and monitoring open woodlands.

About turn: how object orientation affects categorisation and mental rotation.

PubMed

Milivojevic, Branka; Hamm, Jeff P; Corballis, Michael C

2011-11-01

High-density ERPs evoked by rotated alphanumeric characters were examined to determine how neural processing is affected by stimulus orientation during letter/digit classifications and during mirror/normal discriminations. The former task typically produces response times that are unaffected by stimulus orientation while the latter is thought to require mental rotation. Sensitivity to orientation was first observed around 100-140 ms and this effect was attributed to differences in low-level features between vertical and oblique orientations. Subsequently, character misorientation amplified the N170, a neural marker of object classification, between 160 and 220 ms. Top-down processing is reflected in the ERPs beginning at 280-320 ms and this time range may reflect binding of ventral and dorsal stream information. In the case of mirror-normal discrimination these top-down processes can lead to mental rotation between 340 and 700 ms. Therefore, although neural processing reflects object orientation, these effects do not translate into increases in reaction-times or impaired accuracy for categorisation, and precede those that do in the mental-rotation task. Copyright © 2011 Elsevier Ltd. All rights reserved.

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

Decision Tree Repository and Rule Set Based Mingjiang River Estuarine Wetlands Classifaction

NASA Astrophysics Data System (ADS)

Zhang, W.; Li, X.; Xiao, W.

2018-05-01

The increasing urbanization and industrialization have led to wetland losses in estuarine area of Mingjiang River over past three decades. There has been increasing attention given to produce wetland inventories using remote sensing and GIS technology. Due to inconsistency training site and training sample, traditionally pixel-based image classification methods can't achieve a comparable result within different organizations. Meanwhile, object-oriented image classification technique shows grate potential to solve this problem and Landsat moderate resolution remote sensing images are widely used to fulfill this requirement. Firstly, the standardized atmospheric correct, spectrally high fidelity texture feature enhancement was conducted before implementing the object-oriented wetland classification method in eCognition. Secondly, we performed the multi-scale segmentation procedure, taking the scale, hue, shape, compactness and smoothness of the image into account to get the appropriate parameters, using the top and down region merge algorithm from single pixel level, the optimal texture segmentation scale for different types of features is confirmed. Then, the segmented object is used as the classification unit to calculate the spectral information such as Mean value, Maximum value, Minimum value, Brightness value and the Normalized value. The Area, length, Tightness and the Shape rule of the image object Spatial features and texture features such as Mean, Variance and Entropy of image objects are used as classification features of training samples. Based on the reference images and the sampling points of on-the-spot investigation, typical training samples are selected uniformly and randomly for each type of ground objects. The spectral, texture and spatial characteristics of each type of feature in each feature layer corresponding to the range of values are used to create the decision tree repository. Finally, with the help of high resolution reference images, the random sampling method is used to conduct the field investigation, achieve an overall accuracy of 90.31 %, and the Kappa coefficient is 0.88. The classification method based on decision tree threshold values and rule set developed by the repository, outperforms the results obtained from the traditional methodology. Our decision tree repository and rule set based object-oriented classification technique was an effective method for producing comparable and consistency wetlands data set.

Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data

PubMed Central

Myint, Soe W.; Yuan, May; Cerveny, Randall S.; Giri, Chandra P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. PMID:27879757

Object-Oriented Approach to Integrating Database Semantics. Volume 4.

DTIC Science & Technology

1987-12-01

schemata for; 1. Object Classification Shema -- Entities 2. Object Structure and Relationship Schema -- Relations 3. Operation Classification and... relationships are represented in a database is non- intuitive for naive users. *It is difficult to access and combine information in multiple databases. In this...from the CURRENT-.CLASSES table. Choosing a selected item do-selects it. Choose 0 to exit. 1. STUDENTS 2. CUR~RENT-..CLASSES 3. MANAGMNT -.CLASS

About Turn: How Object Orientation Affects Categorisation and Mental Rotation

ERIC Educational Resources Information Center

Milivojevic, Branka; Hamm, Jeff P.; Corballis, Michael C.

2011-01-01

High-density ERPs evoked by rotated alphanumeric characters were examined to determine how neural processing is affected by stimulus orientation during letter/digit classifications and during mirror/normal discriminations. The former task typically produces response times that are unaffected by stimulus orientation while the latter is thought to…

Comparison of using single- or multi-polarimetric TerraSAR-X images for segmentation and classification of man-made maritime objects

NASA Astrophysics Data System (ADS)

Teutsch, Michael; Saur, Günter

2011-11-01

Spaceborne SAR imagery offers high capability for wide-ranging maritime surveillance especially in situations, where AIS (Automatic Identification System) data is not available. Therefore, maritime objects have to be detected and optional information such as size, orientation, or object/ship class is desired. In recent research work, we proposed a SAR processing chain consisting of pre-processing, detection, segmentation, and classification for single-polarimetric (HH) TerraSAR-X StripMap images to finally assign detection hypotheses to class "clutter", "non-ship", "unstructured ship", or "ship structure 1" (bulk carrier appearance) respectively "ship structure 2" (oil tanker appearance). In this work, we extend the existing processing chain and are now able to handle full-polarimetric (HH, HV, VH, VV) TerraSAR-X data. With the possibility of better noise suppression using the different polarizations, we slightly improve both the segmentation and the classification process. In several experiments we demonstrate the potential benefit for segmentation and classification. Precision of size and orientation estimation as well as correct classification rates are calculated individually for single- and quad-polarization and compared to each other.

Identification of cultivated land using remote sensing images based on object-oriented artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Li, Nan; Zhu, Xiufang

2017-04-01

Cultivated land resources is the key to ensure food security. Timely and accurate access to cultivated land information is conducive to a scientific planning of food production and management policies. The GaoFen 1 (GF-1) images have high spatial resolution and abundant texture information and thus can be used to identify fragmentized cultivated land. In this paper, an object-oriented artificial bee colony algorithm was proposed for extracting cultivated land from GF-1 images. Firstly, the GF-1 image was segmented by eCognition software and some samples from the segments were manually identified into 2 types (cultivated land and non-cultivated land). Secondly, the artificial bee colony (ABC) algorithm was used to search for classification rules based on the spectral and texture information extracted from the image objects. Finally, the extracted classification rules were used to identify the cultivated land area on the image. The experiment was carried out in Hongze area, Jiangsu Province using wide field-of-view sensor on the GF-1 satellite image. The total precision of classification result was 94.95%, and the precision of cultivated land was 92.85%. The results show that the object-oriented ABC algorithm can overcome the defect of insufficient spectral information in GF-1 images and obtain high precision in cultivated identification.

Using object-oriented classification and high-resolution imagery to map fuel types in a Mediterranean region.

Treesearch

L. Arroyo; S.P. Healey; W.B. Cohen; D. Cocero; J.A. Manzanera

2006-01-01

Knowledge of fuel load and composition is critical in fighting, preventing, and understanding wildfires. Commonly, the generation of fuel maps from remotely sensed imagery has made use of medium-resolution sensors such as Landsat. This paper presents a methodology to generate fuel type maps from high spatial resolution satellite data through object-oriented...

Guide to NavyFOAM V1.0

DTIC Science & Technology

2011-04-01

NavyFOAM has been developed using an open-source CFD software tool-kit ( OpenFOAM ) that draws heavily upon object-oriented programming. The...numerical methods and the physical models in the original version of OpenFOAM have been upgraded in an effort to improve accuracy and robustness of...computational fluid dynamics OpenFOAM , Object Oriented Programming (OOP) (CFD), NavyFOAM, 16. SECURITY CLASSIFICATION OF: a. REPORT UNCLASSIFIED b

A Scalable, Open Source Platform for Data Processing, Archiving and Dissemination

DTIC Science & Technology

2016-01-01

Object Oriented Data Technology (OODT) big data toolkit developed by NASA and the Work-flow INstance Generation and Selection (WINGS) scientific work...to several challenge big data problems and demonstrated the utility of OODT-WINGS in addressing them. Specific demonstrated analyses address i...source software, Apache, Object Oriented Data Technology, OODT, semantic work-flows, WINGS, big data , work- flow management 16. SECURITY CLASSIFICATION OF

Image classification independent of orientation and scale

NASA Astrophysics Data System (ADS)

Arsenault, Henri H.; Parent, Sebastien; Moisan, Sylvain

1998-04-01

The recognition of targets independently of orientation has become fairly well developed in recent years for in-plane rotation. The out-of-plane rotation problem is much less advanced. When both out-of-plane rotations and changes of scale are present, the problem becomes very difficult. In this paper we describe our research on the combined out-of- plane rotation problem and the scale invariance problem. The rotations were limited to rotations about an axis perpendicular to the line of sight. The objects to be classified were three kinds of military vehicles. The inputs used were infrared imagery and photographs. We used a variation of a method proposed by Neiberg and Casasent, where a neural network is trained with a subset of the database and a minimum distances from lines in feature space are used for classification instead of nearest neighbors. Each line in the feature space corresponds to one class of objects, and points on one line correspond to different orientations of the same target. We found that the training samples needed to be closer for some orientations than for others, and that the most difficult orientations are where the target is head-on to the observer. By means of some additional training of the neural network, we were able to achieve 100% correct classification for 360 degree rotation and a range of scales over a factor of five.

Object-oriented and pixel-based classification approach for land cover using airborne long-wave infrared hyperspectral data

NASA Astrophysics Data System (ADS)

Marwaha, Richa; Kumar, Anil; Kumar, Arumugam Senthil

2015-01-01

Our primary objective was to explore a classification algorithm for thermal hyperspectral data. Minimum noise fraction is applied to thermal hyperspectral data and eight pixel-based classifiers, i.e., constrained energy minimization, matched filter, spectral angle mapper (SAM), adaptive coherence estimator, orthogonal subspace projection, mixture-tuned matched filter, target-constrained interference-minimized filter, and mixture-tuned target-constrained interference minimized filter are tested. The long-wave infrared (LWIR) has not yet been exploited for classification purposes. The LWIR data contain emissivity and temperature information about an object. A highest overall accuracy of 90.99% was obtained using the SAM algorithm for the combination of thermal data with a colored digital photograph. Similarly, an object-oriented approach is applied to thermal data. The image is segmented into meaningful objects based on properties such as geometry, length, etc., which are grouped into pixels using a watershed algorithm and an applied supervised classification algorithm, i.e., support vector machine (SVM). The best algorithm in the pixel-based category is the SAM technique. SVM is useful for thermal data, providing a high accuracy of 80.00% at a scale value of 83 and a merge value of 90, whereas for the combination of thermal data with a colored digital photograph, SVM gives the highest accuracy of 85.71% at a scale value of 82 and a merge value of 90.

Mapping land cover in urban residential landscapes using fine resolution imagery and object-oriented classification

USDA-ARS?s Scientific Manuscript database

A knowledge of different types of land cover in urban residential landscapes is important for building social and economic city-wide policies including landscape ordinances and water conservation programs. Urban landscapes are typically heterogeneous, so classification of land cover in these areas ...

Extraction of Shrimp Ponds Using Object Oriented Classification vis-a-vis Pixel Based Classification

DTIC Science & Technology

2004-11-01

302 25th ACRS 2004 Chiang Mai , Thailand B-3.6 Data Processing...Proceedings of the 25th Asian Conference on Remote Sensing, Held in Chiang Mai , Thailand on 22-26 November 2004. Copyrighted; Government Purpose Rights... Chiang Mai , Thailand B-3.6 Data Processing

[Object-oriented aquatic vegetation extracting approach based on visible vegetation indices.

PubMed

Jing, Ran; Deng, Lei; Zhao, Wen Ji; Gong, Zhao Ning

2016-05-01

Using the estimation of scale parameters (ESP) image segmentation tool to determine the ideal image segmentation scale, the optimal segmented image was created by the multi-scale segmentation method. Based on the visible vegetation indices derived from mini-UAV imaging data, we chose a set of optimal vegetation indices from a series of visible vegetation indices, and built up a decision tree rule. A membership function was used to automatically classify the study area and an aquatic vegetation map was generated. The results showed the overall accuracy of image classification using the supervised classification was 53.7%, and the overall accuracy of object-oriented image analysis (OBIA) was 91.7%. Compared with pixel-based supervised classification method, the OBIA method improved significantly the image classification result and further increased the accuracy of extracting the aquatic vegetation. The Kappa value of supervised classification was 0.4, and the Kappa value based OBIA was 0.9. The experimental results demonstrated that using visible vegetation indices derived from the mini-UAV data and OBIA method extracting the aquatic vegetation developed in this study was feasible and could be applied in other physically similar areas.

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost

PubMed Central

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited. PMID:26528811

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.

PubMed

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited.

Extraction and Analysis of Major Autumn Crops in Jingxian County Based on Multi - Temporal gf - 1 Remote Sensing Image and Object-Oriented

NASA Astrophysics Data System (ADS)

Ren, B.; Wen, Q.; Zhou, H.; Guan, F.; Li, L.; Yu, H.; Wang, Z.

2018-04-01

The purpose of this paper is to provide decision support for the adjustment and optimization of crop planting structure in Jingxian County. The object-oriented information extraction method is used to extract corn and cotton from Jingxian County of Hengshui City in Hebei Province, based on multi-period GF-1 16-meter images. The best time of data extraction was screened by analyzing the spectral characteristics of corn and cotton at different growth stages based on multi-period GF-116-meter images, phenological data, and field survey data. The results showed that the total classification accuracy of corn and cotton was up to 95.7 %, the producer accuracy was 96 % and 94 % respectively, and the user precision was 95.05 % and 95.9 % respectively, which satisfied the demand of crop monitoring application. Therefore, combined with multi-period high-resolution images and object-oriented classification can be a good extraction of large-scale distribution of crop information for crop monitoring to provide convenient and effective technical means.

An Investigation of Automatic Change Detection for Topographic Map Updating

NASA Astrophysics Data System (ADS)

Duncan, P.; Smit, J.

2012-08-01

Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI), South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.

An evaluation of object-oriented image analysis techniques to identify motorized vehicle effects in semi-arid to arid ecosystems of the American West

USGS Publications Warehouse

Mladinich, C.

2010-01-01

Human disturbance is a leading ecosystem stressor. Human-induced modifications include transportation networks, areal disturbances due to resource extraction, and recreation activities. High-resolution imagery and object-oriented classification rather than pixel-based techniques have successfully identified roads, buildings, and other anthropogenic features. Three commercial, automated feature-extraction software packages (Visual Learning Systems' Feature Analyst, ENVI Feature Extraction, and Definiens Developer) were evaluated by comparing their ability to effectively detect the disturbed surface patterns from motorized vehicle traffic. Each package achieved overall accuracies in the 70% range, demonstrating the potential to map the surface patterns. The Definiens classification was more consistent and statistically valid. Copyright ?? 2010 by Bellwether Publishing, Ltd. All rights reserved.

[On risk-oriented model of sanitary epidemiologic surveillance in occupational hygiene].

PubMed

Zaitseval, N V; Mai, I V; Kostarev, V G; Bashketova, N S

2015-01-01

In 2015, Federal Service on surveillance in consumers rights protection and public well-being set a task to organize planned work of regional agencies on basis of risk-oriented model of control and supervision. Based on results of pilot project in Rospotrebnadzor Department of Perm area and St-Petersburg, the article covers methodic approaches to classification of objects liable to surveillance in occupational hygiene. The classification considers possibility of sanitary law violation, severity of this violation consequences and number of workers exposed to risk factors including hazardous work conditions. The authors specified recommendations on periodicity and forms of planned inspections considering evaluation of potential risk for human health, determined problems that require solution in implementation of risk-oriented model of surveillance.

The Development of an ICF-Oriented, Adaptive Physician Assessment Instrument of Mobility, Self-care, and Domestic Life

ERIC Educational Resources Information Center

Farin, Erik; Fleitz, Annette

2009-01-01

The objective of this study was development and psychometric testing of an adaptive, International Classification of Functioning, Disability, and Health (ICF)-oriented questionnaire to be processed by the rehabilitation physician that aids in assessing mobility, self-care, and domestic life (Moses-Physician). The intent is to develop a physician…

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data

NASA Astrophysics Data System (ADS)

Gajda, Agnieszka; Wójtowicz-Nowakowska, Anna

2013-04-01

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data Land cover maps are generally produced on the basis of high resolution imagery. Recently, LiDAR (Light Detection and Ranging) data have been brought into use in diverse applications including land cover mapping. In this study we attempted to assess the accuracy of land cover classification using both high resolution aerial imagery and LiDAR data (airborne laser scanning, ALS), testing two classification approaches: a pixel-based classification and object-oriented image analysis (OBIA). The study was conducted on three test areas (3 km2 each) in the administrative area of Kraków, Poland, along the course of the Vistula River. They represent three different dominating land cover types of the Vistula River valley. Test site 1 had a semi-natural vegetation, with riparian forests and shrubs, test site 2 represented a densely built-up area, and test site 3 was an industrial site. Point clouds from ALS and ortophotomaps were both captured in November 2007. Point cloud density was on average 16 pt/m2 and it contained additional information about intensity and encoded RGB values. Ortophotomaps had a spatial resolution of 10 cm. From point clouds two raster maps were generated: intensity (1) and (2) normalised Digital Surface Model (nDSM), both with the spatial resolution of 50 cm. To classify the aerial data, a supervised classification approach was selected. Pixel based classification was carried out in ERDAS Imagine software. Ortophotomaps and intensity and nDSM rasters were used in classification. 15 homogenous training areas representing each cover class were chosen. Classified pixels were clumped to avoid salt and pepper effect. Object oriented image object classification was carried out in eCognition software, which implements both the optical and ALS data. Elevation layers (intensity, firs/last reflection, etc.) were used at segmentation stage due to proper wages usage. Thus a more precise and unambiguous boundaries of segments (objects) were received. As a results of the classification 5 classes of land cover (buildings, water, high and low vegetation and others) were extracted. Both pixel-based image analysis and OBIA were conducted with a minimum mapping unit of 10m2. Results were validated on the basis on manual classification and random points (80 per test area), reference data set was manually interpreted using ortophotomaps and expert knowledge of the test site areas.

Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods. 10; Chapter

NASA Technical Reports Server (NTRS)

Myint, Soe W.; Mesev, Victor; Quattrochi, Dale; Wentz, Elizabeth A.

2013-01-01

Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post-classification steps. Within this chapter, each of the four approaches is described in terms of scale and accuracy classifying urban land use and urban land cover; and for its range of urban applications. We demonstrate the overview of four main classification groups in Figure 1 while Table 1 details the approaches with respect to classification requirements and procedures (e.g., reflectance conversion, steps before training sample selection, training samples, spatial approaches commonly used, classifiers, primary inputs for classification, output structures, number of output layers, and accuracy assessment). The chapter concludes with a brief summary of the methods reviewed and the challenges that remain in developing new classification methods for improving the efficiency and accuracy of mapping urban areas.

Object-based Classification for Detecting Landslides and Stochastic Procedure to landslide susceptibility maps - A Case at Baolai Village, SW Taiwan

NASA Astrophysics Data System (ADS)

Lin, Ying-Tong; Chang, Kuo-Chen; Yang, Ci-Jian

2017-04-01

As the result of global warming in the past decades, Taiwan has experienced more and more extreme typhoons with hazardous massive landslides. In this study, we use object-oriented analysis method to classify landslide area at Baolai village by using Formosat-2 satellite images. We used for multiresolution segmented to generate the blocks, and used hierarchical logic to classified 5 different kinds of features. After that, classification the landslide into different type of landslide. Beside, we use stochastic procedure to integrate landslide susceptibility maps. This study assumed that in the extreme event, 2009 Typhoon Morakot, which precipitation goes to 1991.5mm in 5 days, and the highest landslide susceptible area. The results show that study area's landslide area was greatly changes, most of landslide was erosion by gully and made dip slope slide, or erosion by the stream, especially at undercut bank. From the landslide susceptibility maps, we know that the old landslide area have high potential to occur landslides in the extreme event. This study demonstrates the changing of landslide area and the landslide susceptible area. Keywords: Formosat-2, object-oriented, segmentation, classification, landslide, Baolai Village, SW Taiwan, FS

Satellite remote sensing of isolated wetlands using object-oriented classification of LANDSAT-7 data

EPA Science Inventory

There has been an increasing interest in characterizing and mapping isolated depressional wetlands due to a 2001 U.S. Supreme Court decision that effectively removed their protected status. Our objective was to determine the utility of satellite remote sensing to accurately map ...

Mangrove classification through the use of object oriented classification and support vector machine of lidar datasets: a case study in Naawan and Manticao, Misamis Oriental, Philippines

NASA Astrophysics Data System (ADS)

Jalbuena, Rey L.; Peralta, Rudolph V.; Tamondong, Ayin M.

2016-10-01

Mangroves are trees or shrubs that grows at the surface between the land and the sea in tropical and sub-tropical latitudes. Mangroves are essential in supporting various marine life, thus, it is important to preserve and manage these areas. There are many approaches in creating Mangroves maps, one of which is through the use of Light Detection and Ranging (LiDAR). It is a remote sensing technique which uses light pulses to measure distances and to generate three-dimensional point clouds of the Earth's surface. In this study, the topographic LiDAR Data will be used to analyze the geophysical features of the terrain and create a Mangrove map. The dataset that we have were first pre-processed using the LAStools software. It is a software that is used to process LiDAR data sets and create different layers such as DSM, DTM, nDSM, Slope, LiDAR Intensity, LiDAR number of first returns, and CHM. All the aforementioned layers together was used to derive the Mangrove class. Then, an Object-based Image Analysis (OBIA) was performed using eCognition. OBIA analyzes a group of pixels with similar properties called objects, as compared to the traditional pixel-based which only examines a single pixel. Multi-threshold and multiresolution segmentation were used to delineate the different classes and split the image into objects. There are four levels of classification, first is the separation of the Land from the Water. Then the Land class was further dived into Ground and Non-ground objects. Furthermore classification of Nonvegetation, Mangroves, and Other Vegetation was done from the Non-ground objects. Lastly Separation of the mangrove class was done through the Use of field verified training points which was then run into a Support Vector Machine (SVM) classification. Different classes were separated using the different layer feature properties, such as mean, mode, standard deviation, geometrical properties, neighbor-related properties, and textural properties. Accuracy assessment was done using a different set of field validation points. This workflow was applied in the classification of Mangroves to a LiDAR dataset of Naawan and Manticao, Misamis Oriental, Philippines. The process presented in this study shows that LiDAR data and its derivatives can be used in extracting and creating Mangrove maps, which can be helpful in managing coastal environment.

Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data

USGS Publications Warehouse

Myint, S.W.; Yuan, M.; Cerveny, R.S.; Giri, C.P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and objectoriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. ?? 2008 by MDPI.

Object oriented studies into artificial space debris

NASA Technical Reports Server (NTRS)

Adamson, J. M.; Marshall, G.

1988-01-01

A prototype simulation is being developed under contract to the Royal Aerospace Establishment (RAE), Farnborough, England, to assist in the discrimination of artificial space objects/debris. The methodology undertaken has been to link Object Oriented programming, intelligent knowledge based system (IKBS) techniques and advanced computer technology with numeric analysis to provide a graphical, symbolic simulation. The objective is to provide an additional layer of understanding on top of conventional classification methods. Use is being made of object and rule based knowledge representation, multiple reasoning, truth maintenance and uncertainty. Software tools being used include Knowledge Engineering Environment (KEE) and SymTactics for knowledge representation. Hooks are being developed within the SymTactics framework to incorporate mathematical models describing orbital motion and fragmentation. Penetration and structural analysis can also be incorporated. SymTactics is an Object Oriented discrete event simulation tool built as a domain specific extension to the KEE environment. The tool provides facilities for building, debugging and monitoring dynamic (military) simulations.

Semi-automatic classification of glaciovolcanic landforms: An object-based mapping approach based on geomorphometry

NASA Astrophysics Data System (ADS)

Pedersen, G. B. M.

2016-02-01

A new object-oriented approach is developed to classify glaciovolcanic landforms (Procedure A) and their landform elements boundaries (Procedure B). It utilizes the principle that glaciovolcanic edifices are geomorphometrically distinct from lava shields and plains (Pedersen and Grosse, 2014), and the approach is tested on data from Reykjanes Peninsula, Iceland. The outlined procedures utilize slope and profile curvature attribute maps (20 m/pixel) and the classified results are evaluated quantitatively through error matrix maps (Procedure A) and visual inspection (Procedure B). In procedure A, the highest obtained accuracy is 94.1%, but even simple mapping procedures provide good results (> 90% accuracy). Successful classification of glaciovolcanic landform element boundaries (Procedure B) is also achieved and this technique has the potential to delineate the transition from intraglacial to subaerial volcanic activity in orthographic view. This object-oriented approach based on geomorphometry overcomes issues with vegetation cover, which has been typically problematic for classification schemes utilizing spectral data. Furthermore, it handles complex edifice outlines well and is easily incorporated into a GIS environment, where results can be edited or fused with other mapping results. The approach outlined here is designed to map glaciovolcanic edifices within the Icelandic neovolcanic zone but may also be applied to similar subaerial or submarine volcanic settings, where steep volcanic edifices are surrounded by flat plains.

The information extraction of Gannan citrus orchard based on the GF-1 remote sensing image

NASA Astrophysics Data System (ADS)

Wang, S.; Chen, Y. L.

2017-02-01

The production of Gannan oranges is the largest in China, which occupied an important part in the world. The extraction of citrus orchard quickly and effectively has important significance for fruit pathogen defense, fruit production and industrial planning. The traditional spectra extraction method of citrus orchard based on pixel has a lower classification accuracy, difficult to avoid the “pepper phenomenon”. In the influence of noise, the phenomenon that different spectrums of objects have the same spectrum is graveness. Taking Xunwu County citrus fruit planting area of Ganzhou as the research object, aiming at the disadvantage of the lower accuracy of the traditional method based on image element classification method, a decision tree classification method based on object-oriented rule set is proposed. Firstly, multi-scale segmentation is performed on the GF-1 remote sensing image data of the study area. Subsequently the sample objects are selected for statistical analysis of spectral features and geometric features. Finally, combined with the concept of decision tree classification, a variety of empirical values of single band threshold, NDVI, band combination and object geometry characteristics are used hierarchically to execute the information extraction of the research area, and multi-scale segmentation and hierarchical decision tree classification is implemented. The classification results are verified with the confusion matrix, and the overall Kappa index is 87.91%.

Object-oriented feature extraction approach for mapping supraglacial debris in Schirmacher Oasis using very high-resolution satellite data

NASA Astrophysics Data System (ADS)

Jawak, Shridhar D.; Jadhav, Ajay; Luis, Alvarinho J.

2016-05-01

Supraglacial debris was mapped in the Schirmacher Oasis, east Antarctica, by using WorldView-2 (WV-2) high resolution optical remote sensing data consisting of 8-band calibrated Gram Schmidt (GS)-sharpened and atmospherically corrected WV-2 imagery. This study is a preliminary attempt to develop an object-oriented rule set to extract supraglacial debris for Antarctic region using 8-spectral band imagery. Supraglacial debris was manually digitized from the satellite imagery to generate the ground reference data. Several trials were performed using few existing traditional pixel-based classification techniques and color-texture based object-oriented classification methods to extract supraglacial debris over a small domain of the study area. Multi-level segmentation and attributes such as scale, shape, size, compactness along with spectral information from the data were used for developing the rule set. The quantitative analysis of error was carried out against the manually digitized reference data to test the practicability of our approach over the traditional pixel-based methods. Our results indicate that OBIA-based approach (overall accuracy: 93%) for extracting supraglacial debris performed better than all the traditional pixel-based methods (overall accuracy: 80-85%). The present attempt provides a comprehensive improved method for semiautomatic feature extraction in supraglacial environment and a new direction in the cryospheric research.

Real-time classification of vehicles by type within infrared imagery

NASA Astrophysics Data System (ADS)

Kundegorski, Mikolaj E.; Akçay, Samet; Payen de La Garanderie, Grégoire; Breckon, Toby P.

2016-10-01

Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios.

Comparison of classification algorithms for various methods of preprocessing radar images of the MSTAR base

NASA Astrophysics Data System (ADS)

Borodinov, A. A.; Myasnikov, V. V.

2018-04-01

The present work is devoted to comparing the accuracy of the known qualification algorithms in the task of recognizing local objects on radar images for various image preprocessing methods. Preprocessing involves speckle noise filtering and normalization of the object orientation in the image by the method of image moments and by a method based on the Hough transform. In comparison, the following classification algorithms are used: Decision tree; Support vector machine, AdaBoost, Random forest. The principal component analysis is used to reduce the dimension. The research is carried out on the objects from the base of radar images MSTAR. The paper presents the results of the conducted studies.

Evaluation of image deblurring methods via a classification metric

NASA Astrophysics Data System (ADS)

Perrone, Daniele; Humphreys, David; Lamb, Robert A.; Favaro, Paolo

2012-09-01

The performance of single image deblurring algorithms is typically evaluated via a certain discrepancy measure between the reconstructed image and the ideal sharp image. The choice of metric, however, has been a source of debate and has also led to alternative metrics based on human visual perception. While fixed metrics may fail to capture some small but visible artifacts, perception-based metrics may favor reconstructions with artifacts that are visually pleasant. To overcome these limitations, we propose to assess the quality of reconstructed images via a task-driven metric. In this paper we consider object classification as the task and therefore use the rate of classification as the metric to measure deblurring performance. In our evaluation we use data with different types of blur in two cases: Optical Character Recognition (OCR), where the goal is to recognise characters in a black and white image, and object classification with no restrictions on pose, illumination and orientation. Finally, we show how off-the-shelf classification algorithms benefit from working with deblurred images.

Three-dimensional object recognition using similar triangles and decision trees

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly

1993-01-01

A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.

D Land Cover Classification Based on Multispectral LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

2016-06-01

Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrared (NIR) and at 1550nm intermediate infrared (IR). It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA) approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.

Examining change detection approaches for tropical mangrove monitoring

USGS Publications Warehouse

Myint, Soe W.; Franklin, Janet; Buenemann, Michaela; Kim, Won; Giri, Chandra

2014-01-01

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

Mapping impervious surfaces using object-oriented classification in a semiarid urban region

USDA-ARS?s Scientific Manuscript database

Mapping the expansion of impervious surfaces in urbanizing areas is important for monitoring and understanding the hydrologic impacts of land development. The most common approach using spectral vegetation indices, however, is difficult in arid and semiarid environments where vegetation is sparse an...

Mapping ecological states in a complex environment

NASA Astrophysics Data System (ADS)

Steele, C. M.; Bestelmeyer, B.; Burkett, L. M.; Ayers, E.; Romig, K.; Slaughter, A.

2013-12-01

The vegetation of northern Chihuahuan Desert rangelands is sparse, heterogeneous and for most of the year, consists of a large proportion of non-photosynthetic material. The soils in this area are spectrally bright and variable in their reflectance properties. Both factors provide challenges to the application of remote sensing for estimating canopy variables (e.g., leaf area index, biomass, percentage canopy cover, primary production). Additionally, with reference to current paradigms of rangeland health assessment, remotely-sensed estimates of canopy variables have limited practical use to the rangeland manager if they are not placed in the context of ecological site and ecological state. To address these challenges, we created a multifactor classification system based on the USDA-NRCS ecological site schema and associated state-and-transition models to map ecological states on desert rangelands in southern New Mexico. Applying this system using per-pixel image processing techniques and multispectral, remotely sensed imagery raised other challenges. Per-pixel image classification relies upon the spectral information in each pixel alone, there is no reference to the spatial context of the pixel and its relationship with its neighbors. Ecological state classes may have direct relevance to managers but the non-unique spectral properties of different ecological state classes in our study area means that per-pixel classification of multispectral data performs poorly in discriminating between different ecological states. We found that image interpreters who are familiar with the landscape and its associated ecological site descriptions perform better than per-pixel classification techniques in assigning ecological states. However, two important issues affect manual classification methods: subjectivity of interpretation and reproducibility of results. An alternative to per-pixel classification and manual interpretation is object-based image analysis. Object-based image analysis provides a platform for classification that more closely resembles human recognition of objects within a remotely sensed image. The analysis presented here compares multiple thematic maps created for test locations on the USDA-ARS Jornada Experimental Range ranch. Three study sites in different pastures, each 300 ha in size, were selected for comparison on the basis of their ecological site type (';Clayey', ';Sandy' and a combination of both) and the degree of complexity of vegetation cover. Thematic maps were produced for each study site using (i) manual interpretation of digital aerial photography (by five independent interpreters); (ii) object-oriented, decision-tree classification of fine and moderate spatial resolution imagery (Quickbird; Landsat Thematic Mapper) and (iii) ground survey. To identify areas of uncertainty, we compared agreement in location, areal extent and class assignation between 5 independently produced, manually-digitized ecological state maps and with the map created from ground survey. Location, areal extent and class assignation of the map produced by object-oriented classification was also assessed with reference to the ground survey map.

Object-oriented Persistent Homology

PubMed Central

Wang, Bao; Wei, Guo-Wei

2015-01-01

Persistent homology provides a new approach for the topological simplification of big data via measuring the life time of intrinsic topological features in a filtration process and has found its success in scientific and engineering applications. However, such a success is essentially limited to qualitative data classification and analysis. Indeed, persistent homology has rarely been employed for quantitative modeling and prediction. Additionally, the present persistent homology is a passive tool, rather than a proactive technique, for classification and analysis. In this work, we outline a general protocol to construct object-oriented persistent homology methods. By means of differential geometry theory of surfaces, we construct an objective functional, namely, a surface free energy defined on the data of interest. The minimization of the objective functional leads to a Laplace-Beltrami operator which generates a multiscale representation of the initial data and offers an objective oriented filtration process. The resulting differential geometry based object-oriented persistent homology is able to preserve desirable geometric features in the evolutionary filtration and enhances the corresponding topological persistence. The cubical complex based homology algorithm is employed in the present work to be compatible with the Cartesian representation of the Laplace-Beltrami flow. The proposed Laplace-Beltrami flow based persistent homology method is extensively validated. The consistence between Laplace-Beltrami flow based filtration and Euclidean distance based filtration is confirmed on the Vietoris-Rips complex for a large amount of numerical tests. The convergence and reliability of the present Laplace-Beltrami flow based cubical complex filtration approach are analyzed over various spatial and temporal mesh sizes. The Laplace-Beltrami flow based persistent homology approach is utilized to study the intrinsic topology of proteins and fullerene molecules. Based on a quantitative model which correlates the topological persistence of fullerene central cavity with the total curvature energy of the fullerene structure, the proposed method is used for the prediction of fullerene isomer stability. The efficiency and robustness of the present method are verified by more than 500 fullerene molecules. It is shown that the proposed persistent homology based quantitative model offers good predictions of total curvature energies for ten types of fullerene isomers. The present work offers the first example to design object-oriented persistent homology to enhance or preserve desirable features in the original data during the filtration process and then automatically detect or extract the corresponding topological traits from the data. PMID:26705370

Orientation selectivity based structure for texture classification

NASA Astrophysics Data System (ADS)

Wu, Jinjian; Lin, Weisi; Shi, Guangming; Zhang, Yazhong; Lu, Liu

2014-10-01

Local structure, e.g., local binary pattern (LBP), is widely used in texture classification. However, LBP is too sensitive to disturbance. In this paper, we introduce a novel structure for texture classification. Researches on cognitive neuroscience indicate that the primary visual cortex presents remarkable orientation selectivity for visual information extraction. Inspired by this, we investigate the orientation similarities among neighbor pixels, and propose an orientation selectivity based pattern for local structure description. Experimental results on texture classification demonstrate that the proposed structure descriptor is quite robust to disturbance.

Wing Classification in the Virtual Research Center

NASA Technical Reports Server (NTRS)

Campbell, William H.

1999-01-01

The Virtual Research Center (VRC) is a Web site that hosts a database of documents organized to allow teams of scientists and engineers to store and maintain documents. A number of other workgroup-related capabilities are provided. My tasks as a NASA/ASEE Summer Faculty Fellow included developing a scheme for classifying the workgroups using the VRC using the various Divisions within NASA Enterprises. To this end I developed a plan to use several CGI Perl scripts to gather classification information from the leaders of the workgroups, and to display all the workgroups within a specified classification. I designed, implemented, and partially tested scripts which can be used to do the classification. I was also asked to consider directions for future development of the VRC. I think that the VRC can use XML to advantage. XML is a markup language with designer tags that can be used to build meaning into documents. An investigation as to how CORBA, an object-oriented object request broker included with JDK 1.2, might be used also seems justified.

Extracting built-up areas from TerraSAR-X data using object-oriented classification method

NASA Astrophysics Data System (ADS)

Wang, SuYun; Sun, Z. C.

2017-02-01

Based on single-polarized TerraSAR-X, the approach generates homogeneous segments on an arbitrary number of scale levels by applying a region-growing algorithm which takes the intensity of backscatter and shape-related properties into account. The object-oriented procedure consists of three main steps: firstly, the analysis of the local speckle behavior in the SAR intensity data, leading to the generation of a texture image; secondly, a segmentation based on the intensity image; thirdly, the classification of each segment using the derived texture file and intensity information in order to identify and extract build-up areas. In our research, the distribution of BAs in Dongying City is derived from single-polarized TSX SM image (acquired on 17th June 2013) with average ground resolution of 3m using our proposed approach. By cross-validating the random selected validation points with geo-referenced field sites, Quick Bird high-resolution imagery, confusion matrices with statistical indicators are calculated and used for assessing the classification results. The results demonstrate that an overall accuracy 92.89 and a kappa coefficient of 0.85 could be achieved. We have shown that connect texture information with the analysis of the local speckle divergence, combining texture and intensity of construction extraction is feasible, efficient and rapid.

Segmentation, classification, and pose estimation of military vehicles in low resolution laser radar images

NASA Astrophysics Data System (ADS)

Neulist, Joerg; Armbruster, Walter

2005-05-01

Model-based object recognition in range imagery typically involves matching the image data to the expected model data for each feasible model and pose hypothesis. Since the matching procedure is computationally expensive, the key to efficient object recognition is the reduction of the set of feasible hypotheses. This is particularly important for military vehicles, which may consist of several large moving parts such as the hull, turret, and gun of a tank, and hence require an eight or higher dimensional pose space to be searched. The presented paper outlines techniques for reducing the set of feasible hypotheses based on an estimation of target dimensions and orientation. Furthermore, the presence of a turret and a main gun and their orientations are determined. The vehicle parts dimensions as well as their error estimates restrict the number of model hypotheses whereas the position and orientation estimates and their error bounds reduce the number of pose hypotheses needing to be verified. The techniques are applied to several hundred laser radar images of eight different military vehicles with various part classifications and orientations. On-target resolution in azimuth, elevation and range is about 30 cm. The range images contain up to 20% dropouts due to atmospheric absorption. Additionally some target retro-reflectors produce outliers due to signal crosstalk. The presented algorithms are extremely robust with respect to these and other error sources. The hypothesis space for hull orientation is reduced to about 5 degrees as is the error for turret rotation and gun elevation, provided the main gun is visible.

Urban Change Detection of Pingtan City based on Bi-temporal Remote Sensing Images

NASA Astrophysics Data System (ADS)

Degang, JIANG; Jinyan, XU; Yikang, GAO

2017-02-01

In this paper, a pair of SPOT 5-6 images with the resolution of 0.5m is selected. An object-oriented classification method is used to the two images and five classes of ground features were identified as man-made objects, farmland, forest, waterbody and unutilized land. An auxiliary ASTER GDEM was used to improve the classification accuracy. And the change detection based on the classification results was performed. Accuracy assessment was carried out finally. Consequently, satisfactory results were obtained. The results show that great changes of the Pingtan city have been detected as the expansion of the city area and the intensity increase of man-made buildings, roads and other infrastructures with the establishment of Pingtan comprehensive experimental zone. Wide range of open sea area along the island coast zones has been reclaimed for port and CBDs construction.

Classification of product inspection items using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, H.-W.

1998-03-01

Automated processing and classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. This approach involves two main steps: preprocessing and classification. Preprocessing locates individual items and segments ones that touch using a modified watershed algorithm. The second stage involves extraction of features that allow discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper. We use a new nonlinear feature extraction scheme called the maximum representation and discriminating feature (MRDF) extraction method to compute nonlinear features that are used as inputs to a classifier. The MRDF is shown to provide better classification and a better ROC (receiver operating characteristic) curve than other methods.

Temperament Profiles from Infancy to Middle Childhood: Development and Associations with Behavior Problems

ERIC Educational Resources Information Center

Janson, Harald; Mathiesen, Kristin S.

2008-01-01

The authors applied I-States as Objects Analysis (ISOA), a recently proposed person-oriented analytic approach, to the study of temperament development in 921 Norwegian children from a population-based sample. A 5-profile classification based on cluster analysis of standardized mother reports of activity, sociability, emotionality, and shyness at…

exprso: an R-package for the rapid implementation of machine learning algorithms.

PubMed

Quinn, Thomas; Tylee, Daniel; Glatt, Stephen

2016-01-01

Machine learning plays a major role in many scientific investigations. However, non-expert programmers may struggle to implement the elaborate pipelines necessary to build highly accurate and generalizable models. We introduce exprso , a new R package that is an intuitive machine learning suite designed specifically for non-expert programmers. Built initially for the classification of high-dimensional data, exprso uses an object-oriented framework to encapsulate a number of common analytical methods into a series of interchangeable modules. This includes modules for feature selection, classification, high-throughput parameter grid-searching, elaborate cross-validation schemes (e.g., Monte Carlo and nested cross-validation), ensemble classification, and prediction. In addition, exprso also supports multi-class classification (through the 1-vs-all generalization of binary classifiers) and the prediction of continuous outcomes.

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

NASA Astrophysics Data System (ADS)

Cheng, Gong; Han, Junwei; Zhou, Peicheng; Guo, Lei

2014-12-01

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.

Grasp movement decoding from premotor and parietal cortex.

PubMed

Townsend, Benjamin R; Subasi, Erk; Scherberger, Hansjörg

2011-10-05

Despite recent advances in harnessing cortical motor-related activity to control computer cursors and robotic devices, the ability to decode and execute different grasping patterns remains a major obstacle. Here we demonstrate a simple Bayesian decoder for real-time classification of grip type and wrist orientation in macaque monkeys that uses higher-order planning signals from anterior intraparietal cortex (AIP) and ventral premotor cortex (area F5). Real-time decoding was based on multiunit signals, which had similar tuning properties to cells in previous single-unit recording studies. Maximum decoding accuracy for two grasp types (power and precision grip) and five wrist orientations was 63% (chance level, 10%). Analysis of decoder performance showed that grip type decoding was highly accurate (90.6%), with most errors occurring during orientation classification. In a subsequent off-line analysis, we found small but significant performance improvements (mean, 6.25 percentage points) when using an optimized spike-sorting method (superparamagnetic clustering). Furthermore, we observed significant differences in the contributions of F5 and AIP for grasp decoding, with F5 being better suited for classification of the grip type and AIP contributing more toward decoding of object orientation. However, optimum decoding performance was maximal when using neural activity simultaneously from both areas. Overall, these results highlight quantitative differences in the functional representation of grasp movements in AIP and F5 and represent a first step toward using these signals for developing functional neural interfaces for hand grasping.

New feature extraction method for classification of agricultural products from x-ray images

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, Ha-Woon; Keagy, Pamela M.; Schatzki, Thomas F.

1999-01-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work the MRDF is applied to standard features. The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC data.

Relationship between AOD and synoptic circulation over the Eastern Mediterranean: A comparison between subjective and objective classifications

NASA Astrophysics Data System (ADS)

Bodenheimer, Shalev; Nirel, Ronit; Lensky, Itamar M.; Dayan, Uri

2018-03-01

The Eastern Mediterranean (EM) Basin is strongly affected by dust originating from two of the largest world sources: The Sahara Desert and the Arabian Peninsula. Climatologically, the distribution pattern of aerosol optical depth (AOD), as proxy to particulate matter (PM), is known to be correlated with synoptic circulation. The climatological relationship between circulation type classifications (CTCs) and AOD levels over the EM Basin ("synoptic skill") was examined for the years 2000-2014. We compared the association between subjective (expert-based) and objective (fully automated) classifications and AOD using autoregressive models. After seasonal adjustment, the mean values of R2 for the different methods were similar. However, the distinct spatial pattern of the R2 values suggests that subjective classifications perform better in their area of expertise, specifically in the southeast region of the study area, while, objective CTCs had better synoptic skill over the northern part of the EM. This higher synoptic skill of subjective CTCs stem from their ability to identify distinct circulation types (e.g. Sharav lows and winter lows) that are infrequent but are highly correlated with AOD. Notably, a simple CTC based on seasonality rather than meteorological parameters predicted well AOD levels, especially over the south-eastern part of the domain. Synoptic classifications that are area-oriented are likely better predictors of AOD and possibly other environmental variables.

A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images

NASA Astrophysics Data System (ADS)

Keyport, Ren N.; Oommen, Thomas; Martha, Tapas R.; Sajinkumar, K. S.; Gierke, John S.

2018-02-01

A comparative analysis of landslides detected by pixel-based and object-oriented analysis (OOA) methods was performed using very high-resolution (VHR) remotely sensed aerial images for the San Juan La Laguna, Guatemala, which witnessed widespread devastation during the 2005 Hurricane Stan. A 3-band orthophoto of 0.5 m spatial resolution together with a 115 field-based landslide inventory were used for the analysis. A binary reference was assigned with a zero value for landslide and unity for non-landslide pixels. The pixel-based analysis was performed using unsupervised classification, which resulted in 11 different trial classes. Detection of landslides using OOA includes 2-step K-means clustering to eliminate regions based on brightness; elimination of false positives using object properties such as rectangular fit, compactness, length/width ratio, mean difference of objects, and slope angle. Both overall accuracy and F-score for OOA methods outperformed pixel-based unsupervised classification methods in both landslide and non-landslide classes. The overall accuracy for OOA and pixel-based unsupervised classification was 96.5% and 94.3%, respectively, whereas the best F-score for landslide identification for OOA and pixel-based unsupervised methods: were 84.3% and 77.9%, respectively.Results indicate that the OOA is able to identify the majority of landslides with a few false positive when compared to pixel-based unsupervised classification.

Mixing geometric and radiometric features for change classification

NASA Astrophysics Data System (ADS)

Fournier, Alexandre; Descombes, Xavier; Zerubia, Josiane

2008-02-01

Most basic change detection algorithms use a pixel-based approach. Whereas such approach is quite well defined for monitoring important area changes (such as urban growth monitoring) in low resolution images, an object based approach seems more relevant when the change detection is specifically aimed toward targets (such as small buildings and vehicles). In this paper, we present an approach that mixes radiometric and geometric features to qualify the changed zones. The goal is to establish bounds (appearance, disappearance, substitution ...) between the detected changes and the underlying objects. We proceed by first clustering the change map (containing each pixel bitemporal radiosity) in different classes using the entropy-kmeans algorithm. Assuming that most man-made objects have a polygonal shape, a polygonal approximation algorithm is then used in order to characterize the resulting zone shapes. Hence allowing us to refine the primary rough classification, by integrating the polygon orientations in the state space. Tests are currently conducted on Quickbird data.

Abstracting of suspected illegal land use in urban areas using case-based classification of remote sensing images

NASA Astrophysics Data System (ADS)

Chen, Fulong; Wang, Chao; Yang, Chengyun; Zhang, Hong; Wu, Fan; Lin, Wenjuan; Zhang, Bo

2008-11-01

This paper proposed a method that uses a case-based classification of remote sensing images and applied this method to abstract the information of suspected illegal land use in urban areas. Because of the discrete cases for imagery classification, the proposed method dealt with the oscillation of spectrum or backscatter within the same land use category, and it not only overcame the deficiency of maximum likelihood classification (the prior probability of land use could not be obtained) but also inherited the advantages of the knowledge-based classification system, such as artificial intelligence and automatic characteristics. Consequently, the proposed method could do the classifying better. Then the researchers used the object-oriented technique for shadow removal in highly dense city zones. With multi-temporal SPOT 5 images whose resolution was 2.5×2.5 meters, the researchers found that the method can abstract suspected illegal land use information in urban areas using post-classification comparison technique.

New data clustering for RBF classifier of agriculture products from x-ray images

NASA Astrophysics Data System (ADS)

Casasent, David P.; Chen, Xuewen

1999-08-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a subsystem for automated non-invasive detection of defective product items on a conveyor belt. We discuss the use of clustering and how it is vital to achieve useful classification. New clustering methods using class identify and new cluster classes are advanced and shown to be of use for this application. Radial basis function neural net classifiers are emphasized. We expect our results to be of use for other classifiers and applications.

Object-oriented classification of forest structure from light detection and ranging data for stand mapping

Treesearch

Alicia A. Sullivan; Robert J. McGaughey; Hans-Erik Andersen; Peter Schiess

2009-01-01

Stand delineation is an important step in the process of establishing a forest inventory and provides the spatial framework for many forest management decisions. Many methods for extracting forest structure characteristics for stand delineation and other purposes have been researched in the past, primarily focusing on high-resolution imagery and satellite data. High-...

Object-oriented classification of drumlins from digital elevation models

NASA Astrophysics Data System (ADS)

Saha, Kakoli

Drumlins are common elements of glaciated landscapes which are easily identified by their distinct morphometric characteristics including shape, length/width ratio, elongation ratio, and uniform direction. To date, most researchers have mapped drumlins by tracing contours on maps, or through on-screen digitization directly on top of hillshaded digital elevation models (DEMs). This paper seeks to utilize the unique morphometric characteristics of drumlins and investigates automated extraction of the landforms as objects from DEMs by Definiens Developer software (V.7), using the 30 m United States Geological Survey National Elevation Dataset DEM as input. The Chautauqua drumlin field in Pennsylvania and upstate New York, USA was chosen as a study area. As the study area is huge (approximately covers 2500 sq.km. of area), small test areas were selected for initial testing of the method. Individual polygons representing the drumlins were extracted from the elevation data set by automated recognition, using Definiens' Multiresolution Segmentation tool, followed by rule-based classification. Subsequently parameters such as length, width and length-width ratio, perimeter and area were measured automatically. To test the accuracy of the method, a second base map was produced by manual on-screen digitization of drumlins from topographic maps and the same morphometric parameters were extracted from the mapped landforms using Definiens Developer. Statistical comparison showed a high agreement between the two methods confirming that object-oriented classification for extraction of drumlins can be used for mapping these landforms. The proposed method represents an attempt to solve the problem by providing a generalized rule-set for mass extraction of drumlins. To check that the automated extraction process was next applied to a larger area. Results showed that the proposed method is as successful for the bigger area as it was for the smaller test areas.

Vegetation Monitoring of Mashhad Using AN Object-Oriented POST Classification Comparison Method

NASA Astrophysics Data System (ADS)

Khalili Moghadam, N.; Delavar, M. R.; Forati, A.

2017-09-01

By and large, todays mega cities are confronting considerable urban development in which many new buildings are being constructed in fringe areas of these cities. This remarkable urban development will probably end in vegetation reduction even though each mega city requires adequate areas of vegetation, which is considered to be crucial and helpful for these cities from a wide variety of perspectives such as air pollution reduction, soil erosion prevention, and eco system as well as environmental protection. One of the optimum methods for monitoring this vital component of each city is multi-temporal satellite images acquisition and using change detection techniques. In this research, the vegetation and urban changes of Mashhad, Iran, were monitored using an object-oriented (marker-based watershed algorithm) post classification comparison (PCC) method. A Bi-temporal multi-spectral Landsat satellite image was used from the study area to detect the changes of urban and vegetation areas and to find a relation between these changes. The results of this research demonstrate that during 1987-2017, Mashhad urban area has increased about 22525 hectares and the vegetation area has decreased approximately 4903 hectares. These statistics substantiate the close relationship between urban development and vegetation reduction. Moreover, the overall accuracies of 85.5% and 91.2% were achieved for the first and the second image classification, respectively. In addition, the overall accuracy and kappa coefficient of change detection were assessed 84.1% and 70.3%, respectively.

Enriching User-Oriented Class Associations for Library Classification Schemes.

ERIC Educational Resources Information Center

Pu, Hsiao-Tieh; Yang, Chyan

2003-01-01

Explores the possibility of adding user-oriented class associations to hierarchical library classification schemes. Analyses a log of book circulation records from a university library in Taiwan and shows that classification schemes can be made more adaptable by analyzing circulation patterns of similar users. (Author/LRW)

A semi-automated image analysis procedure for in situ plankton imaging systems.

PubMed

Bi, Hongsheng; Guo, Zhenhua; Benfield, Mark C; Fan, Chunlei; Ford, Michael; Shahrestani, Suzan; Sieracki, Jeffery M

2015-01-01

Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups.

A Semi-Automated Image Analysis Procedure for In Situ Plankton Imaging Systems

PubMed Central

Bi, Hongsheng; Guo, Zhenhua; Benfield, Mark C.; Fan, Chunlei; Ford, Michael; Shahrestani, Suzan; Sieracki, Jeffery M.

2015-01-01

Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups. PMID:26010260

An Ensemble Method with Integration of Feature Selection and Classifier Selection to Detect the Landslides

NASA Astrophysics Data System (ADS)

Zhongqin, G.; Chen, Y.

2017-12-01

Abstract Quickly identify the spatial distribution of landslides automatically is essential for the prevention, mitigation and assessment of the landslide hazard. It's still a challenging job owing to the complicated characteristics and vague boundary of the landslide areas on the image. The high resolution remote sensing image has multi-scales, complex spatial distribution and abundant features, the object-oriented image classification methods can make full use of the above information and thus effectively detect the landslides after the hazard happened. In this research we present a new semi-supervised workflow, taking advantages of recent object-oriented image analysis and machine learning algorithms to quick locate the different origins of landslides of some areas on the southwest part of China. Besides a sequence of image segmentation, feature selection, object classification and error test, this workflow ensemble the feature selection and classifier selection. The feature this study utilized were normalized difference vegetation index (NDVI) change, textural feature derived from the gray level co-occurrence matrices (GLCM), spectral feature and etc. The improvement of this study shows this algorithm significantly removes some redundant feature and the classifiers get fully used. All these improvements lead to a higher accuracy on the determination of the shape of landslides on the high resolution remote sensing image, in particular the flexibility aimed at different kinds of landslides.

Nonlinear features for product inspection

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1999-03-01

Classification of real-time X-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work, the MRDF is applied to standard features (rather than iconic data). The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC (receiver operating characteristic) data.

Assessment of sexual orientation using the hemodynamic brain response to visual sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Mehdorn, Hubertus; Bosinski, Hartmut; Siebner, Hartwig

2009-06-01

The assessment of sexual orientation is of importance to the diagnosis and treatment of sex offenders and paraphilic disorders. Phallometry is considered gold standard in objectifying sexual orientation, yet this measurement has been criticized because of its intrusiveness and limited reliability. To evaluate whether the spatial response pattern to sexual stimuli as revealed by a change in blood oxygen level-dependent (BOLD) signal can be used for individual classification of sexual orientation. We used a preexisting functional MRI (fMRI) data set that had been acquired in a nonclinical sample of 12 heterosexual men and 14 homosexual men. During fMRI, participants were briefly exposed to pictures of same-sex and opposite-sex genitals. Data analysis involved four steps: (i) differences in the BOLD response to female and male sexual stimuli were calculated for each subject; (ii) these contrast images were entered into a group analysis to calculate whole-brain difference maps between homosexual and heterosexual participants; (iii) a single expression value was computed for each subject expressing its correspondence to the group result; and (iv) based on these expression values, Fisher's linear discriminant analysis and the kappa-nearest neighbor classification method were used to predict the sexual orientation of each subject. Sensitivity and specificity of the two classification methods in predicting individual sexual orientation. Both classification methods performed well in predicting individual sexual orientation with a mean accuracy of >85% (Fisher's linear discriminant analysis: 92% sensitivity, 85% specificity; kappa-nearest neighbor classification: 88% sensitivity, 92% specificity). Despite the small sample size, the functional response patterns of the brain to sexual stimuli contained sufficient information to predict individual sexual orientation with high accuracy. These results suggest that fMRI-based classification methods hold promise for the diagnosis of paraphilic disorders (e.g., pedophilia).

Optimization of Support Vector Machine (SVM) for Object Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

Investigation of the relative orientation of the system of optical sensors to monitor the technosphere objects

NASA Astrophysics Data System (ADS)

Petrochenko, Andrey; Konyakhin, Igor

2017-06-01

In connection with the development of robotics have become increasingly popular variety of three-dimensional reconstruction of the system mapping and image-set received from the optical sensors. The main objective of technical and robot vision is the detection, tracking and classification of objects of the space in which these systems and robots operate [15,16,18]. Two-dimensional images sometimes don't contain sufficient information to address those or other problems: the construction of the map of the surrounding area for a route; object identification, tracking their relative position and movement; selection of objects and their attributes to complement the knowledge base. Three-dimensional reconstruction of the surrounding space allows you to obtain information on the relative positions of objects, their shape, surface texture. Systems, providing training on the basis of three-dimensional reconstruction of the results of the comparison can produce two-dimensional images of three-dimensional model that allows for the recognition of volume objects on flat images. The problem of the relative orientation of industrial robots with the ability to build threedimensional scenes of controlled surfaces is becoming actual nowadays.

Object linking in repositories

NASA Technical Reports Server (NTRS)

Eichmann, David (Editor); Beck, Jon; Atkins, John; Bailey, Bill

1992-01-01

This topic is covered in three sections. The first section explores some of the architectural ramifications of extending the Eichmann/Atkins lattice-based classification scheme to encompass the assets of the full life cycle of software development. A model is considered that provides explicit links between objects in addition to the edges connecting classification vertices in the standard lattice. The second section gives a description of the efforts to implement the repository architecture using a commercially available object-oriented database management system. Some of the features of this implementation are described, and some of the next steps to be taken to produce a working prototype of the repository are pointed out. In the final section, it is argued that design and instantiation of reusable components have competing criteria (design-for-reuse strives for generality, design-with-reuse strives for specificity) and that providing mechanisms for each can be complementary rather than antagonistic. In particular, it is demonstrated how program slicing techniques can be applied to customization of reusable components.

Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm

PubMed Central

Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong

2016-01-01

In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis. PMID:27959895

Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm.

PubMed

Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong

2016-01-01

In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.

Comprehensive 4-stage categorization of bicuspid aortic valve leaflet morphology by cardiac MRI in 386 patients.

PubMed

Murphy, I G; Collins, J; Powell, A; Markl, M; McCarthy, P; Malaisrie, S C; Carr, J C; Barker, A J

2017-08-01

Bicuspid aortic valve (BAV) disease is heterogeneous and related to valve dysfunction and aortopathy. Appropriate follow up and surveillance of patients with BAV may depend on correct phenotypic categorization. There are multiple classification schemes, however a need exists to comprehensively capture commissure fusion, leaflet asymmetry, and valve orifice orientation. Our aim was to develop a BAV classification scheme for use at MRI to ascertain the frequency of different phenotypes and the consistency of BAV classification. The BAV classification scheme builds on the Sievers surgical BAV classification, adding valve orifice orientation, partial leaflet fusion and leaflet asymmetry. A single observer successfully applied this classification to 386 of 398 Cardiac MRI studies. Repeatability of categorization was ascertained with intraobserver and interobserver kappa scores. Sensitivity and specificity of MRI findings was determined from operative reports, where available. Fusion of the right and left leaflets accounted for over half of all cases. Partial leaflet fusion was seen in 46% of patients. Good interobserver agreement was seen for orientation of the valve opening (κ = 0.90), type (κ = 0.72) and presence of partial fusion (κ = 0.83, p < 0.0001). Retrospective review of operative notes showed sensitivity and specificity for orientation (90, 93%) and for Sievers type (73, 87%). The proposed BAV classification schema was assessed by MRI for its reliability to classify valve morphology in addition to illustrating the wide heterogeneity of leaflet size, orifice orientation, and commissural fusion. The classification may be helpful in further understanding the relationship between valve morphology, flow derangement and aortopathy.

Detecting Slums from Quick Bird Data in Pune Using AN Object Oriented Approach

NASA Astrophysics Data System (ADS)

Shekhar, S.

2012-07-01

We have been witnessing a gradual and steady transformation from a pre dominantly rural society to an urban society in India and by 2030, it will have more people living in urban than rural areas. Slums formed an integral part of Indian urbanisation as most of the Indian cities lack in basic needs of an acceptable life. Many efforts are being taken to improve their conditions. To carry out slum renewal programs and monitor its implementation, slum settlements should be recorded to obtain an adequate spatial data base. This can be only achieved through the analysis of remote sensing data with very high spatial resolution. Regarding the occurrences of settlement areas in the remote sensing data pixel-based approach on a high resolution image is unable to represent the heterogeneity of complex urban environments. Hence there is a need for sophisticated method and data for slum analysis. An attempt has been made to detect and discriminate the slums of Pune city by describing typical characteristics of these settlements, by using eCognition software from quick bird data on the basis of object oriented approach. Based on multi resolution segmentation, initial objects were created and further depend on texture, geometry and contextual characteristics of the image objects, they were classified into slums and non-slums. The developed rule base allowed the description of knowledge about phenomena clearly and easily using fuzzy membership functions and the described knowledge stored in the classification rule base led to the best classification with more than 80% accuracy.

Hierarchical Higher Order Crf for the Classification of Airborne LIDAR Point Clouds in Urban Areas

NASA Astrophysics Data System (ADS)

Niemeyer, J.; Rottensteiner, F.; Soergel, U.; Heipke, C.

2016-06-01

We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the distance and the orientation of a segment with respect to the closest road. It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification.

Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle.

PubMed

Diaz-Varela, R A; Zarco-Tejada, P J; Angileri, V; Loudjani, P

2014-02-15

Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11 cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5 m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Single-Frame Terrain Mapping Software for Robotic Vehicles

NASA Technical Reports Server (NTRS)

Rankin, Arturo L.

2011-01-01

This software is a component in an unmanned ground vehicle (UGV) perception system that builds compact, single-frame terrain maps for distribution to other systems, such as a world model or an operator control unit, over a local area network (LAN). Each cell in the map encodes an elevation value, terrain classification, object classification, terrain traversability, terrain roughness, and a confidence value into four bytes of memory. The input to this software component is a range image (from a lidar or stereo vision system), and optionally a terrain classification image and an object classification image, both registered to the range image. The single-frame terrain map generates estimates of the support surface elevation, ground cover elevation, and minimum canopy elevation; generates terrain traversability cost; detects low overhangs and high-density obstacles; and can perform geometry-based terrain classification (ground, ground cover, unknown). A new origin is automatically selected for each single-frame terrain map in global coordinates such that it coincides with the corner of a world map cell. That way, single-frame terrain maps correctly line up with the world map, facilitating the merging of map data into the world map. Instead of using 32 bits to store the floating-point elevation for a map cell, the vehicle elevation is assigned to the map origin elevation and reports the change in elevation (from the origin elevation) in terms of the number of discrete steps. The single-frame terrain map elevation resolution is 2 cm. At that resolution, terrain elevation from 20.5 to 20.5 m (with respect to the vehicle's elevation) is encoded into 11 bits. For each four-byte map cell, bits are assigned to encode elevation, terrain roughness, terrain classification, object classification, terrain traversability cost, and a confidence value. The vehicle s current position and orientation, the map origin, and the map cell resolution are all included in a header for each map. The map is compressed into a vector prior to delivery to another system.

Global Warming and Geographically Scalar Climatic Objects Exist: An Ontologically Realist and Object-Oriented Analysis of the Daymet TMAX Climate Summaries for North America

NASA Astrophysics Data System (ADS)

Jackson, C. P.

2017-12-01

The scientific materialist worldview, what Peter Unger refers to as the Scientiphical worldview, or Scientiphicalism, has been utterly catastrophic for mesoscale objects in general, but, with its closely associated twentieth-century formal logic, this has been especially true for notoriously vague things like climate change, coastlines, mountains and dust storms. That is, any so-called representations or references ultimately suffer the same ontological demise as their referents, no matter how well-defined their boundaries may in fact be. Against this reductionist metaphysics, climatic objects are discretized within three separate ontologically realist systems, Graham Harman's object-oriented philosophy, or ontology (OOO), Markus Gabriel's ontology of fields of sense (OFS) and Tristan Garcia's two systems and new order of time, so as to make an ontological case for any geographically scalar object, beginning with pixels, as well as any notoriously vague thing they are said to represent. Four-month overlapping TMAX seasonals were first developed from the Oak Ridge National Laboratory (ORNL) Daymet climate temperature maximum (TMAX) monthly summaries (1980-2016) for North America and segmented within Trimble's eCognition Developer using the simple and widely familiar quadtree algorithm with a scale parameter of four, in this example. The regression coefficient was then calculated for the resulting 37-year climatic objects and an equally simple classification was applied. The same segmentation and classification was applied to the Daymet annual summaries, as well, for comparison. As was expected, the mean warming and cooling trends are lowest for the annual summary TMAX climatic objects. However, the Fall (SOND) season has the largest and smallest areas of warming and cooling, respectively, and the highest mean trend for warming objects. Conversely, Spring (MAMJ) has the largest and smallest areas undergoing cooling and warming, respectively. Finally, Summer (JJAS) has the highest mean trend for cooling objects. Not only do these highly heterogeneous and variable patterns become readily apparent with each set of objects, but so do any possible anomalies that might warrant further investigation.

Creating a Canonical Scientific and Technical Information Classification System for NCSTRL+

NASA Technical Reports Server (NTRS)

Tiffany, Melissa E.; Nelson, Michael L.

1998-01-01

The purpose of this paper is to describe the new subject classification system for the NCSTRL+ project. NCSTRL+ is a canonical digital library (DL) based on the Networked Computer Science Technical Report Library (NCSTRL). The current NCSTRL+ classification system uses the NASA Scientific and Technical (STI) subject classifications, which has a bias towards the aerospace, aeronautics, and engineering disciplines. Examination of other scientific and technical information classification systems showed similar discipline-centric weaknesses. Traditional, library-oriented classification systems represented all disciplines, but were too generalized to serve the needs of a scientific and technically oriented digital library. Lack of a suitable existing classification system led to the creation of a lightweight, balanced, general classification system that allows the mapping of more specialized classification schemes into the new framework. We have developed the following classification system to give equal weight to all STI disciplines, while being compact and lightweight.

Gender differences in global-local perception? Evidence from orientation and shape judgments.

PubMed

Kimchi, Ruth; Amishav, Rama; Sulitzeanu-Kenan, Anat

2009-01-01

Direct examinations of gender differences in global-local processing are sparse, and the results are inconsistent. We examined this issue with a visuospatial judgment task and with a shape judgment task. Women and men were presented with hierarchical stimuli that varied in closure (open or closed shape) or in line orientation (oblique or horizontal/vertical) at the global or local level. The task was to classify the stimuli on the basis of the variation at the global level (global classification) or at the local level (local classification). Women's classification by closure (global or local) was more accurate than men's for stimuli that varied in closure on both levels, suggesting a female advantage in discriminating shape properties. No gender differences were observed in global-local processing bias. Women and men exhibited a global advantage, and they did not differ in their speed of global or local classification, with only one exception. Women were slower than men in local classification by orientation when the to-be-classified lines were embedded in a global line with a different orientation. This finding suggests that women are more distracted than men by misleading global oriented context when performing local orientation judgments, perhaps because women and men differ in their ability to use cognitive schemes to compensate for the distracting effects of the global context. Our findings further suggest that whether or not gender differences arise depends not only on the nature of the visual task but also on the visual context.

New nonlinear features for inspection, robotics, and face recognition

NASA Astrophysics Data System (ADS)

Casasent, David P.; Talukder, Ashit

1999-10-01

Classification of real-time X-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work, the MRDF is applied to standard features (rather than iconic data). The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC (receiver operating characteristic) data. Other applications of these new feature spaces in robotics and face recognition are also noted.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

PubMed Central

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

PubMed

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.

The Research on Dryland Crop Classification Based on the Fusion of SENTINEL-1A SAR and Optical Images

NASA Astrophysics Data System (ADS)

Liu, F.; Chen, T.; He, J.; Wen, Q.; Yu, F.; Gu, X.; Wang, Z.

2018-04-01

In recent years, the quick upgrading and improvement of SAR sensors provide beneficial complements for the traditional optical remote sensing in the aspects of theory, technology and data. In this paper, Sentinel-1A SAR data and GF-1 optical data were selected for image fusion, and more emphases were put on the dryland crop classification under a complex crop planting structure, regarding corn and cotton as the research objects. Considering the differences among various data fusion methods, the principal component analysis (PCA), Gram-Schmidt (GS), Brovey and wavelet transform (WT) methods were compared with each other, and the GS and Brovey methods were proved to be more applicable in the study area. Then, the classification was conducted based on the object-oriented technique process. And for the GS, Brovey fusion images and GF-1 optical image, the nearest neighbour algorithm was adopted to realize the supervised classification with the same training samples. Based on the sample plots in the study area, the accuracy assessment was conducted subsequently. The values of overall accuracy and kappa coefficient of fusion images were all higher than those of GF-1 optical image, and GS method performed better than Brovey method. In particular, the overall accuracy of GS fusion image was 79.8 %, and the Kappa coefficient was 0.644. Thus, the results showed that GS and Brovey fusion images were superior to optical images for dryland crop classification. This study suggests that the fusion of SAR and optical images is reliable for dryland crop classification under a complex crop planting structure.

Segmentation of prostate biopsy needles in transrectal ultrasound images

NASA Astrophysics Data System (ADS)

Krefting, Dagmar; Haupt, Barbara; Tolxdorff, Thomas; Kempkensteffen, Carsten; Miller, Kurt

2007-03-01

Prostate cancer is the most common cancer in men. Tissue extraction at different locations (biopsy) is the gold-standard for diagnosis of prostate cancer. These biopsies are commonly guided by transrectal ultrasound imaging (TRUS). Exact location of the extracted tissue within the gland is desired for more specific diagnosis and provides better therapy planning. While the orientation and the position of the needle within clinical TRUS image are limited, the appearing length and visibility of the needle varies strongly. Marker lines are present and tissue inhomogeneities and deflection artefacts may appear. Simple intensity, gradient oder edge-detecting based segmentation methods fail. Therefore a multivariate statistical classificator is implemented. The independent feature model is built by supervised learning using a set of manually segmented needles. The feature space is spanned by common binary object features as size and eccentricity as well as imaging-system dependent features like distance and orientation relative to the marker line. The object extraction is done by multi-step binarization of the region of interest. The ROI is automatically determined at the beginning of the segmentation and marker lines are removed from the images. The segmentation itself is realized by scale-invariant classification using maximum likelihood estimation and Mahalanobis distance as discriminator. The technique presented here could be successfully applied in 94% of 1835 TRUS images from 30 tissue extractions. It provides a robust method for biopsy needle localization in clinical prostate biopsy TRUS images.

Autonomous underwater vehicle adaptive path planning for target classification

NASA Astrophysics Data System (ADS)

Edwards, Joseph R.; Schmidt, Henrik

2002-11-01

Autonomous underwater vehicles (AUVs) are being rapidly developed to carry sensors into the sea in ways that have previously not been possible. The full use of the vehicles, however, is still not near realization due to lack of the true vehicle autonomy that is promised in the label (AUV). AUVs today primarily attempt to follow as closely as possible a preplanned trajectory. The key to increasing the autonomy of the AUV is to provide the vehicle with a means to make decisions based on its sensor receptions. The current work examines the use of active sonar returns from mine-like objects (MLOs) as a basis for sensor-based adaptive path planning, where the path planning objective is to discriminate between real mines and rocks. Once a target is detected in the mine hunting phase, the mine classification phase is initialized with a derivative cost function to emphasize signal differences and enhance classification capability. The AUV moves adaptively to minimize the cost function. The algorithm is verified using at-sea data derived from the joint MIT/SACLANTCEN GOATS experiments and advanced acoustic simulation using SEALAB. The mission oriented operating system (MOOS) real-time simulator is then used to test the onboard implementation of the algorithm.

Marking Importance in Lectures: Interactive and Textual Orientation

ERIC Educational Resources Information Center

Deroey, Katrien L. B.

2015-01-01

This paper provides a comprehensive overview of lexicogrammatical markers of important lecture points and proposes a classification in terms of their interactive and textual orientation. The importance markers were extracted from the British Academic Spoken English corpus using corpus-driven and corpus-based methods. The classification is based on…

Assessment of government tribology programs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peterson, M.B.; Levinson, T.M.

1985-09-01

An assessment has been made to determine current tribology research and development work sponsored or conducted by the government. Data base surveys and discussions were conducted to isolate current projects sponsored primarily by 21 different government organizations. These projects were classified by subject, objective, energy relevance, type of research, phenomenon being investigated, variables being studied, type of motion, materials and application. An abstract of each project was prepared which included the classification, sponsor, performing organization and a project description. It was found that current work is primarily materials oriented to meet military requirements. Other than the high temperature programs verymore » few of the tribology projects accomplish energy related objectives.« less

Remote Sensing Image Analysis Without Expert Knowledge - A Web-Based Classification Tool On Top of Taverna Workflow Management System

NASA Astrophysics Data System (ADS)

Selsam, Peter; Schwartze, Christian

2016-10-01

Providing software solutions via internet has been known for quite some time and is now an increasing trend marketed as "software as a service". A lot of business units accept the new methods and streamlined IT strategies by offering web-based infrastructures for external software usage - but geospatial applications featuring very specialized services or functionalities on demand are still rare. Originally applied in desktop environments, the ILMSimage tool for remote sensing image analysis and classification was modified in its communicating structures and enabled for running on a high-power server and benefiting from Tavema software. On top, a GIS-like and web-based user interface guides the user through the different steps in ILMSimage. ILMSimage combines object oriented image segmentation with pattern recognition features. Basic image elements form a construction set to model for large image objects with diverse and complex appearance. There is no need for the user to set up detailed object definitions. Training is done by delineating one or more typical examples (templates) of the desired object using a simple vector polygon. The template can be large and does not need to be homogeneous. The template is completely independent from the segmentation. The object definition is done completely by the software.

Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation and contrast of the spatial structures present in the image. Then the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines using the available spectral information and the extracted spatial information. Spatial post-processing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple classifier system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

Discovery of User-Oriented Class Associations for Enriching Library Classification Schemes.

ERIC Educational Resources Information Center

Pu, Hsiao-Tieh

2002-01-01

Presents a user-based approach to exploring the possibility of adding user-oriented class associations to hierarchical library classification schemes. Classes not grouped in the same subject hierarchies yet relevant to users' knowledge are obtained by analyzing a log book of a university library's circulation records, using collaborative filtering…

Software Re-Engineering of the Human Factors Analysis and Classification System - (Maintenance Extension) Using Object Oriented Methods in a Microsoft Environment

DTIC Science & Technology

2001-09-01

replication) -- all from Visual Basic and VBA . In fact, we found that the SQL Server engine actually had a plethora of options, most formidable of...2002, the new SQL Server 2000 database engine, and Microsoft Visual Basic.NET. This thesis describes our use of the Spiral Development Model to...versions of Microsoft products? Specifically, the pending release of Microsoft Office 2002, the new SQL Server 2000 database engine, and Microsoft

The Land-Use and Land-Cover Change Analysis in Beijing Huairou in Last Ten Years

NASA Astrophysics Data System (ADS)

Zhao, Q.; Liu, G.; Tu, J.; Wang, Z.

2018-04-01

With eCognition software, the sample-based object-oriented classification method is used. Remote sensing images in Huairou district of Beijing had been classified using remote sensing images of last ten years. According to the results of image processing, the land use types in Huairou district of Beijing were analyzed in the past ten years, and the changes of land use types in Huairou district were obtained, and the reasons for its occurrence were analyzed.

Single neural code for blur in subjects with different interocular optical blur orientation

PubMed Central

Radhakrishnan, Aiswaryah; Sawides, Lucie; Dorronsoro, Carlos; Peli, Eli; Marcos, Susana

2015-01-01

The ability of the visual system to compensate for differences in blur orientation between eyes is not well understood. We measured the orientation of the internal blur code in both eyes of the same subject monocularly by presenting pairs of images blurred with real ocular point spread functions (PSFs) of similar blur magnitude but varying in orientations. Subjects assigned a level of confidence to their selection of the best perceived image in each pair. Using a classification-images–inspired paradigm and applying a reverse correlation technique, a classification map was obtained from the weighted averages of the PSFs, representing the internal blur code. Positive and negative neural PSFs were obtained from the classification map, representing the neural blur for best and worse perceived blur, respectively. The neural PSF was found to be highly correlated in both eyes, even for eyes with different ocular PSF orientations (rPos = 0.95; rNeg = 0.99; p < 0.001). We found that in subjects with similar and with different ocular PSF orientations between eyes, the orientation of the positive neural PSF was closer to the orientation of the ocular PSF of the eye with the better optical quality (average difference was ∼10°), while the orientation of the positive and negative neural PSFs tended to be orthogonal. These results suggest a single internal code for blur with orientation driven by the orientation of the optical blur of the eye with better optical quality. PMID:26114678

Performance-scalable volumetric data classification for online industrial inspection

NASA Astrophysics Data System (ADS)

Abraham, Aby J.; Sadki, Mustapha; Lea, R. M.

2002-03-01

Non-intrusive inspection and non-destructive testing of manufactured objects with complex internal structures typically requires the enhancement, analysis and visualization of high-resolution volumetric data. Given the increasing availability of fast 3D scanning technology (e.g. cone-beam CT), enabling on-line detection and accurate discrimination of components or sub-structures, the inherent complexity of classification algorithms inevitably leads to throughput bottlenecks. Indeed, whereas typical inspection throughput requirements range from 1 to 1000 volumes per hour, depending on density and resolution, current computational capability is one to two orders-of-magnitude less. Accordingly, speeding up classification algorithms requires both reduction of algorithm complexity and acceleration of computer performance. A shape-based classification algorithm, offering algorithm complexity reduction, by using ellipses as generic descriptors of solids-of-revolution, and supporting performance-scalability, by exploiting the inherent parallelism of volumetric data, is presented. A two-stage variant of the classical Hough transform is used for ellipse detection and correlation of the detected ellipses facilitates position-, scale- and orientation-invariant component classification. Performance-scalability is achieved cost-effectively by accelerating a PC host with one or more COTS (Commercial-Off-The-Shelf) PCI multiprocessor cards. Experimental results are reported to demonstrate the feasibility and cost-effectiveness of the data-parallel classification algorithm for on-line industrial inspection applications.

SLO blind data set inversion and classification using physically complete models

NASA Astrophysics Data System (ADS)

Shamatava, I.; Shubitidze, F.; Fernández, J. P.; Barrowes, B. E.; O'Neill, K.; Grzegorczyk, T. M.; Bijamov, A.

2010-04-01

Discrimination studies carried out on TEMTADS and Metal Mapper blind data sets collected at the San Luis Obispo UXO site are presented. The data sets included four types of targets of interest: 2.36" rockets, 60-mm mortar shells, 81-mm projectiles, and 4.2" mortar items. The total parameterized normalized magnetic source (NSMS) amplitudes were used to discriminate TOI from metallic clutter and among the different hazardous UXO. First, in object's frame coordinate, the total NSMS were determined for each TOI along three orthogonal axes from the training data provided by the Strategic Environmental Research and Development Program (SERDP) along with the referred blind data sets. Then the inverted total NSMS were used to extract the time-decay classification features. Once our inversion and classification algorithms were tested on the calibration data sets then we applied the same procedure to all blind data sets. The combined NSMS and differential evolution algorithm is utilized for determine the NSMS strengths for each cell. The obtained total NSMS time-decay curves were used to extract the discrimination features and perform classification using the training data as reference. In addition, for cross validation, the inverted locations and orientations from NSMS-DE algorithm were compared against the inverted data that obtained via the magnetic field, vector and scalar potentials (HAP) method and the combined dipole and Gauss-Newton approach technique. We examined the entire time decay history of the total NSMS case-by-case for classification purposes. Also, we use different multi-class statistical classification algorithms for separating the dangerous objects from non hazardous items. The inverted targets were ranked by target ID and submitted to SERDP for independent scoring. The independent scoring results are presented.

Principals' Leadership Orientation in Relationship to the Classification of Their Schools in New Jersey

ERIC Educational Resources Information Center

dela Cruz, Samuel

2016-01-01

The relationship of principals' leadership orientations to the classification of their schools in New Jersey were examined in this study. While their role has expanded over the years, school principals continue to be essential in school reform and sustainability efforts. However, they are often overshadowed by the role of teachers. This…

The research on construction and application of machining process knowledge base

NASA Astrophysics Data System (ADS)

Zhao, Tan; Qiao, Lihong; Qie, Yifan; Guo, Kai

2018-03-01

In order to realize the application of knowledge in machining process design, from the perspective of knowledge in the application of computer aided process planning(CAPP), a hierarchical structure of knowledge classification is established according to the characteristics of mechanical engineering field. The expression of machining process knowledge is structured by means of production rules and the object-oriented methods. Three kinds of knowledge base models are constructed according to the representation of machining process knowledge. In this paper, the definition and classification of machining process knowledge, knowledge model, and the application flow of the process design based on the knowledge base are given, and the main steps of the design decision of the machine tool are carried out as an application by using the knowledge base.

Integration of a knowledge-based system and a clinical documentation system via a data dictionary.

PubMed

Eich, H P; Ohmann, C; Keim, E; Lang, K

1997-01-01

This paper describes the design and realisation of a knowledge-based system and a clinical documentation system linked via a data dictionary. The software was developed as a shell with object oriented methods and C++ for IBM-compatible PC's and WINDOWS 3.1/95. The data dictionary covers terminology and document objects with relations to external classifications. It controls the terminology in the documentation program with form-based entry of clinical documents and in the knowledge-based system with scores and rules. The software was applied to the clinical field of acute abdominal pain by implementing a data dictionary with 580 terminology objects, 501 document objects, and 2136 links; a documentation module with 8 clinical documents and a knowledge-based system with 10 scores and 7 sets of rules.

ASIST SIG/CR Classification Workshop 2000: Classification for User Support and Learning.

ERIC Educational Resources Information Center

Soergel, Dagobert

2001-01-01

Reports on papers presented at the 62nd Annual Meeting of ASIST (American Society for Information Science and Technology) for the Special Interest Group in Classification Research (SIG/CR). Topics include types of knowledge; developing user-oriented classifications, including domain analysis; classification in the user interface; and automatic…

Instructional Method Classifications Lack User Language and Orientation

ERIC Educational Resources Information Center

Neumann, Susanne; Koper, Rob

2010-01-01

Following publications emphasizing the need of a taxonomy for instructional methods, this article presents a literature review on classifications for learning and teaching in order to identify possible classifications for instructional methods. Data was collected for 37 classifications capturing the origins, theoretical underpinnings, purposes and…

An integration of minimum local feature representation methods to recognize large variation of foods

NASA Astrophysics Data System (ADS)

Razali, Mohd Norhisham bin; Manshor, Noridayu; Halin, Alfian Abdul; Mustapha, Norwati; Yaakob, Razali

2017-10-01

Local invariant features have shown to be successful in describing object appearances for image classification tasks. Such features are robust towards occlusion and clutter and are also invariant against scale and orientation changes. This makes them suitable for classification tasks with little inter-class similarity and large intra-class difference. In this paper, we propose an integrated representation of the Speeded-Up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors, using late fusion strategy. The proposed representation is used for food recognition from a dataset of food images with complex appearance variations. The Bag of Features (BOF) approach is employed to enhance the discriminative ability of the local features. Firstly, the individual local features are extracted to construct two kinds of visual vocabularies, representing SURF and SIFT. The visual vocabularies are then concatenated and fed into a Linear Support Vector Machine (SVM) to classify the respective food categories. Experimental results demonstrate impressive overall recognition at 82.38% classification accuracy based on the challenging UEC-Food100 dataset.

Mapping the distribution of mangrove species in the Core Zone of Mai Po Marshes Nature Reserve, Hong Kong, using hyperspectral data and high-resolution data

NASA Astrophysics Data System (ADS)

Jia, Mingming; Zhang, Yuanzhi; Wang, Zongming; Song, Kaishan; Ren, Chunying

2014-12-01

Mangrove species compositions and distributions are essential for conservation and restoration efforts. In this study, hyperspectral data of EO-1 HYPERION sensor and high spatial resolution data of SPOT-5 sensor were used in Mai Po mangrove species mapping. Objected-oriented method was used in mangrove species classification processing. Firstly, mangrove objects were obtained via segmenting high spatial resolution data of SPOT-5. Then the objects were classified into different mangrove species based on the spectral differences of HYPERION image. The classification result showed that in the top canopy, Kandelia obovata and Avicennia marina dominated Mai Po Marshes Natural Reserve, with area of 196.8 ha and 110.8 ha, respectively, Acanthus ilicifolius and Aegiceras corniculatum were mixed together and living at the edge of channels with an area of 11.7 ha. Additionally, mangrove species shows clearly zonations and associations in the Mai Po Core Zone. The overall accuracy of our mangrove map was 88% and the Kappa confidence was 0.83, which indicated great potential of using hyperspectral and high-resolution data for distinguishing and mapping mangrove species.

Task Oriented Evaluation of Module Extraction Techniques

NASA Astrophysics Data System (ADS)

Palmisano, Ignazio; Tamma, Valentina; Payne, Terry; Doran, Paul

Ontology Modularization techniques identify coherent and often reusable regions within an ontology. The ability to identify such modules, thus potentially reducing the size or complexity of an ontology for a given task or set of concepts is increasingly important in the Semantic Web as domain ontologies increase in terms of size, complexity and expressivity. To date, many techniques have been developed, but evaluation of the results of these techniques is sketchy and somewhat ad hoc. Theoretical properties of modularization algorithms have only been studied in a small number of cases. This paper presents an empirical analysis of a number of modularization techniques, and the modules they identify over a number of diverse ontologies, by utilizing objective, task-oriented measures to evaluate the fitness of the modules for a number of statistical classification problems.

REVIEW ARTICLE: Spectrophotometric applications of digital signal processing

NASA Astrophysics Data System (ADS)

Morawski, Roman Z.

2006-09-01

Spectrophotometry is more and more often the method of choice not only in analysis of (bio)chemical substances, but also in the identification of physical properties of various objects and their classification. The applications of spectrophotometry include such diversified tasks as monitoring of optical telecommunications links, assessment of eating quality of food, forensic classification of papers, biometric identification of individuals, detection of insect infestation of seeds and classification of textiles. In all those applications, large numbers of data, generated by spectrophotometers, are processed by various digital means in order to extract measurement information. The main objective of this paper is to review the state-of-the-art methodology for digital signal processing (DSP) when applied to data provided by spectrophotometric transducers and spectrophotometers. First, a general methodology of DSP applications in spectrophotometry, based on DSP-oriented models of spectrophotometric data, is outlined. Then, the most important classes of DSP methods for processing spectrophotometric data—the methods for DSP-aided calibration of spectrophotometric instrumentation, the methods for the estimation of spectra on the basis of spectrophotometric data, the methods for the estimation of spectrum-related measurands on the basis of spectrophotometric data—are presented. Finally, the methods for preprocessing and postprocessing of spectrophotometric data are overviewed. Throughout the review, the applications of DSP are illustrated with numerous examples related to broadly understood spectrophotometry.

Hazard classification assessment for the MC3423 detonator shipping package

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, R.B.

1981-11-05

An investigation was made to determine whether the MC3423 detonator should be assigned a DOT hazard classification of Detonating Fuze, Class C Explosive, per Federal Register 49 CFR 173.113, when packaged as specified. This study covers two propagation tests which evaluated the effects of two orientations of the MC3423 in its shipping tray. The method of testing was approved by DOE, Albuquerque Operations Office. Test data led to the recommended hazard classification of Detonating Fuze, Class C Explosive for both orientations of the detonator.

Evaluating the Generality and Limits of Blind Return-Oriented Programming Attacks

DTIC Science & Technology

2015-12-01

consider a recently proposed information disclosure vulnerability called blind return-oriented programming (BROP). Under certain conditions, this...implementation disclosure attacks 15. NUMBER OF PAGES 75 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18. SECURITY CLASSIFICATION OF...Science iii THIS PAGE INTENTIONALLY LEFT BLANK iv ABSTRACT We consider a recently proposed information disclosure vulnerability called blind return

A Proposal to Develop Interactive Classification Technology

NASA Technical Reports Server (NTRS)

deBessonet, Cary

1998-01-01

Research for the first year was oriented towards: 1) the design of an interactive classification tool (ICT); and 2) the development of an appropriate theory of inference for use in ICT technology. The general objective was to develop a theory of classification that could accommodate a diverse array of objects, including events and their constituent objects. Throughout this report, the term "object" is to be interpreted in a broad sense to cover any kind of object, including living beings, non-living physical things, events, even ideas and concepts. The idea was to produce a theory that could serve as the uniting fabric of a base technology capable of being implemented in a variety of automated systems. The decision was made to employ two technologies under development by the principal investigator, namely, SMS (Symbolic Manipulation System) and SL (Symbolic Language) [see debessonet, 1991, for detailed descriptions of SMS and SL]. The plan was to enhance and modify these technologies for use in an ICT environment. As a means of giving focus and direction to the proposed research, the investigators decided to design an interactive, classificatory tool for use in building accessible knowledge bases for selected domains. Accordingly, the proposed research was divisible into tasks that included: 1) the design of technology for classifying domain objects and for building knowledge bases from the results automatically; 2) the development of a scheme of inference capable of drawing upon previously processed classificatory schemes and knowledge bases; and 3) the design of a query/ search module for accessing the knowledge bases built by the inclusive system. The interactive tool for classifying domain objects was to be designed initially for textual corpora with a view to having the technology eventually be used in robots to build sentential knowledge bases that would be supported by inference engines specially designed for the natural or man-made environments in which the robots would be called upon to operate.

Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral–spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

An AI-based approach to structural damage identification by modal analysis

NASA Technical Reports Server (NTRS)

Glass, B. J.; Hanagud, S.

1990-01-01

Flexible-structure damage is presently addressed by a combined model- and parameter-identification approach which employs the AI methodologies of classification, heuristic search, and object-oriented model knowledge representation. The conditions for model-space search convergence to the best model are discussed in terms of search-tree organization and initial model parameter error. In the illustrative example of a truss structure presented, the use of both model and parameter identification is shown to lead to smaller parameter corrections than would be required by parameter identification alone.

Remembering Left–Right Orientation of Pictures

PubMed Central

Bartlett, James C.; Gernsbacher, Morton Ann; Till, Robert E.

2015-01-01

In a study of recognition memory for pictures, we observed an asymmetry in classifying test items as “same” versus “different” in left–right orientation: Identical copies of previously viewed items were classified more accurately than left–right reversals of those items. Response bias could not explain this asymmetry, and, moreover, correct “same” and “different” classifications were independently manipulable: Whereas repetition of input pictures (one vs. two presentations) affected primarily correct “same” classifications, retention interval (3 hr vs. 1 week) affected primarily correct “different” classifications. In addition, repetition but not retention interval affected judgments that previously seen pictures (both identical and reversed) were “old”. These and additional findings supported a dual-process hypothesis that links “same” classifications to high familiarity, and “different” classifications to conscious sampling of images of previously viewed pictures. PMID:2949051

Constructivisms from a genetic point of view: a critical classification of current tendencies.

PubMed

Sánchez, José Carlos; Loredo, José Carlos

2009-12-01

In this paper, we propose a critical classification of contemporary constructivist orientations. Our fundamental theoretical reference is the notion of genesis, understood as the construction of reality in a way that is neither relativist nor positivist-realist. We identify a nucleus of classic, genetic constructivism that revolves around the ideas of Baldwin, Piaget and Vygotsky and discuss two tendencies that distort the spirit of that nucleus: objectivism and subjectivism. Objectivism rules out the psychological, constructive activity of the subject, subordinating (or just reducing) it to objective structures either from nature (like genetic endowment or neural functioning), or from culture (like language or social practices). Subjectivism completely detaches the objectivity of knowledge from its construction on the part of the subject, reducing it to the mere product of individual interest, view, or irrationality. Thus, subjectivism is the non-constructive way to conceive the subject. Then, we attempt to show the dialectics that exists between these two tendencies and the scope of our criteria by analysing a representative (non exhaustive) group of authors who are defined as constructivists or who bring important elements to the debate about constructivism.

Evaluation of single-band snow-patch mapping using high-resolution microwave remote sensing: an application in the maritime Antarctic

NASA Astrophysics Data System (ADS)

Mora, Carla; Jiménez, Juan Javier; Pina, Pedro; Catalão, João; Vieira, Gonçalo

2017-01-01

The mountainous and ice-free terrains of the maritime Antarctic generate complex mosaics of snow patches, ranging from tens to hundreds of metres. These can only be accurately mapped using high-resolution remote sensing. In this paper we evaluate the application of radar scenes from TerraSAR-X in High Resolution SpotLight mode for mapping snow patches at a test area on Fildes Peninsula (King George Island, South Shetlands). Snow-patch mapping and characterization of snow stratigraphy were conducted at the time of image acquisition on 12 and 13 January 2012. Snow was wet in all studied snow patches, with coarse-grain and rounded crystals showing advanced melting and with frequent ice layers in the snow pack. Two TerraSAR-X scenes in HH and VV polarization modes were analysed, with the former showing the best results when discriminating between wet snow, lake water and bare soil. However, significant overlap in the backscattering signal was found. Average wet-snow backscattering was -18.0 dB in HH mode, with water showing -21.1 dB and bare soil showing -11.9 dB. Single-band pixel-based and object-oriented image classification methods were used to assess the classification potential of TerraSAR-X SpotLight imagery. The best results were obtained with an object-oriented approach using a watershed segmentation with a support vector machine (SVM) classifier, with an overall accuracy of 92 % and Kappa of 0.88. The main limitation was the west to north-west facing snow patches, which showed significant error, an issue related to artefacts from the geometry of satellite imagery acquisition. The results show that TerraSAR-X in SpotLight mode provides high-quality imagery for mapping wet snow and snowmelt in the maritime Antarctic. The classification procedure that we propose is a simple method and a first step to an implementation in operational mode if a good digital elevation model is available.

Toward a Reasoned Classification of Diseases Using Physico-Chemical Based Phenotypes

PubMed Central

Schwartz, Laurent; Lafitte, Olivier; da Veiga Moreira, Jorgelindo

2018-01-01

Background: Diseases and health conditions have been classified according to anatomical site, etiological, and clinical criteria. Physico-chemical mechanisms underlying the biology of diseases, such as the flow of energy through cells and tissues, have been often overlooked in classification systems. Objective: We propose a conceptual framework toward the development of an energy-oriented classification of diseases, based on the principles of physical chemistry. Methods: A review of literature on the physical chemistry of biological interactions in a number of diseases is traced from the point of view of the fluid and solid mechanics, electricity, and chemistry. Results: We found consistent evidence in literature of decreased and/or increased physical and chemical forces intertwined with biological processes of numerous diseases, which allowed the identification of mechanical, electric and chemical phenotypes of diseases. Discussion: Biological mechanisms of diseases need to be evaluated and integrated into more comprehensive theories that should account with principles of physics and chemistry. A hypothetical model is proposed relating the natural history of diseases to mechanical stress, electric field, and chemical equilibria (ATP) changes. The present perspective toward an innovative disease classification may improve drug-repurposing strategies in the future. PMID:29541031

Mastery motivation as a predictor of occupational performance following upper limb intervention for school-aged children with congenital hemiplegia.

PubMed

Miller, Laura; Ziviani, Jenny; Ware, Robert S; Boyd, Roslyn N

2014-10-01

To determine the extent to which children's mastery motivation predicts occupational performance outcomes following upper limb intervention (ULI). In this cohort study, participants received 45 hours of ULI, either in an intensive group-based or distributed individualized model. The Dimensions of Mastery Questionnaire (DMQ) measured mastery motivation at baseline. Occupational performance outcomes were assessed at baseline and 13 weeks' post-intervention using the Canadian Occupational Performance Measure (COPM). Multivariable models determined the contribution of mastery motivation to COPM outcome irrespective of group membership. Forty-two children with congenital hemiplegia (29 males, 13 females; mean age 7y 8mo [SD 2y 2mo]; range 5y 1mo-12y 8mo; Manual Ability Classification System [MACS] I=20 and II=22; predominant motor type unilateral spastic n=41) participated in the study. Significant gains were seen in COPM performance and satisfaction scores (p<0.001) post-intervention with no between group differences. Children who had greater persistence with object-oriented tasks (p=0.02) and better manual ability (p=0.03) achieved higher COPM performance scores at 13 weeks. Children's persistence on object-oriented tasks was the strongest predictor of COPM satisfaction (p=0.01). Children's persistence with object-oriented tasks as well as manual abilities needs to be considered when undertaking ULI. Predetermining children's motivational predispositions can assist clinicians to tailor therapy sessions individually based on children's strengths, contributing to effective engagement in ULI. © 2014 Mac Keith Press.

The comprehensive health care orientation process indicators explain hospital organisation's attractiveness: a Bayesian analysis of newly hired nurse and physician survey data.

PubMed

Peltokoski, Jaana; Vehviläinen-Julkunen, Katri; Pitkäaho, Taina; Mikkonen, Santtu; Miettinen, Merja

2015-10-01

To examine the relationship of a comprehensive health care orientation process with a hospital's attractiveness. Little is known about indicators of the employee orientation process that most likely explain a hospital organisation's attractiveness. Empirical data collected from registered nurses (n = 145) and physicians (n = 37) working in two specialised hospital districts. A Naive Bayes Classification was applied to examine the comprehensive orientation process indicators that predict hospital's attractiveness. The model was composed of five orientation process indicators: the contribution of the orientation process to nurses' and physicians' intention to stay; the defined responsibilities of the orientation process; interaction between newcomer and colleagues; responsibilities that are adapted for tasks; and newcomers' baseline knowledge assessment that should be done before the orientation phase. The Naive Bayes Classification was used to explore employee orientation process and related indicators. The model constructed provides insight that can be used in designing and implementing the orientation process to promote the hospital organisation's attractiveness. Managers should focus on developing fluently organised orientation practices based on the indicators that predict the hospital's attractiveness. For the purpose of personalised orientation, employees' baseline knowledge and competence level should be assessed before the orientation phase. © 2014 John Wiley & Sons Ltd.

Augmented reality three-dimensional object visualization and recognition with axially distributed sensing.

PubMed

Markman, Adam; Shen, Xin; Hua, Hong; Javidi, Bahram

2016-01-15

An augmented reality (AR) smartglass display combines real-world scenes with digital information enabling the rapid growth of AR-based applications. We present an augmented reality-based approach for three-dimensional (3D) optical visualization and object recognition using axially distributed sensing (ADS). For object recognition, the 3D scene is reconstructed, and feature extraction is performed by calculating the histogram of oriented gradients (HOG) of a sliding window. A support vector machine (SVM) is then used for classification. Once an object has been identified, the 3D reconstructed scene with the detected object is optically displayed in the smartglasses allowing the user to see the object, remove partial occlusions of the object, and provide critical information about the object such as 3D coordinates, which are not possible with conventional AR devices. To the best of our knowledge, this is the first report on combining axially distributed sensing with 3D object visualization and recognition for applications to augmented reality. The proposed approach can have benefits for many applications, including medical, military, transportation, and manufacturing.

Reliability of McConnell's classification of patellar orientation in symptomatic and asymptomatic subjects.

PubMed

Watson, C J; Propps, M; Galt, W; Redding, A; Dobbs, D

1999-07-01

Test-retest reliability study with blinded testers. To determine the intratester reliability of the McConnell classification system and to determine whether the intertester reliability of this system would be improved by one-on-one training of the testers, increasing the variability and numbers of subjects, blinding the testers to the absence or presence of patellofemoral pain syndrome, and adhering to the McConnell classification system as it is taught in the "McConnell Patellofemoral Treatment Plan" continuing education course. The McConnell classification system is currently used by physical therapy clinicians to quantify static patellar orientation. The measurements generated from this system purportedly guide the therapist in the application of patellofemoral tape and in assessment of the efficacy of treatment interventions on changing patellar orientation. Fifty-six subjects (age range, 21-65 years) provided a total of 101 knees for assessment. Seventy-six knees did not produce symptoms. A researcher who did not participate in the measuring process determined that 17 subjects had patellofemoral pain syndrome in 25 knees. Two testers concurrently measured static patellar orientation (anterior/posterior and medial/lateral tilt, medial/lateral glide, and patellar rotation) on subjects, using the McConnell classification system. Repeat measures were performed 3-7 days later. A kappa (kappa) statistic was used to assess the degree of agreement within each tester and between testers. The kappa coefficients for intratester reliability varied from -0.06 to 0.35. Intertester reliability ranged from -0.03 to 0.19. The McConnell classification system, in its current form, does not appear to be very reliable. Intratester reliability ranged from poor to fair, and intertester reliability was poor to slight. This system should not be used as a measurement tool or as a basis for treatment decisions.

Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Blaschke, Thomas; Tiede, Dirk; Moghaddam, Mohammad Hossein Rezaei

2017-09-01

This article presents a method of object-based image analysis (OBIA) for landslide delineation and landslide-related change detection from multi-temporal satellite images. It uses both spatial and spectral information on landslides, through spectral analysis, shape analysis, textural measurements using a gray-level co-occurrence matrix (GLCM), and fuzzy logic membership functionality. Following an initial segmentation step, particular combinations of various information layers were investigated to generate objects. This was achieved by applying multi-resolution segmentation to IRS-1D, SPOT-5, and ALOS satellite imagery in sequential steps of feature selection and object classification, and using slope and flow direction derivatives from a digital elevation model together with topographically-oriented gray level co-occurrence matrices. Fuzzy membership values were calculated for 11 different membership functions using 20 landslide objects from a landslide training data. Six fuzzy operators were used for the final classification and the accuracies of the resulting landslide maps were compared. A Fuzzy Synthetic Evaluation (FSE) approach was adapted for validation of the results and for an accuracy assessment using the landslide inventory database. The FSE approach revealed that the AND operator performed best with an accuracy of 93.87% for 2005 and 94.74% for 2011, closely followed by the MEAN Arithmetic operator, while the OR and AND (*) operators yielded relatively low accuracies. An object-based change detection was then applied to monitor landslide-related changes that occurred in northern Iran between 2005 and 2011. Knowledge rules to detect possible landslide-related changes were developed by evaluating all possible landslide-related objects for both time steps.

New Employee Orientation, Division of Personnel and Labor Relations,

Science.gov Websites

Employee Training Exit Survey HR Forms New Employee Orientation For Admin Staff Classification Form Packets State Officer Compensation Commission Administration / Personnel and Labor Relations / New Employee Orientation New Employee Orientation Last Updated: October 2013 Welcome to the State of Alaska As a new

Rotation-invariant features for multi-oriented text detection in natural images.

PubMed

Yao, Cong; Zhang, Xin; Bai, Xiang; Liu, Wenyu; Ma, Yi; Tu, Zhuowen

2013-01-01

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes.

Characterization of the horizontal structure of the tropical forest canopy using object-based LiDAR and multispectral image analysis

NASA Astrophysics Data System (ADS)

Dupuy, Stéphane; Lainé, Gérard; Tassin, Jacques; Sarrailh, Jean-Michel

2013-12-01

This article's goal is to explore the benefits of using Digital Surface Model (DSM) and Digital Terrain Model (DTM) derived from LiDAR acquisitions for characterizing the horizontal structure of different facies in forested areas (primary forests vs. secondary forests) within the framework of an object-oriented classification. The area under study is the island of Mayotte in the western Indian Ocean. The LiDAR data were the data originally acquired by an airborne small-footprint discrete-return LiDAR for the "Litto3D" coastline mapping project. They were used to create a Digital Elevation Model (DEM) at a spatial resolution of 1 m and a Digital Canopy Model (DCM) using median filtering. The use of two successive segmentations at different scales allowed us to adjust the segmentation parameters to the local structure of the landscape and of the cover. Working in object-oriented mode with LiDAR allowed us to discriminate six vegetation classes based on canopy height and horizontal heterogeneity. This heterogeneity was assessed using a texture index calculated from the height-transition co-occurrence matrix. Overall accuracy exceeds 90%. The resulting product is the first vegetation map of Mayotte which emphasizes the structure over the composition.

A study of navigation in virtual space

NASA Technical Reports Server (NTRS)

Darken, Rudy; Sibert, John L.; Shumaker, Randy

1994-01-01

In the physical world, man has developed efficient methods for navigation and orientation. These methods are dependent on the high-fidelity stimuli presented by the environment. When placed in a virtual world which cannot offer stimuli of the same quality due to computing constraints and immature technology, tasks requiring the maintenance of position and orientation knowledge become laborious. In this paper, we present a representative set of techniques based on principles of navigation derived from real world analogs including human and avian navigation behavior and cartography. A preliminary classification of virtual worlds is presented based on the size of the world, the density of objects in the world, and the level of activity taking place in the world. We also summarize an informal study we performed to determine how the tools influenced the subjects' navigation strategies and behavior. We conclude that principles extracted from real world navigation aids such as maps can be seen to apply in virtual environments.

Rheological Characterization and Cluster Classification of Iranian Commercial Foods, Drinks and Desserts to Recommend for Esophageal Dysphagia Diets

PubMed Central

ZARGARAAN, Azizollaah; OMARAEE, Yasaman; RASTMANESH, Reza; TAHERI, Negin; FADAVI, Ghasem; FADAEI, Morteza; MOHAMMADIFAR, Mohammad Amin

2013-01-01

Abstract Background In the absence of dysphagia-oriented food products, rheological characterization of available food items is of importance for safe swallowing and adequate nutrient intake of dysphagic patients. In this way, introducing alternative items (with similar ease of swallow) is helpful to improve quality of life and nutritional intake of esophageal cancer dysphagia patients. The present study aimed at rheological characterization and cluster classification of potentially suitable foodstuffs marketed in Iran for their possible use in dysphagia diets. Methods In this descriptive study, rheological data were obtained during January and February 2012 in Rheology Lab of National Nutrition and Food Technology Research Institute Tehran, Iran. Steady state and oscillatory shear parameters of 39 commercial samples were obtained using a Physica MCR 301 rheometer (Anton-Paar, GmbH, Graz, Austria). Matlab Fuzzy Logic Toolbox (R2012 a) was utilized for cluster classification of the samples. Results Using an extended list of rheological parameters and fuzzy logic methods, 39 commercial samples (drinks, main courses and desserts) were divided to 5 clusters and degree of membership to each cluster was stated by a number between 0 and 0.99. Conclusion Considering apparent viscosity of foodstuffs as a single criterion for classification of dysphagia-oriented food products is shortcoming of current guidelines in dysphagia diets. Authors proposed to some revisions in classification of dysphagia-oriented food products and including more rheological parameters (especially, viscoelastic parameters) in the classification. PMID:26060647

Rheological Characterization and Cluster Classification of Iranian Commercial Foods, Drinks and Desserts to Recommend for Esophageal Dysphagia Diets.

PubMed

Zargaraan, Azizollaah; Omaraee, Yasaman; Rastmanesh, Reza; Taheri, Negin; Fadavi, Ghasem; Fadaei, Morteza; Mohammadifar, Mohammad Amin

2013-12-01

In the absence of dysphagia-oriented food products, rheological characterization of available food items is of importance for safe swallowing and adequate nutrient intake of dysphagic patients. In this way, introducing alternative items (with similar ease of swallow) is helpful to improve quality of life and nutritional intake of esophageal cancer dysphagia patients. The present study aimed at rheological characterization and cluster classification of potentially suitable foodstuffs marketed in Iran for their possible use in dysphagia diets. In this descriptive study, rheological data were obtained during January and February 2012 in Rheology Lab of National Nutrition and Food Technology Research Institute Tehran, Iran. Steady state and oscillatory shear parameters of 39 commercial samples were obtained using a Physica MCR 301 rheometer (Anton-Paar, GmbH, Graz, Austria). Matlab Fuzzy Logic Toolbox (R2012 a) was utilized for cluster classification of the samples. Using an extended list of rheological parameters and fuzzy logic methods, 39 commercial samples (drinks, main courses and desserts) were divided to 5 clusters and degree of membership to each cluster was stated by a number between 0 and 0.99. Considering apparent viscosity of foodstuffs as a single criterion for classification of dysphagia-oriented food products is shortcoming of current guidelines in dysphagia diets. Authors proposed to some revisions in classification of dysphagia-oriented food products and including more rheological parameters (especially, viscoelastic parameters) in the classification.

Land cover change assessment using object-oriented classification based on image segmentation in the Binah river watershed (Togo and Benin)

NASA Astrophysics Data System (ADS)

Badjana, M. H.; Helmschrot, J.; Wala, K.; Flugel, W. A.; Afouda, A.; Akpagana, K.

2014-12-01

Assessing and monitoring land cover changes over time, especially in Sub-Saharan Africa characterized by both a high population growth and the highest rate of land degradation in the world is of high relevance for sustainable land management, water security and food production. In this study, land cover changes between 1972 and 2013 were investigated in the Binah river watershed (North of Togo and Benin) using advanced remote sensing and GIS technologies to support sustainable land and water resources management efforts. To this end, multi-temporal satellite images - Landsat MSS (1972), TM (1987) and ETM+ (2013) were processed using object-oriented classification based on image segmentation and post-classification comparison methods. Five main land cover classes namely agricultural land, forest land, savannah, settlements and water bodies have been identified with overall accuracies of 75.11% (1972), 81.82% (1987), and 86.1% (2013) and respective Kappa statistics of 0.67, 0.76 and 0.83. These classification results helped to explicitly assess the spatio-temporal pattern of land cover within the basin. The results indicate that savannah as the main vegetation type in the basin has decreased from 63.3% of the basin area in 1972 to 60.4% in 1987 and 35.6% in 2013. Also the forest land which covered 20.7% in 1972 has decreased to 12.7% in 1987 and 11.7% in 2013. This severe decrease in vegetation mainly resulted from the extension of agricultural areas and settlements, which is, thus, considered as the main driving force. In fact, agricultural land increased of 61.4% from 1972 to 1987, 81.4% from 1987 to 2013 and almost twice from 1972 to 2013 while human settlements increased from 0.8% of the basin area in 1972 to 2.5% in 1987 and 7.7% in 2013. The transition maps illustrate the conversion of savannah to agricultural land at each time step relating to slash and burn agriculture, but also demonstrate the threat of environmental degradation of the savannah biome. However, at the same time, some proportions of agricultural land were converted to savannah relating to fallow agriculture. As a first assessment for the Binah river watershed, this study provides useful guidelines for vegetation restoration and conservation, efforts in managing land degradation and implementing integrated land and water resources management plans.

Object-oriented classification using quasi-synchronous multispectral images (optical and radar) over agricultural surface

NASA Astrophysics Data System (ADS)

Marais Sicre, Claire; Baup, Frederic; Fieuzal, Remy

2015-04-01

In the context of climate change (with consequences on temperature and precipitation patterns), persons involved in agricultural management have the imperative to combine: sufficient productivity (as a response of the increment of the necessary foods) and durability of the resources (in order to restrain waste of water, fertilizer or environmental damages). To this end, a detailed knowledge of land use will improve the management of food and water, while preserving the ecosystems. Among the wide range of available monitoring tools, numerous studies demonstrated the interest of satellite images for agricultural mapping. Recently, the launch of several radar and optical sensors offer new perspectives for the multi-wavelength crop monitoring (Terrasar-X, Radarsat-2, Sentinel-1, Landsat-8…) allowing surface survey whatever the cloud conditions. Previous studies have demonstrated the interest of using multi-temporal approaches for crop classification, requiring several images for suitable classification results. Unfortunately, these approaches are limited (due to the satellite orbit cycle) and require waiting several days, week or month before offering an accurate land use map. The objective of this study is to compare the accuracy of object-oriented classification (random forest algorithm combined with vector layer coming from segmentation) to map winter crop (barley, rapeseed, grasslands and wheat) and soil states (bare soils with different surface roughness) using quasi-synchronous images. Satellite data are composed of multi-frequency and multi-polarization (HH, VV, HV and VH) images acquired near the 14th of April, 2010, over a studied area (90km²) located close to Toulouse in France. This is a region of alluvial plains and hills, which are mostly mixed farming and governed by a temperate climate. Remote sensing images are provided by Formosat-2 (04/18), Radarsat-2 (C-band, 04/15), Terrasar-X (X-band, 04/14) and ALOS (L-band, 04/14). Ground data are collected over 214 plots during the MCM'10 experiment conducted by the CESBIO laboratory in 2010. Classifications performances have been evaluated considering two cases: using only one frequency in optical or microwave domain, or using a combination of several frequencies (mixed between optical and microwave). For the first case, best results were obtained using optical wavelength with mean overall accuracy (OA) of 84%, followed by Terrasar-X (HH) and Radarsat-2 (HV or HV) which respectively offer overall accuracies of 77% and 73%. Concerning the vegetation, wheat was well classified whatever the wavelength used (OA > 93%). Barley was more complicated to classified and could be mingled with wheat or grassland. Best results were obtained using of green, red, blue, X-band or L-band wavelength offering an OA superior to 45%. Radar images were clearly well adapted to identify rapeseed (OA > 83%), especially at C (VV, HH and HV) and X-band (HH). The accuracy of grassland classification never exceeded 79% and results were stable between frequencies (excepted at L-band: 51%). The three soil roughness states were quite well classified whatever the wavelength and performances decreased with the increase of soil roughness. The combine use of multi-frequencies increased performances of the classification. Overall accuracy reached respectively 83% and 96% for C-band full polarization and for Formosat-2 multispectral approaches.

A fingerprint classification algorithm based on combination of local and global information

NASA Astrophysics Data System (ADS)

Liu, Chongjin; Fu, Xiang; Bian, Junjie; Feng, Jufu

2011-12-01

Fingerprint recognition is one of the most important technologies in biometric identification and has been wildly applied in commercial and forensic areas. Fingerprint classification, as the fundamental procedure in fingerprint recognition, can sharply decrease the quantity for fingerprint matching and improve the efficiency of fingerprint recognition. Most fingerprint classification algorithms are based on the number and position of singular points. Because the singular points detecting method only considers the local information commonly, the classification algorithms are sensitive to noise. In this paper, we propose a novel fingerprint classification algorithm combining the local and global information of fingerprint. Firstly we use local information to detect singular points and measure their quality considering orientation structure and image texture in adjacent areas. Furthermore the global orientation model is adopted to measure the reliability of singular points group. Finally the local quality and global reliability is weighted to classify fingerprint. Experiments demonstrate the accuracy and effectivity of our algorithm especially for the poor quality fingerprint images.

Enhancing Interactivity and Productivity through Object-Oriented Authoring: An Instructional Designer's Perspective.

ERIC Educational Resources Information Center

Chapman, Bryan L.

1994-01-01

Discusses the effect of object-oriented programming on the evolution of authoring systems. Topics include the definition of an object; examples of object-oriented authoring interfaces; what object-orientation means to an instructional developer; how object orientation increases productivity and enhances interactivity; and the future of courseware…

Wayfinding concept in University of Brawijaya

NASA Astrophysics Data System (ADS)

Firjatullah, H.; Kurniawan, E. B.; Purnamasari, W. D.

2017-06-01

Wayfinding is an activity related to the orientation and motion from first point to point of destination. Benefits of wayfinding in the area of education, namely as a means of helping direct a person to a destination so as to reduce the lostness and assist users in remembering the way through the role of space and objects wayfinding. Around 48% new students of University of Brawijaya (UB) 2015 that was ever lost when entering the campus. This shows the need for wayfinding concept to someone who was unfamiliar with the surrounding area as freshmen. This study uses mental map analysis to find the objects hierarchy wayfinding determination based on the respondents and the space syntax (visual graph analysis) as a hierarchy based on the determination of the configuration space. The overlay result say that hierarchy between both of analysis shows there are several objects which are potentially in wayfinding process on the campus of UB. The concept of wayfinding generate different treatment of the selected object based of wayfinding classification, both in maintaining the function of the object in space and develop the potential of the object wayfinding.

The BL Lac Classification

NASA Astrophysics Data System (ADS)

Landt, H.; Padovani, P.

1999-12-01

In the optical wavelength range the distinction between a radio galaxy and a BL Lac object is mainly based on the Ca II H and K break observed in the optical spectrum. Marchã et al. (1996, MNRAS, 281, 425) have expanded on the previously used division by suggesting objects with Ca II break values lower than 0.4 to be classified as BL Lacs and sources with values higher than 0.4 to be classified as galaxies. We present new evidence that there is a smooth transition between BL Lac objects and Fanaroff-Riley type I radio galaxies. We find an increase in X-ray and radio core luminosity as the Ca II break gets more and more diluted. This suggests that the only difference between BL Lac objects and their parent population lies in orientation. The closer the jet of the radio galaxy to the observer's line of sight, the more its luminosity gets amplified and the object becomes BL Lac-like. We will address the question of the BL Lac parent population and will propose to unify the beamed and unbeamed objects in nomenclature.

Extraction of texture features with a multiresolution neural network

NASA Astrophysics Data System (ADS)

Lepage, Richard; Laurendeau, Denis; Gagnon, Roger A.

1992-09-01

Texture is an important surface characteristic. Many industrial materials such as wood, textile, or paper are best characterized by their texture. Detection of defaults occurring on such materials or classification for quality control anD matching can be carried out through careful texture analysis. A system for the classification of pieces of wood used in the furniture industry is proposed. This paper is concerned with a neural network implementation of the features extraction and classification components of the proposed system. Texture appears differently depending at which spatial scale it is observed. A complete description of a texture thus implies an analysis at several spatial scales. We propose a compact pyramidal representation of the input image for multiresolution analysis. The feature extraction system is implemented on a multilayer artificial neural network. Each level of the pyramid, which is a representation of the input image at a given spatial resolution scale, is mapped into a layer of the neural network. A full resolution texture image is input at the base of the pyramid and a representation of the texture image at multiple resolutions is generated by the feedforward pyramid structure of the neural network. The receptive field of each neuron at a given pyramid level is preprogrammed as a discrete Gaussian low-pass filter. Meaningful characteristics of the textured image must be extracted if a good resolving power of the classifier must be achieved. Local dominant orientation is the principal feature which is extracted from the textured image. Local edge orientation is computed with a Sobel mask at four orientation angles (multiple of (pi) /4). The resulting intrinsic image, that is, the local dominant orientation image, is fed to the texture classification neural network. The classification network is a three-layer feedforward back-propagation neural network.

Spatial scale and distribution of neurovascular signals underlying decoding of orientation and eye of origin from fMRI data

PubMed Central

Harrison, Charlotte; Jackson, Jade; Oh, Seung-Mock; Zeringyte, Vaida

2016-01-01

Multivariate pattern analysis of functional magnetic resonance imaging (fMRI) data is widely used, yet the spatial scales and origin of neurovascular signals underlying such analyses remain unclear. We compared decoding performance for stimulus orientation and eye of origin from fMRI measurements in human visual cortex with predictions based on the columnar organization of each feature and estimated the spatial scales of patterns driving decoding. Both orientation and eye of origin could be decoded significantly above chance in early visual areas (V1–V3). Contrary to predictions based on a columnar origin of response biases, decoding performance for eye of origin in V2 and V3 was not significantly lower than that in V1, nor did decoding performance for orientation and eye of origin differ significantly. Instead, response biases for both features showed large-scale organization, evident as a radial bias for orientation, and a nasotemporal bias for eye preference. To determine whether these patterns could drive classification, we quantified the effect on classification performance of binning voxels according to visual field position. Consistent with large-scale biases driving classification, binning by polar angle yielded significantly better decoding performance for orientation than random binning in V1–V3. Similarly, binning by hemifield significantly improved decoding performance for eye of origin. Patterns of orientation and eye preference bias in V2 and V3 showed a substantial degree of spatial correlation with the corresponding patterns in V1, suggesting that response biases in these areas originate in V1. Together, these findings indicate that multivariate classification results need not reflect the underlying columnar organization of neuronal response selectivities in early visual areas. NEW & NOTEWORTHY Large-scale response biases can account for decoding of orientation and eye of origin in human early visual areas V1–V3. For eye of origin this pattern is a nasotemporal bias; for orientation it is a radial bias. Differences in decoding performance across areas and stimulus features are not well predicted by differences in columnar-scale organization of each feature. Large-scale biases in extrastriate areas are spatially correlated with those in V1, suggesting biases originate in primary visual cortex. PMID:27903637

The Longterm Centimeter-band Total Flux and Linear Polarization Properties of the Pearson-Readhead Survey Sources

NASA Astrophysics Data System (ADS)

Aller, M. F.; Aller, H. D.; Hughes, P. A.

2001-12-01

Using centimeter-band total flux and linear polarization observations of the Pearson-Readhead sample sources systematically obtained with the UMRAO 26-m radio telescope during the past 16 years, we identify the range of variability properties and their temporal changes as functions of both optical and radio morphological classification. We find that our earlier statistical analysis, based on a time window of 6.4 years, did not delineate the full amplitude range of the total flux variability; further, several galaxies exhibit longterm, systematic changes or rather infrequent outbursts requiring long term observations for detection. Using radio classification as a delineator, we confirm, and find additional evidence, that significant changes in flux density can occur in steep spectrum and lobe-dominated objects as well as in compact, flat-spectrum objects. We find that statistically the time-averaged total flux density spectra steepen when longer time windows are included, which we attribute to a selection effect in the source sample. We have identified preferred orientations of the electric vector of the polarized emission (EVPA) in an unbiased manner in several sources, including several QSOs which have exhibited large variations in total flux while maintaining stable EVPAs, and compared these with orientations of the flow direction indicated by VLB morphology. We have looked for systematic, monotonic changes in EVPA which might be expected in the emission from a precessing jet, but none were identified. A Scargle periodogram analysis found no strong evidence for periodicity in any of the sample sources. We thank the NSF for grants AST-8815678, AST-9120224, AST-9421979, and AST-9900723 which provided partial support for this research. The operation of the 26-meter telescope is supported by the University of Michigan Department of Astronomy.

Automated detection of snow avalanche deposits: segmentation and classification of optical remote sensing imagery

NASA Astrophysics Data System (ADS)

Lato, M. J.; Frauenfelder, R.; Bühler, Y.

2012-09-01

Snow avalanches in mountainous areas pose a significant threat to infrastructure (roads, railways, energy transmission corridors), personal property (homes) and recreational areas as well as for lives of people living and moving in alpine terrain. The impacts of snow avalanches range from delays and financial loss through road and railway closures, destruction of property and infrastructure, to loss of life. Avalanche warnings today are mainly based on meteorological information, snow pack information, field observations, historically recorded avalanche events as well as experience and expert knowledge. The ability to automatically identify snow avalanches using Very High Resolution (VHR) optical remote sensing imagery has the potential to assist in the development of accurate, spatially widespread, detailed maps of zones prone to avalanches as well as to build up data bases of past avalanche events in poorly accessible regions. This would provide decision makers with improved knowledge of the frequency and size distributions of avalanches in such areas. We used an object-oriented image interpretation approach, which employs segmentation and classification methodologies, to detect recent snow avalanche deposits within VHR panchromatic optical remote sensing imagery. This produces avalanche deposit maps, which can be integrated with other spatial mapping and terrain data. The object-oriented approach has been tested and validated against manually generated maps in which avalanches are visually recognized and digitized. The accuracy (both users and producers) are over 0.9 with errors of commission less than 0.05. Future research is directed to widespread testing of the algorithm on data generated by various sensors and improvement of the algorithm in high noise regions as well as the mapping of avalanche paths alongside their deposits.

Sexual orientation and gender identity: review of concepts, controversies and their relation to psychopathology classification systems

PubMed Central

Moleiro, Carla; Pinto, Nuno

2015-01-01

Numerous controversies and debates have taken place throughout the history of psychopathology (and its main classification systems) with regards to sexual orientation and gender identity. These are still reflected on present reformulations of gender dysphoria in both the Diagnostic and Statistical Manual and the International Classification of Diseases, and in more or less subtle micro-aggressions experienced by lesbian, gay, bisexual and trans patients in mental health care. The present paper critically reviews this history and current controversies. It reveals that this deeply complex field contributes (i) to the reflection on the very concept of mental illness; (ii) to the focus on subjective distress and person-centered experience of psychopathology; and (iii) to the recognition of stigma and discrimination as significant intervening variables. Finally, it argues that sexual orientation and gender identity have been viewed, in the history of the field of psychopathology, between two poles: gender transgression and gender variance/fluidity. PMID:26483748

Sexual orientation and gender identity: review of concepts, controversies and their relation to psychopathology classification systems.

PubMed

Moleiro, Carla; Pinto, Nuno

2015-01-01

Numerous controversies and debates have taken place throughout the history of psychopathology (and its main classification systems) with regards to sexual orientation and gender identity. These are still reflected on present reformulations of gender dysphoria in both the Diagnostic and Statistical Manual and the International Classification of Diseases, and in more or less subtle micro-aggressions experienced by lesbian, gay, bisexual and trans patients in mental health care. The present paper critically reviews this history and current controversies. It reveals that this deeply complex field contributes (i) to the reflection on the very concept of mental illness; (ii) to the focus on subjective distress and person-centered experience of psychopathology; and (iii) to the recognition of stigma and discrimination as significant intervening variables. Finally, it argues that sexual orientation and gender identity have been viewed, in the history of the field of psychopathology, between two poles: gender transgression and gender variance/fluidity.

Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation

PubMed Central

Qian, Xiaohua; Tan, Hua; Zhang, Jian; Zhao, Weilin; Chan, Michael D.; Zhou, Xiaobo

2016-01-01

Purpose: Pseudoprogression (PsP) can mimic true tumor progression (TTP) on magnetic resonance imaging in patients with glioblastoma multiform (GBM). The phenotypical similarity between PsP and TTP makes it a challenging task for physicians to distinguish these entities. So far, no approved biomarkers or computer-aided diagnosis systems have been used clinically for this purpose. Methods: To address this challenge, the authors developed an objective classification system for PsP and TTP based on longitudinal diffusion tensor imaging. A novel spatio-temporal discriminative dictionary learning scheme was proposed to differentiate PsP and TTP, thereby avoiding segmentation of the region of interest. The authors constructed a novel discriminative sparse matrix with the classification-oriented dictionary learning approach by excluding the shared features of two categories, so that the pooled features captured the subtle difference between PsP and TTP. The most discriminating features were then identified from the pooled features by their feature scoring system. Finally, the authors stratified patients with GBM into PsP and TTP by a support vector machine approach. Tenfold cross-validation (CV) and the area under the receiver operating characteristic (AUC) were used to assess the robustness of the developed system. Results: The average accuracy and AUC values after ten rounds of tenfold CV were 0.867 and 0.92, respectively. The authors also assessed the effects of different methods and factors (such as data types, pooling techniques, and dimensionality reduction approaches) on the performance of their classification system which obtained the best performance. Conclusions: The proposed objective classification system without segmentation achieved a desirable and reliable performance in differentiating PsP from TTP. Thus, the developed approach is expected to advance the clinical research and diagnosis of PsP and TTP. PMID:27806598

Dictionary learning-based CT detection of pulmonary nodules

NASA Astrophysics Data System (ADS)

Wu, Panpan; Xia, Kewen; Zhang, Yanbo; Qian, Xiaohua; Wang, Ge; Yu, Hengyong

2016-10-01

Segmentation of lung features is one of the most important steps for computer-aided detection (CAD) of pulmonary nodules with computed tomography (CT). However, irregular shapes, complicated anatomical background and poor pulmonary nodule contrast make CAD a very challenging problem. Here, we propose a novel scheme for feature extraction and classification of pulmonary nodules through dictionary learning from training CT images, which does not require accurately segmented pulmonary nodules. Specifically, two classification-oriented dictionaries and one background dictionary are learnt to solve a two-category problem. In terms of the classification-oriented dictionaries, we calculate sparse coefficient matrices to extract intrinsic features for pulmonary nodule classification. The support vector machine (SVM) classifier is then designed to optimize the performance. Our proposed methodology is evaluated with the lung image database consortium and image database resource initiative (LIDC-IDRI) database, and the results demonstrate that the proposed strategy is promising.

Determining the orientation of depth-rotated familiar objects.

PubMed

Niimi, Ryosuke; Yokosawa, Kazuhiko

2008-02-01

How does the human visual system determine the depth-orientation of familiar objects? We examined reaction times and errors in the detection of 15 degrees differences in the depth orientations of two simultaneously presented familiar objects, which were the same objects (Experiment 1) or different objects (Experiment 2). Detection of orientation differences was best for 0 degrees (front) and 180 degrees (back), while 45 degrees and 135 degrees yielded poorer results, and 90 degrees (side) showed intermediate results, suggesting that the visual system is tuned for front, side and back orientations. We further found that those advantages are due to orientation-specific features such as horizontal linear contours and symmetry, since the 90 degrees advantage was absent for objects with curvilinear contours, and asymmetric object diminished the 0 degrees and 180 degrees advantages. We conclude that the efficiency of visually determining object orientation is highly orientation-dependent, and object orientation may be perceived in favor of front-back axes.

Patterns of Alcohol Use and Consequences Among Empirically Derived Sexual Minority Subgroups

PubMed Central

Talley, Amelia E.; Sher, Kenneth J.; Steinley, Douglas; Wood, Phillip K.; Littlefield, Andrew K.

2012-01-01

Objective: The current study develops an empirically determined classification of sexual orientation developmental patterns based on participants’ annual reports of self-identifications, sexual attractions, and sexual behaviors during the first 4 years of college. A secondary aim of the current work was to examine trajectories of alcohol involvement among identified subgroups. Method: Data were drawn from a subsample of a longitudinal study of incoming first-time college students at a large, public university (n = 2,068). Longitudinal latent class analysis was used to classify sexual minority participants into empirically derived subgroups based on three self-reported facets of sexual orientation. Multivariate repeated-measures analyses were conducted to examine how trajectories of alcohol involvement varied by sexual orientation class membership. Results: Four unique subclasses of sexual orientation developmental patterns were identified for males and females: one consistently exclusively heterosexual group and three sexual minority groups. Despite generally similar alcohol use patterns among subclasses, certain sexual minority subgroups reported elevated levels of alcohol-related negative consequences and maladaptive motivations for use throughout college compared with their exclusively heterosexual counterparts. Conclusions: Elevations in coping and conformity motivations for alcohol use were seen among those subgroups that also evidenced heightened negative alcohol-related consequences. Implications and limitations of the current work are discussed. PMID:22333337

Non-Trivial Feature Derivation for Intensifying Feature Detection Using LIDAR Datasets Through Allometric Aggregation Data Analysis Applying Diffused Hierarchical Clustering for Discriminating Agricultural Land Cover in Portions of Northern Mindanao, Philippines

NASA Astrophysics Data System (ADS)

Villar, Ricardo G.; Pelayo, Jigg L.; Mozo, Ray Mari N.; Salig, James B., Jr.; Bantugan, Jojemar

2016-06-01

Leaning on the derived results conducted by Central Mindanao University Phil-LiDAR 2.B.11 Image Processing Component, the paper attempts to provides the application of the Light Detection and Ranging (LiDAR) derived products in arriving quality Landcover classification considering the theoretical approach of data analysis principles to minimize the common problems in image classification. These are misclassification of objects and the non-distinguishable interpretation of pixelated features that results to confusion of class objects due to their closely-related spectral resemblance, unbalance saturation of RGB information is a challenged at the same time. Only low density LiDAR point cloud data is exploited in the research denotes as 2 pts/m2 of accuracy which bring forth essential derived information such as textures and matrices (number of returns, intensity textures, nDSM, etc.) in the intention of pursuing the conditions for selection characteristic. A novel approach that takes gain of the idea of object-based image analysis and the principle of allometric relation of two or more observables which are aggregated for each acquisition of datasets for establishing a proportionality function for data-partioning. In separating two or more data sets in distinct regions in a feature space of distributions, non-trivial computations for fitting distribution were employed to formulate the ideal hyperplane. Achieving the distribution computations, allometric relations were evaluated and match with the necessary rotation, scaling and transformation techniques to find applicable border conditions. Thus, a customized hybrid feature was developed and embedded in every object class feature to be used as classifier with employed hierarchical clustering strategy for cross-examining and filtering features. This features are boost using machine learning algorithms as trainable sets of information for a more competent feature detection. The product classification in this investigation was compared to a classification based on conventional object-oriented approach promoting straight-forward functionalities of the software eCognition. A compelling rise of efficiency in the overall accuracy (74.4% to 93.4%) and kappa index of agreement (70.5% to 91.7%) is noticeable based on the initial process. Nevertheless, having low-dense LiDAR dataset could be enough in generating exponential increase of performance in accuracy.

The search for structure - Object classification in large data sets. [for astronomers

NASA Technical Reports Server (NTRS)

Kurtz, Michael J.

1988-01-01

Research concerning object classifications schemes are reviewed, focusing on large data sets. Classification techniques are discussed, including syntactic, decision theoretic methods, fuzzy techniques, and stochastic and fuzzy grammars. Consideration is given to the automation of MK classification (Morgan and Keenan, 1973) and other problems associated with the classification of spectra. In addition, the classification of galaxies is examined, including the problems of systematic errors, blended objects, galaxy types, and galaxy clusters.

Semantic Relations for Problem-Oriented Medical Records

PubMed Central

Uzuner, Ozlem; Mailoa, Jonathan; Ryan, Russell; Sibanda, Tawanda

2010-01-01

Summary Objective We describe semantic relation (SR) classification on medical discharge summaries. We focus on relations targeted to the creation of problem-oriented records. Thus, we define relations that involve the medical problems of patients. Methods and Materials We represent patients’ medical problems with their diseases and symptoms. We study the relations of patients’ problems with each other and with concepts that are identified as tests and treatments. We present an SR classifier that studies a corpus of patient records one sentence at a time. For all pairs of concepts that appear in a sentence, this SR classifier determines the relations between them. In doing so, the SR classifier takes advantage of surface, lexical, and syntactic features and uses these features as input to a support vector machine. We apply our SR classifier to two sets of medical discharge summaries, one obtained from the Beth Israel-Deaconess Medical Center (BIDMC), Boston, MA and the other from Partners Healthcare, Boston, MA. Results On the BIDMC corpus, our SR classifier achieves micro-averaged F-measures that range from 74% to 95% on the various relation types. On the Partners corpus, the micro-averaged F-measures on the various relation types range from 68% to 91%. Our experiments show that lexical features (in particular, tokens that occur between candidate concepts, which we refer to as inter-concept tokens) are very informative for relation classification in medical discharge summaries. Using only the inter-concept tokens in the corpus, our SR classifier can recognize 84% of the relations in the BIDMC corpus and 72% of the relations in the Partners corpus. Conclusion These results are promising for semantic indexing of medical records. They imply that we can take advantage of lexical patterns in discharge summaries for relation classification at a sentence level. PMID:20646918

High-resolution land cover classification using low resolution global data

NASA Astrophysics Data System (ADS)

Carlotto, Mark J.

2013-05-01

A fusion approach is described that combines texture features from high-resolution panchromatic imagery with land cover statistics derived from co-registered low-resolution global databases to obtain high-resolution land cover maps. The method does not require training data or any human intervention. We use an MxN Gabor filter bank consisting of M=16 oriented bandpass filters (0-180°) at N resolutions (3-24 meters/pixel). The size range of these spatial filters is consistent with the typical scale of manmade objects and patterns of cultural activity in imagery. Clustering reduces the complexity of the data by combining pixels that have similar texture into clusters (regions). Texture classification assigns a vector of class likelihoods to each cluster based on its textural properties. Classification is unsupervised and accomplished using a bank of texture anomaly detectors. Class likelihoods are modulated by land cover statistics derived from lower resolution global data over the scene. Preliminary results from a number of Quickbird scenes show our approach is able to classify general land cover features such as roads, built up area, forests, open areas, and bodies of water over a wide range of scenes.

Regional yield predictions of malting barley by remote sensing and ancillary data

NASA Astrophysics Data System (ADS)

Weissteiner, Christof J.; Braun, Matthias; Kuehbauch, Walter

2004-02-01

Yield forecasts are of high interest to the malting and brewing industry in order to allow the most convenient purchasing policy of raw materials. Within this investigation, malting barley yield forecasts (Hordeum vulgare L.) were performed for typical growing regions in South-Western Germany. Multisensoral and multitemporal Remote Sensing data on one hand and ancillary meteorological, agrostatistical, topographical and pedological data on the other hand were used as input data for prediction models, which were based on an empirical-statistical modeling approach. Since spring barley production is depending on acreage and on the yield per area, classification is needed, which was performed by a supervised multitemporal classification algorithm, utilizing optical Remote Sensing data (LANDSAT TM/ETM+). Comparison between a pixel-based and an object-oriented classification algorithm was carried out. The basic version of the yield estimation model was conducted by means of linear correlation of Remote Sensing data (NOAA-AVHRR NDVI), CORINE land cover data and agrostatistical data. In an extended version meteorological data (temperature, precipitation, etc.) and soil data was incorporated. Both, basic and extended prediction systems, led to feasible results, depending on the selection of the time span for NDVI accumulation.

General object-oriented software development

NASA Technical Reports Server (NTRS)

Seidewitz, Edwin V.; Stark, Mike

1986-01-01

Object-oriented design techniques are gaining increasing popularity for use with the Ada programming language. A general approach to object-oriented design which synthesizes the principles of previous object-oriented methods into the overall software life-cycle, providing transitions from specification to design and from design to code. It therefore provides the basis for a general object-oriented development methodology.

14 CFR Section 6 - Objective Classification of Balance Sheet Elements

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Objective Classification of Balance Sheet... AIR CARRIERS Balance Sheet Classifications Section 6 Objective Classification of Balance Sheet...) Record here all general and working funds available on demand as of the date of the balance sheet which...

14 CFR Section 6 - Objective Classification of Balance Sheet Elements

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 4 2011-01-01 2011-01-01 false Objective Classification of Balance Sheet... AIR CARRIERS Balance Sheet Classifications Section 6 Objective Classification of Balance Sheet...) Record here all general and working funds available on demand as of the date of the balance sheet which...

Proposed declassification of disease categories related to sexual orientation in the International Statistical Classification of Diseases and Related Health Problems (ICD-11).

PubMed

Cochran, Susan D; Drescher, Jack; Kismödi, Eszter; Giami, Alain; García-Moreno, Claudia; Atalla, Elham; Marais, Adele; Vieira, Elisabeth Meloni; Reed, Geoffrey M

2014-09-01

The World Health Organization is developing the 11th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-11), planned for publication in 2017. The Working Group on the Classification of Sexual Disorders and Sexual Health was charged with reviewing and making recommendations on disease categories related to sexuality in the chapter on mental and behavioural disorders in the 10th revision (ICD-10), published in 1990. This chapter includes categories for diagnoses based primarily on sexual orientation even though ICD-10 states that sexual orientation alone is not a disorder. This article reviews the scientific evidence and clinical rationale for continuing to include these categories in the ICD. A review of the evidence published since 1990 found little scientific interest in these categories. In addition, the Working Group found no evidence that they are clinically useful: they neither contribute to health service delivery or treatment selection nor provide essential information for public health surveillance. Moreover, use of these categories may create unnecessary harm by delaying accurate diagnosis and treatment. The Working Group recommends that these categories be deleted entirely from ICD-11. Health concerns related to sexual orientation can be better addressed using other ICD categories.

Proposed declassification of disease categories related to sexual orientation in the International Statistical Classification of Diseases and Related Health Problems (ICD-11)

PubMed Central

Drescher, Jack; Kismödi, Eszter; Giami, Alain; García-Moreno, Claudia; Atalla, Elham; Marais, Adele; Vieira, Elisabeth Meloni; Reed, Geoffrey M

2014-01-01

Abstract The World Health Organization is developing the 11th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-11), planned for publication in 2017. The Working Group on the Classification of Sexual Disorders and Sexual Health was charged with reviewing and making recommendations on disease categories related to sexuality in the chapter on mental and behavioural disorders in the 10th revision (ICD-10), published in 1990. This chapter includes categories for diagnoses based primarily on sexual orientation even though ICD-10 states that sexual orientation alone is not a disorder. This article reviews the scientific evidence and clinical rationale for continuing to include these categories in the ICD. A review of the evidence published since 1990 found little scientific interest in these categories. In addition, the Working Group found no evidence that they are clinically useful: they neither contribute to health service delivery or treatment selection nor provide essential information for public health surveillance. Moreover, use of these categories may create unnecessary harm by delaying accurate diagnosis and treatment. The Working Group recommends that these categories be deleted entirely from ICD-11. Health concerns related to sexual orientation can be better addressed using other ICD categories. PMID:25378758

Visual Search for Object Orientation Can Be Modulated by Canonical Orientation

ERIC Educational Resources Information Center

Ballaz, Cecile; Boutsen, Luc; Peyrin, Carole; Humphreys, Glyn W.; Marendaz, Christian

2005-01-01

The authors studied the influence of canonical orientation on visual search for object orientation. Displays consisted of pictures of animals whose axis of elongation was either vertical or tilted in their canonical orientation. Target orientation could be either congruent or incongruent with the object's canonical orientation. In Experiment 1,…

Fully Convolutional Network Based Shadow Extraction from GF-2 Imagery

NASA Astrophysics Data System (ADS)

Li, Z.; Cai, G.; Ren, H.

2018-04-01

There are many shadows on the high spatial resolution satellite images, especially in the urban areas. Although shadows on imagery severely affect the information extraction of land cover or land use, they provide auxiliary information for building extraction which is hard to achieve a satisfactory accuracy through image classification itself. This paper focused on the method of building shadow extraction by designing a fully convolutional network and training samples collected from GF-2 satellite imagery in the urban region of Changchun city. By means of spatial filtering and calculation of adjacent relationship along the sunlight direction, the small patches from vegetation or bridges have been eliminated from the preliminary extracted shadows. Finally, the building shadows were separated. The extracted building shadow information from the proposed method in this paper was compared with the results from the traditional object-oriented supervised classification algorihtms. It showed that the deep learning network approach can improve the accuracy to a large extent.

Security aspects of space operations data

NASA Technical Reports Server (NTRS)

Schmitz, Stefan

1993-01-01

This paper deals with data security. It identifies security threats to European Space Agency's (ESA) In Orbit Infrastructure Ground Segment (IOI GS) and proposes a method of dealing with its complex data structures from the security point of view. It is part of the 'Analysis of Failure Modes, Effects Hazards and Risks of the IOI GS for Operations, including Backup Facilities and Functions' carried out on behalf of the European Space Operations Center (ESOC). The security part of this analysis has been prepared with the following aspects in mind: ESA's large decentralized ground facilities for operations, the multiple organizations/users involved in the operations and the developments of ground data systems, and the large heterogeneous network structure enabling access to (sensitive) data which does involve crossing organizational boundaries. An IOI GS data objects classification is introduced to determine the extent of the necessary protection mechanisms. The proposal of security countermeasures is oriented towards the European 'Information Technology Security Evaluation Criteria (ITSEC)' whose hierarchically organized requirements can be directly mapped to the security sensitivity classification.

Plant Classification from Bat-Like Echolocation Signals

PubMed Central

Yovel, Yossi; Franz, Matthias Otto; Stilz, Peter; Schnitzler, Hans-Ulrich

2008-01-01

Classification of plants according to their echoes is an elementary component of bat behavior that plays an important role in spatial orientation and food acquisition. Vegetation echoes are, however, highly complex stochastic signals: from an acoustical point of view, a plant can be thought of as a three-dimensional array of leaves reflecting the emitted bat call. The received echo is therefore a superposition of many reflections. In this work we suggest that the classification of these echoes might not be such a troublesome routine for bats as formerly thought. We present a rather simple approach to classifying signals from a large database of plant echoes that were created by ensonifying plants with a frequency-modulated bat-like ultrasonic pulse. Our algorithm uses the spectrogram of a single echo from which it only uses features that are undoubtedly accessible to bats. We used a standard machine learning algorithm (SVM) to automatically extract suitable linear combinations of time and frequency cues from the spectrograms such that classification with high accuracy is enabled. This demonstrates that ultrasonic echoes are highly informative about the species membership of an ensonified plant, and that this information can be extracted with rather simple, biologically plausible analysis. Thus, our findings provide a new explanatory basis for the poorly understood observed abilities of bats in classifying vegetation and other complex objects. PMID:18369425

[Automation in surgery: a systematical approach].

PubMed

Strauss, G; Meixensberger, J; Dietz, A; Manzey, D

2007-04-01

Surgical assistance systems permit a misalignment from the purely manual to an assisted activity of the surgeon (automation). Automation defines a system, that partly or totally fulfils function, those was carried out before totally or partly by the user. The organization of surgical assistance systems following application (planning, simulation, intraoperative navigation and visualization) or technical configuration of the system (manipulator, robot) is not suitable for a description of the interaction between user (surgeon) and the system. The available work has the goal of providing a classification for the degree of the automation of surgical interventions and describing by examples. The presented classification orients itself at pre-working from the Human-Factors-Sciences. As a condition for an automation of a surgical intervention applies that an assumption of a task, which was alone assigned so far to the surgeon takes place via the system. For both reference objects (humans and machine) the condition passively or actively comes into consideration. Besides can be classified according to which functions are taken over during a selected function division by humans and/or the surgical assistance system. Three functional areas were differentiated: "information acquisition and -analysis", "decision making and action planning" as well as "execution of the surgical action". From this results a classification of pre- and intraoperative surgical assist systems in six categories, which represent different automation degrees. The classification pattern is described and illustrated on the basis of surgical of examples.

In search of a consumer-focused food classification system. An experimental heuristic approach to differentiate degrees of quality.

PubMed

Torres-Ruiz, Francisco J; Marano-Marcolini, Carla; Lopez-Zafra, Esther

2018-06-01

The present paper focuses on the problems that arise in food classification systems (FCSs), especially when the food product type has different levels or grades of quality. Despite the principal function of these systems being to assist the consumer (to inform, clarify and facilitate choice and purchase), they frequently have the opposite effect. Thus, the main aim of the present research involves providing orientations for the design of effective food classification systems. To address this objective, considering the context of food product consumption (related to heuristic processing), we conducted an experimental study with 720 participants. We analysed the usefulness of heuristic elements by a factorial 2 (category length: short and long) × 3 (visual signs: colours, numbers and images) design in relation to recall and recognition activities. The results showed that the elements used to make the classification more effective for consumers vary depending on whether the user seeks to prioritize the recall or the recognition of product categories. Thus, long categories with images significantly improve recognition, and short categories with colours improve recall. A series of recommendations are provided that can help to enhance FCSs and to make them more intuitive and easier to understand for consumers. Implications with regard to theory and practice are discussed. Copyright © 2018 Elsevier Ltd. All rights reserved.

Rotationally invariant clustering of diffusion MRI data using spherical harmonics

NASA Astrophysics Data System (ADS)

Liptrot, Matthew; Lauze, François

2016-03-01

We present a simple approach to the voxelwise classification of brain tissue acquired with diffusion weighted MRI (DWI). The approach leverages the power of spherical harmonics to summarise the diffusion information, sampled at many points over a sphere, using only a handful of coefficients. We use simple features that are invariant to the rotation of the highly orientational diffusion data. This provides a way to directly classify voxels whose diffusion characteristics are similar yet whose primary diffusion orientations differ. Subsequent application of machine-learning to the spherical harmonic coefficients therefore may permit classification of DWI voxels according to their inferred underlying fibre properties, whilst ignoring the specifics of orientation. After smoothing apparent diffusion coefficients volumes, we apply a spherical harmonic transform, which models the multi-directional diffusion data as a collection of spherical basis functions. We use the derived coefficients as voxelwise feature vectors for classification. Using a simple Gaussian mixture model, we examined the classification performance for a range of sub-classes (3-20). The results were compared against existing alternatives for tissue classification e.g. fractional anisotropy (FA) or the standard model used by Camino.1 The approach was implemented on both two publicly-available datasets: an ex-vivo pig brain and in-vivo human brain from the Human Connectome Project (HCP). We have demonstrated how a robust classification of DWI data can be performed without the need for a model reconstruction step. This avoids the potential confounds and uncertainty that such models may impose, and has the benefit of being computable directly from the DWI volumes. As such, the method could prove useful in subsequent pre-processing stages, such as model fitting, where it could inform about individual voxel complexities and improve model parameter choice.

Information mining in remote sensing imagery

NASA Astrophysics Data System (ADS)

Li, Jiang

The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. This dissertation proposes and implements an extensive remote sensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remote sensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem. Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized k-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A k-nearest neighbor search is performed using a query-by-example (QBE) approach. Furthermore, an automatic parametric contour tracing algorithm and an O(n) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty. Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and fuzzy normalized difference vegetation index (NDVI) pattern mining. The study results show the effectiveness of the proposed system prototype and the potentials for other applications in remote sensing.

Target-object integration, attention distribution, and object orientation interactively modulate object-based selection.

PubMed

Al-Janabi, Shahd; Greenberg, Adam S

2016-10-01

The representational basis of attentional selection can be object-based. Various studies have suggested, however, that object-based selection is less robust than spatial selection across experimental paradigms. We sought to examine the manner by which the following factors might explain this variation: Target-Object Integration (targets 'on' vs. part 'of' an object), Attention Distribution (narrow vs. wide), and Object Orientation (horizontal vs. vertical). In Experiment 1, participants discriminated between two targets presented 'on' an object in one session, or presented as a change 'of' an object in another session. There was no spatial cue-thus, attention was initially focused widely-and the objects were horizontal or vertical. We found evidence of object-based selection only when targets constituted a change 'of' an object. Additionally, object orientation modulated the sign of object-based selection: We observed a same-object advantage for horizontal objects, but a same-object cost for vertical objects. In Experiment 2, an informative cue preceded a single target presented 'on' an object or as a change 'of' an object (thus, attention was initially focused narrowly). Unlike in Experiment 1, we found evidence of object-based selection independent of target-object integration. We again found that the sign of selection was modulated by the objects' orientation. This result may reflect a meridian effect, which emerged due to anisotropies in the cortical representations when attention is oriented endogenously. Experiment 3 revealed that object orientation did not modulate object-based selection when attention was oriented exogenously. Our findings suggest that target-object integration, attention distribution, and object orientation modulate object-based selection, but only in combination.

Automatic Spatio-Temporal Flow Velocity Measurement in Small Rivers Using Thermal Image Sequences

NASA Astrophysics Data System (ADS)

Lin, D.; Eltner, A.; Sardemann, H.; Maas, H.-G.

2018-05-01

An automatic spatio-temporal flow velocity measurement approach, using an uncooled thermal camera, is proposed in this paper. The basic principle of the method is to track visible thermal features at the water surface in thermal camera image sequences. Radiometric and geometric calibrations are firstly implemented to remove vignetting effects in thermal imagery and to get the interior orientation parameters of the camera. An object-based unsupervised classification approach is then applied to detect the interest regions for data referencing and thermal feature tracking. Subsequently, GCPs are extracted to orient the river image sequences and local hot points are identified as tracking features. Afterwards, accurate dense tracking outputs are obtained using pyramidal Lucas-Kanade method. To validate the accuracy potential of the method, measurements obtained from thermal feature tracking are compared with reference measurements taken by a propeller gauge. Results show a great potential of automatic flow velocity measurement in small rivers using imagery from a thermal camera.

Activities identification for activity-based cost/management applications of the diagnostics outpatient procedures.

PubMed

Alrashdan, Abdalla; Momani, Amer; Ababneh, Tamador

2012-01-01

One of the most challenging problems facing healthcare providers is to determine the actual cost for their procedures, which is important for internal accounting and price justification to insurers. The objective of this paper is to find suitable categories to identify the diagnostic outpatient medical procedures and translate them from functional orientation to process orientation. A hierarchal task tree is developed based on a classification schema of procedural activities. Each procedure is seen as a process consisting of a number of activities. This makes a powerful foundation for activity-based cost/management implementation and provides enough information to discover the value-added and non-value-added activities that assist in process improvement and eventually may lead to cost reduction. Work measurement techniques are used to identify the standard time of each activity at the lowest level of the task tree. A real case study at a private hospital is presented to demonstrate the proposed methodology. © 2011 National Association for Healthcare Quality.

The effects of platform motion and target orientation on the performance of trackball manipulation.

PubMed

Yau, Yi-Jan; Chao, Chin-Jung; Feng, Wen-Yang; Hwang, Sheue-Ling

2011-08-01

The trackball has been widely employed as a control/command input device on moving vehicles, but few studies have explored the effects of platform motion on its manipulation. Fewer still have considered this issue in designing the user interface and the arrangement of console location and orientation simultaneously. This work describes an experiment carried out to investigate the performance of trackball users on a simple point-and-click task in a motion simulator. By varying the orientation of onscreen targets, the effect of cursor movement direction on performance is investigated. The results indicate that the platform motion and target orientation both significantly affect the time required to point and click, but not the accuracy of target selection. The movement times were considerably longer under rolling and pitching motions and for targets located along the diagonal axes of the interface. Subjective evaluations carried out by the participants agree with these objective results. These findings could be used to optimise console and graphical menu design for use on maritime vessels. STATEMENT OF RELEVANCE: In military situations, matters of life or death may be decided in milliseconds. Any delay or error in classification and identification will thus affect the safety of the ship and its crew. This study demonstrates that performance of manipulating a trackball is affected by the platform motion and target orientation. The results of the present study can guide the arrangement of consoles and the design of trackball-based graphical user interfaces on maritime vessels.

Noncontact orientation of objects in three-dimensional space using magnetic levitation

PubMed Central

Subramaniam, Anand Bala; Yang, Dian; Yu, Hai-Dong; Nemiroski, Alex; Tricard, Simon; Ellerbee, Audrey K.; Soh, Siowling; Whitesides, George M.

2014-01-01

This paper describes several noncontact methods of orienting objects in 3D space using Magnetic Levitation (MagLev). The methods use two permanent magnets arranged coaxially with like poles facing and a container containing a paramagnetic liquid in which the objects are suspended. Absent external forcing, objects levitating in the device adopt predictable static orientations; the orientation depends on the shape and distribution of mass within the objects. The orientation of objects of uniform density in the MagLev device shows a sharp geometry-dependent transition: an analytical theory rationalizes this transition and predicts the orientation of objects in the MagLev device. Manipulation of the orientation of the levitating objects in space is achieved in two ways: (i) by rotating and/or translating the MagLev device while the objects are suspended in the paramagnetic solution between the magnets; (ii) by moving a small external magnet close to the levitating objects while keeping the device stationary. Unlike mechanical agitation or robotic selection, orienting using MagLev is possible for objects having a range of different physical characteristics (e.g., different shapes, sizes, and mechanical properties from hard polymers to gels and fluids). MagLev thus has the potential to be useful for sorting and positioning components in 3D space, orienting objects for assembly, constructing noncontact devices, and assembling objects composed of soft materials such as hydrogels, elastomers, and jammed granular media. PMID:25157136

Noncontact orientation of objects in three-dimensional space using magnetic levitation.

PubMed

Subramaniam, Anand Bala; Yang, Dian; Yu, Hai-Dong; Nemiroski, Alex; Tricard, Simon; Ellerbee, Audrey K; Soh, Siowling; Whitesides, George M

2014-09-09

This paper describes several noncontact methods of orienting objects in 3D space using Magnetic Levitation (MagLev). The methods use two permanent magnets arranged coaxially with like poles facing and a container containing a paramagnetic liquid in which the objects are suspended. Absent external forcing, objects levitating in the device adopt predictable static orientations; the orientation depends on the shape and distribution of mass within the objects. The orientation of objects of uniform density in the MagLev device shows a sharp geometry-dependent transition: an analytical theory rationalizes this transition and predicts the orientation of objects in the MagLev device. Manipulation of the orientation of the levitating objects in space is achieved in two ways: (i) by rotating and/or translating the MagLev device while the objects are suspended in the paramagnetic solution between the magnets; (ii) by moving a small external magnet close to the levitating objects while keeping the device stationary. Unlike mechanical agitation or robotic selection, orienting using MagLev is possible for objects having a range of different physical characteristics (e.g., different shapes, sizes, and mechanical properties from hard polymers to gels and fluids). MagLev thus has the potential to be useful for sorting and positioning components in 3D space, orienting objects for assembly, constructing noncontact devices, and assembling objects composed of soft materials such as hydrogels, elastomers, and jammed granular media.

Sexual orientation in the 2013 national health interview survey: a quality assessment.

PubMed

Dahlhamer, James M; Galinsky, Adena M; Joestl, Sarah S; Ward, Brian W

2014-12-01

Objective-This report presents a set of quality analyses of sexual orientation data collected in the 2013 National Health Interview Survey (NHIS). NHIS sexual orientation estimates are compared with those from the National Survey of Family Growth (NSFG) and the National Health and Nutrition Examination Survey (NHANES). Selected health outcomes by sexual orientation are compared between NHIS and NSFG. Assessments of item nonresponse, item response times, and responses to follow-up questions to the sexual orientation question are also presented. Methods-NHIS is a multipurpose health survey conducted continuously throughout the year by the Centers for Disease Control and Prevention's National Center for Health Statistics. Analyses in this report were based on NHIS data collected in 2013 from 34,557 adults aged 18 and over. Sampling weights were used to produce national estimates that are representative of the civilian noninstitutionalized U.S. adult population. Data from the 2006-2010 NSFG and 2009-2012 NHANES were used for the comparisons. Results-Based on the 2013 NHIS data, 96.6% of adults identified as straight, 1.6% identified as gay/lesbian, and 0.7% identified as bisexual. The remaining 1.1% of adults identified as ''something else,'' stated ''I don't know the answer,'' or refused to answer. Responses to follow-up questions suggest that the sexual orientation question is producing little classification error. In addition, largely similar patterns of association between sexual orientation and health were observed for NHIS and NSFG. Analyses of item nonresponse rates revealed few data quality issues, although item response times suggest possible shortcutting of the question and comprehension problems for select respondents. All material appearing in this report is in the public domain and may be reproduced or copied without permission; citation as to source, however, is appreciated.

Land Cover Analysis by Using Pixel-Based and Object-Based Image Classification Method in Bogor

NASA Astrophysics Data System (ADS)

Amalisana, Birohmatin; Rokhmatullah; Hernina, Revi

2017-12-01

The advantage of image classification is to provide earth’s surface information like landcover and time-series changes. Nowadays, pixel-based image classification technique is commonly performed with variety of algorithm such as minimum distance, parallelepiped, maximum likelihood, mahalanobis distance. On the other hand, landcover classification can also be acquired by using object-based image classification technique. In addition, object-based classification uses image segmentation from parameter such as scale, form, colour, smoothness and compactness. This research is aimed to compare the result of landcover classification and its change detection between parallelepiped pixel-based and object-based classification method. Location of this research is Bogor with 20 years range of observation from 1996 until 2016. This region is famous as urban areas which continuously change due to its rapid development, so that time-series landcover information of this region will be interesting.

High-spatial resolution multispectral and panchromatic satellite imagery for mapping perennial desert plants

NASA Astrophysics Data System (ADS)

Alsharrah, Saad A.; Bruce, David A.; Bouabid, Rachid; Somenahalli, Sekhar; Corcoran, Paul A.

2015-10-01

The use of remote sensing techniques to extract vegetation cover information for the assessment and monitoring of land degradation in arid environments has gained increased interest in recent years. However, such a task can be challenging, especially for medium-spatial resolution satellite sensors, due to soil background effects and the distribution and structure of perennial desert vegetation. In this study, we utilised Pleiades high-spatial resolution, multispectral (2m) and panchromatic (0.5m) imagery and focused on mapping small shrubs and low-lying trees using three classification techniques: 1) vegetation indices (VI) threshold analysis, 2) pre-built object-oriented image analysis (OBIA), and 3) a developed vegetation shadow model (VSM). We evaluated the success of each approach using a root of the sum of the squares (RSS) metric, which incorporated field data as control and three error metrics relating to commission, omission, and percent cover. Results showed that optimum VI performers returned good vegetation cover estimates at certain thresholds, but failed to accurately map the distribution of the desert plants. Using the pre-built IMAGINE Objective OBIA approach, we improved the vegetation distribution mapping accuracy, but this came at the cost of over classification, similar to results of lowering VI thresholds. We further introduced the VSM which takes into account shadow for further refining vegetation cover classification derived from VI. The results showed significant improvements in vegetation cover and distribution accuracy compared to the other techniques. We argue that the VSM approach using high-spatial resolution imagery provides a more accurate representation of desert landscape vegetation and should be considered in assessments of desertification.

Training and Development, Division of Personnel and Labor Relations,

Science.gov Websites

Employee Training Exit Survey HR Forms New Employee Orientation For Admin Staff Classification Form Packets Office Classification EPIC EEO Program Labor Relations Payroll Services Recruitment Services Training and State Officer Compensation Commission Administration / Personnel and Labor Relations / Training &

14 CFR Section 16 - Objective Classification-Discontinued Operations

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Objective Classification-Discontinued Operations Section 16 Section 16 Aeronautics and Space OFFICE OF THE SECRETARY, DEPARTMENT OF TRANSPORTATION... AIR CARRIERS Profit and Loss Classification Section 16 Objective Classification—Discontinued...

The Role of Graphic Orientations in Children's Drawings of Familiar and Novel Objects at Rest and in Motion.

ERIC Educational Resources Information Center

Ives, William; Rovet, Joanne

1979-01-01

Reports three experiments which investigate: whether familiar objects have standard graphic orientations (Experiment 1); the relationship between use of object orientations and more conventional methods in depicting familiar objects in motion (Experiment 2); and whether orientations are used differently in novel objects whose only defining feature…

Radial Artery Coursing Behind the Biceps Brachii Tendon: Significance for the Transradial Catheterization and a Clinically Oriented Classification of the Radial Artery Variations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jelev, L., E-mail: ljelev@abv.bg; Surchev, L.

2008-09-15

In routine clinical practice the variations of the radial artery are the main reason for technical failure during transradial catheterization. If these variations are well documented, however, they do not represent a problem in the transradial approach. Therefore, we report here a rare case of the radial artery which is very strange but potentially valuable for the clinical practice: it arises at a right angle from the brachial artery and passes behind the biceps brachii tendon. Based on our findings and on an extensive literature review, we propose for the first time a clinically oriented classification of the variations ofmore » the radial artery. This classification is related to the catheterization success at the usual access site of the radial artery at the wrist.« less

a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

He, H.; Khoshelham, K.; Fraser, C.

2017-09-01

Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

An object-oriented description method of EPMM process

NASA Astrophysics Data System (ADS)

Jiang, Zuo; Yang, Fan

2017-06-01

In order to use the object-oriented mature tools and language in software process model, make the software process model more accord with the industrial standard, it’s necessary to study the object-oriented modelling of software process. Based on the formal process definition in EPMM, considering the characteristics that Petri net is mainly formal modelling tool and combining the Petri net modelling with the object-oriented modelling idea, this paper provides this implementation method to convert EPMM based on Petri net into object models based on object-oriented description.

Urban Land Cover/use Change Detection Using High Resolution SPOT 5 and SPOT 6 Images and Urban Atlas Nomenclature

NASA Astrophysics Data System (ADS)

Akay, S. S.; Sertel, E.

2016-06-01

Urban land cover/use changes like urbanization and urban sprawl have been impacting the urban ecosystems significantly therefore determination of urban land cover/use changes is an important task to understand trends and status of urban ecosystems, to support urban planning and to aid decision-making for urban-based projects. High resolution satellite images could be used to accurately, periodically and quickly map urban land cover/use and their changes by time. This paper aims to determine urban land cover/use changes in Gaziantep city centre between 2010 and 2105 using object based images analysis and high resolution SPOT 5 and SPOT 6 images. 2.5 m SPOT 5 image obtained in 5th of June 2010 and 1.5 m SPOT 6 image obtained in 7th of July 2015 were used in this research to precisely determine land changes in five-year period. In addition to satellite images, various ancillary data namely Normalized Difference Vegetation Index (NDVI), Difference Water Index (NDWI) maps, cadastral maps, OpenStreetMaps, road maps and Land Cover maps, were integrated into the classification process to produce high accuracy urban land cover/use maps for these two years. Both images were geometrically corrected to fulfil the 1/10,000 scale geometric accuracy. Decision tree based object oriented classification was applied to identify twenty different urban land cover/use classes defined in European Urban Atlas project. Not only satellite images and satellite image-derived indices but also different thematic maps were integrated into decision tree analysis to create rule sets for accurate mapping of each class. Rule sets of each satellite image for the object based classification involves spectral, spatial and geometric parameter to automatically produce urban map of the city centre region. Total area of each class per related year and their changes in five-year period were determined and change trend in terms of class transformation were presented. Classification accuracy assessment was conducted by creating a confusion matrix to illustrate the thematic accuracy of each class.

Audible sonar images generated with proprioception for target analysis.

PubMed

Kuc, Roman B

2017-05-01

Some blind humans have demonstrated the ability to detect and classify objects with echolocation using palatal clicks. An audible-sonar robot mimics human click emissions, binaural hearing, and head movements to extract interaural time and level differences from target echoes. Targets of various complexity are examined by transverse displacements of the sonar and by target pose rotations that model movements performed by the blind. Controlled sonar movements executed by the robot provide data that model proprioception information available to blind humans for examining targets from various aspects. The audible sonar uses this sonar location and orientation information to form two-dimensional target images that are similar to medical diagnostic ultrasound tomograms. Simple targets, such as single round and square posts, produce distinguishable and recognizable images. More complex targets configured with several simple objects generate diffraction effects and multiple reflections that produce image artifacts. The presentation illustrates the capabilities and limitations of target classification from audible sonar images.

14 CFR Section 17 - Objective Classification-Extraordinary Items

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Objective Classification-Extraordinary Items Section 17 Section 17 Aeronautics and Space OFFICE OF THE SECRETARY, DEPARTMENT OF TRANSPORTATION... AIR CARRIERS Profit and Loss Classification Section 17 Objective Classification—Extraordinary Items...

Parietal and frontal object areas underlie perception of object orientation in depth.

PubMed

Niimi, Ryosuke; Saneyoshi, Ayako; Abe, Reiko; Kaminaga, Tatsuro; Yokosawa, Kazuhiko

2011-05-27

Recent studies have shown that the human parietal and frontal cortices are involved in object image perception. We hypothesized that the parietal/frontal object areas play a role in differentiating the orientations (i.e., views) of an object. By using functional magnetic resonance imaging, we compared brain activations while human observers differentiated between two object images in depth-orientation (orientation task) and activations while they differentiated the images in object identity (identity task). The left intraparietal area, right angular gyrus, and right inferior frontal areas were activated more for the orientation task than for the identity task. The occipitotemporal object areas, however, were activated equally for the two tasks. No region showed greater activation for the identity task. These results suggested that the parietal/frontal object areas encode view-dependent visual features and underlie object orientation perception. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Object-Oriented Image Clustering Method Using UAS Photogrammetric Imagery

NASA Astrophysics Data System (ADS)

Lin, Y.; Larson, A.; Schultz-Fellenz, E. S.; Sussman, A. J.; Swanson, E.; Coppersmith, R.

2016-12-01

Unmanned Aerial Systems (UAS) have been used widely as an imaging modality to obtain remotely sensed multi-band surface imagery, and are growing in popularity due to their efficiency, ease of use, and affordability. Los Alamos National Laboratory (LANL) has employed the use of UAS for geologic site characterization and change detection studies at a variety of field sites. The deployed UAS equipped with a standard visible band camera to collect imagery datasets. Based on the imagery collected, we use deep sparse algorithmic processing to detect and discriminate subtle topographic features created or impacted by subsurface activities. In this work, we develop an object-oriented remote sensing imagery clustering method for land cover classification. To improve the clustering and segmentation accuracy, instead of using conventional pixel-based clustering methods, we integrate the spatial information from neighboring regions to create super-pixels to avoid salt-and-pepper noise and subsequent over-segmentation. To further improve robustness of our clustering method, we also incorporate a custom digital elevation model (DEM) dataset generated using a structure-from-motion (SfM) algorithm together with the red, green, and blue (RGB) band data for clustering. In particular, we first employ an agglomerative clustering to create an initial segmentation map, from where every object is treated as a single (new) pixel. Based on the new pixels obtained, we generate new features to implement another level of clustering. We employ our clustering method to the RGB+DEM datasets collected at the field site. Through binary clustering and multi-object clustering tests, we verify that our method can accurately separate vegetation from non-vegetation regions, and are also able to differentiate object features on the surface.

Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation

NASA Astrophysics Data System (ADS)

Chen, Y.; Luo, M.; Xu, L.; Zhou, X.; Ren, J.; Zhou, J.

2018-04-01

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

[Stage-adjusted treatment for haemorrhoidal disease].

PubMed

Herold, A

2008-05-01

Haemorrhoidal disease is one of the most frequent disorders in western countries. The aim of individual therapy is eradication of symptoms achieved by normalisation of anatomy and physiology. Treatment is orientated to the stage of the disease: First-degree haemorrhoids are treated conservatively. In addition to high fibre diet, sclerotherapy is used. Haemorrhoids of the 2nd degree prolapse during defecation and return spontaneously. First-line treatment is rubber band ligation. Third-degree haemorrhoids that prolapse during defecation have to be digitally reduced. The majority of these patients need surgery. For segmental disorders haemorrhoidectomy according to Milligan-Morgan or Ferguson is recommended. In circular disease Stapler haemorrhoidopexy is now the procedure of choice. Using a classification orientated therapeutical regime orientated to the classification of haemorrhoidal disease offers high healing rates with a low rate of complications and recurrences.

Research on enhancing the utilization of digital multispectral data and geographic information systems in global habitability studies

NASA Technical Reports Server (NTRS)

Martinko, Edward A.; Merchant, James W.

1988-01-01

During 1986 to 1987, the Kansas Applied Remote Sensing (KARS) Program continued to build upon long-term research efforts oriented towards enhancement and development of technologies for using remote sensing in the inventory and evaluation of land use and renewable resources (both natural and agricultural). These research efforts directly addressed needs and objectives of NASA's Land-Related Global Habitability Program as well as needs of and interests of public agencies and private firms. The KARS Program placed particular emphasis on two major areas: development of intelligent algorithms to improve automated classification of digital multispectral data; and integrating and merging digital multispectral data with ancillary data in spatial modes.

The nature of an object-oriented program: How do practitioners understand the nature of what they are creating?

NASA Astrophysics Data System (ADS)

Thompson, Errol; Kinshuk

2011-09-01

Object-oriented programming is seen as a difficult skill to master. There is considerable debate about the most appropriate way to introduce novice programmers to object-oriented concepts. Is it possible to uncover what the critical aspects or features are that enhance the learning of object-oriented programming? Practitioners have differing understandings of the nature of an object-oriented program. Uncovering these different ways of understanding leads to agreater understanding of the critical aspects and their relationship tothe structure of the program produced. A phenomenographic studywas conducted to uncover practitioner understandings of the nature of an object-oriented program. The study identified five levels of understanding and three dimensions of variation within these levels. These levels and dimensions of variation provide a framework for fostering conceptual change with respect to the nature of an object-oriented program.

Object-Oriented Programming in High Schools the Turing Way.

ERIC Educational Resources Information Center

Holt, Richard C.

This paper proposes an approach to introducing object-oriented concepts to high school computer science students using the Object-Oriented Turing (OOT) language. Students can learn about basic object-oriented (OO) principles such as classes and inheritance by using and expanding a collection of classes that draw pictures like circles and happy…

Imaging, object detection, and change detection with a polarized multistatic GPR array

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beer, N. Reginald; Paglieroni, David W.

A polarized detection system performs imaging, object detection, and change detection factoring in the orientation of an object relative to the orientation of transceivers. The polarized detection system may operate on one of several modes of operation based on whether the imaging, object detection, or change detection is performed separately for each transceiver orientation. In combined change mode, the polarized detection system performs imaging, object detection, and change detection separately for each transceiver orientation, and then combines changes across polarizations. In combined object mode, the polarized detection system performs imaging and object detection separately for each transceiver orientation, and thenmore » combines objects across polarizations and performs change detection on the result. In combined image mode, the polarized detection system performs imaging separately for each transceiver orientation, and then combines images across polarizations and performs object detection followed by change detection on the result.« less

Differentiation chronic post traumatic stress disorder patients from healthy subjects using objective and subjective sleep-related parameters.

PubMed

Tahmasian, Masoud; Jamalabadi, Hamidreza; Abedini, Mina; Ghadami, Mohammad R; Sepehry, Amir A; Knight, David C; Khazaie, Habibolah

2017-05-22

Sleep disturbance is common in chronic post-traumatic stress disorder (PTSD). However, prior work has demonstrated that there are inconsistencies between subjective and objective assessments of sleep disturbance in PTSD. Therefore, we investigated whether subjective or objective sleep assessment has greater clinical utility to differentiate PTSD patients from healthy subjects. Further, we evaluated whether the combination of subjective and objective methods improves the accuracy of classification into patient versus healthy groups, which has important diagnostic implications. We recruited 32 chronic war-induced PTSD patients and 32 age- and gender-matched healthy subjects to participate in this study. Subjective (i.e. from three self-reported sleep questionnaires) and objective sleep-related data (i.e. from actigraphy scores) were collected from each participant. Subjective, objective, and combined (subjective and objective) sleep data were then analyzed using support vector machine classification. The classification accuracy, sensitivity, and specificity for subjective variables were 89.2%, 89.3%, and 89%, respectively. The classification accuracy, sensitivity, and specificity for objective variables were 65%, 62.3%, and 67.8%, respectively. The classification accuracy, sensitivity, and specificity for the aggregate variables (combination of subjective and objective variables) were 91.6%, 93.0%, and 90.3%, respectively. Our findings indicate that classification accuracy using subjective measurements is superior to objective measurements and the combination of both assessments appears to improve the classification accuracy for differentiating PTSD patients from healthy individuals. Copyright © 2017 Elsevier B.V. All rights reserved.

Object-based land cover classification based on fusion of multifrequency SAR data and THAICHOTE optical imagery

NASA Astrophysics Data System (ADS)

Sukawattanavijit, Chanika; Srestasathiern, Panu

2017-10-01

Land Use and Land Cover (LULC) information are significant to observe and evaluate environmental change. LULC classification applying remotely sensed data is a technique popularly employed on a global and local dimension particularly, in urban areas which have diverse land cover types. These are essential components of the urban terrain and ecosystem. In the present, object-based image analysis (OBIA) is becoming widely popular for land cover classification using the high-resolution image. COSMO-SkyMed SAR data was fused with THAICHOTE (namely, THEOS: Thailand Earth Observation Satellite) optical data for land cover classification using object-based. This paper indicates a comparison between object-based and pixel-based approaches in image fusion. The per-pixel method, support vector machines (SVM) was implemented to the fused image based on Principal Component Analysis (PCA). For the objectbased classification was applied to the fused images to separate land cover classes by using nearest neighbor (NN) classifier. Finally, the accuracy assessment was employed by comparing with the classification of land cover mapping generated from fused image dataset and THAICHOTE image. The object-based data fused COSMO-SkyMed with THAICHOTE images demonstrated the best classification accuracies, well over 85%. As the results, an object-based data fusion provides higher land cover classification accuracy than per-pixel data fusion.

Proceedings of the International Workshop on the Foundations of Service-Oriented Architecture (FSOA 2007)

DTIC Science & Technology

2008-06-01

agenda are summarized. x | CMU/SEI-2008-SR-011 SOFTWARE ENGINEERING INSTITUTE | 1 1 Introduction Service -oriented architecture (SOA... service -provision software systems. In this po- sition paper, we investigate an initial classification of challenge areas related to service orientation...decade we have witnessed a significant growth of software applications that are de- livered in the form of services utilizing a network infrastructure

Development of a User-Oriented Data Classification for Information System Design Methodology.

DTIC Science & Technology

1982-06-30

4, December 1978,I T [COD79] CODD , E. F ., "Extending the Database Relational Model h to Capture More Meaning." ACM TODS 4:4, December 1979. [COU731...I AD-All& 879 ALPHA 4 A aOROU INC SILVEXRIN MD.m F /S 5/2DEVELOP 4T OF A UUA-ORIENTS11 DATA CLASSIFICATION FOR INPORMAT--ETCIU)AMR at �-82-C-0129...mwtizuii tm esign = au* C ~I #i systemtic, ady&nmuic Viobze of an terpditand I~~~UWT FigureTO OF Tso: ~ow u PawKq"I m F ~pra o.saper ewmatatLou Page. MIL

Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images.

PubMed

Ran, Lingyan; Zhang, Yanning; Zhang, Qilin; Yang, Tao

2017-06-12

Vision-based mobile robot navigation is a vibrant area of research with numerous algorithms having been developed, the vast majority of which either belong to the scene-oriented simultaneous localization and mapping (SLAM) or fall into the category of robot-oriented lane-detection/trajectory tracking. These methods suffer from high computational cost and require stringent labelling and calibration efforts. To address these challenges, this paper proposes a lightweight robot navigation framework based purely on uncalibrated spherical images. To simplify the orientation estimation, path prediction and improve computational efficiency, the navigation problem is decomposed into a series of classification tasks. To mitigate the adverse effects of insufficient negative samples in the "navigation via classification" task, we introduce the spherical camera for scene capturing, which enables 360° fisheye panorama as training samples and generation of sufficient positive and negative heading directions. The classification is implemented as an end-to-end Convolutional Neural Network (CNN), trained on our proposed Spherical-Navi image dataset, whose category labels can be efficiently collected. This CNN is capable of predicting potential path directions with high confidence levels based on a single, uncalibrated spherical image. Experimental results demonstrate that the proposed framework outperforms competing ones in realistic applications.

Haptic Classification of Common Objects: Knowledge-Driven Exploration.

ERIC Educational Resources Information Center

Lederman, Susan J.; Klatzky, Roberta L.

1990-01-01

Theoretical and empirical issues relating to haptic exploration and the representation of common objects during haptic classification were investigated in 3 experiments involving a total of 112 college students. Results are discussed in terms of a computational model of human haptic object classification with implications for dextrous robot…

Single-Grasp Object Classification and Feature Extraction with Simple Robot Hands and Tactile Sensors.

PubMed

Spiers, Adam J; Liarokapis, Minas V; Calli, Berk; Dollar, Aaron M

2016-01-01

Classical robotic approaches to tactile object identification often involve rigid mechanical grippers, dense sensor arrays, and exploratory procedures (EPs). Though EPs are a natural method for humans to acquire object information, evidence also exists for meaningful tactile property inference from brief, non-exploratory motions (a 'haptic glance'). In this work, we implement tactile object identification and feature extraction techniques on data acquired during a single, unplanned grasp with a simple, underactuated robot hand equipped with inexpensive barometric pressure sensors. Our methodology utilizes two cooperating schemes based on an advanced machine learning technique (random forests) and parametric methods that estimate object properties. The available data is limited to actuator positions (one per two link finger) and force sensors values (eight per finger). The schemes are able to work both independently and collaboratively, depending on the task scenario. When collaborating, the results of each method contribute to the other, improving the overall result in a synergistic fashion. Unlike prior work, the proposed approach does not require object exploration, re-grasping, grasp-release, or force modulation and works for arbitrary object start positions and orientations. Due to these factors, the technique may be integrated into practical robotic grasping scenarios without adding time or manipulation overheads.

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

PubMed

Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

2016-01-01

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

Towards a general object-oriented software development methodology

NASA Technical Reports Server (NTRS)

Seidewitz, ED; Stark, Mike

1986-01-01

An object is an abstract software model of a problem domain entity. Objects are packages of both data and operations of that data (Goldberg 83, Booch 83). The Ada (tm) package construct is representative of this general notion of an object. Object-oriented design is the technique of using objects as the basic unit of modularity in systems design. The Software Engineering Laboratory at the Goddard Space Flight Center is currently involved in a pilot program to develop a flight dynamics simulator in Ada (approximately 40,000 statements) using object-oriented methods. Several authors have applied object-oriented concepts to Ada (e.g., Booch 83, Cherry 85). It was found that these methodologies are limited. As a result a more general approach was synthesized with allows a designer to apply powerful object-oriented principles to a wide range of applications and at all stages of design. An overview is provided of this approach. Further, how object-oriented design fits into the overall software life-cycle is considered.

Drug-induced sedation endoscopy (DISE) classification systems: a systematic review and meta-analysis.

PubMed

Dijemeni, Esuabom; D'Amone, Gabriele; Gbati, Israel

2017-12-01

Drug-induced sedation endoscopy (DISE) classification systems have been used to assess anatomical findings on upper airway obstruction, and decide and plan surgical treatments and act as a predictor for surgical treatment outcome for obstructive sleep apnoea management. The first objective is to identify if there is a universally accepted DISE grading and classification system for analysing DISE findings. The second objective is to identify if there is one DISE grading and classification treatment planning framework for deciding appropriate surgical treatment for obstructive sleep apnoea (OSA). The third objective is to identify if there is one DISE grading and classification treatment outcome framework for determining the likelihood of success for a given OSA surgical intervention. A systematic review was performed to identify new and significantly modified DISE classification systems: concept, advantages and disadvantages. Fourteen studies proposing a new DISE classification system and three studies proposing a significantly modified DISE classification were identified. None of the studies were based on randomised control trials. DISE is an objective method for visualising upper airway obstruction. The classification and assessment of clinical findings based on DISE is highly subjective due to the increasing number of DISE classification systems. Hence, this creates a growing divergence in surgical treatment planning and treatment outcome. Further research on a universally accepted objective DISE assessment is critically needed.

The influence of grasping habits and object orientation on motor planning in children and adults.

PubMed

Jovanovic, Bianca; Schwarzer, Gudrun

2017-12-01

We investigated the influence of habitual grasp strategies and object orientation on motor planning in 3-year-olds and 4- to 5-year-old children and adults. Participants were required to rotate different vertically oriented objects around 180°. Usually, adults perform this task by grasping objects with an awkward grip (thumb and index finger pointing downward) at the beginning of the movement, in order to finish it with a comfortable hand position. This pattern corresponds to the well-known end-state comfort effect (ESC) in grasp planning. The presented objects were associated with different habitual grasp orientations that either corresponded with the grasp direction required to reach end-state comfort (downward) or implied a contrary grasp orientation (upward). Additionally, they were presented either in their usual, canonical orientation (e.g., shovel with the blade oriented downward versus cup with its opening oriented upward) or upside down. As dependent variable we analyzed the number of grips conforming to the end-state comfort principle (ESC score) realized in each object type and orientation condition. The number of grips conforming to ESC strongly increased with age. In addition, the extent to which end-state comfort was considered was influenced by the actual orientation of the objects' functional parts. Thus, in all age-groups the ESC score was highest when the functional parts of the objects were oriented downward (shovel presented canonically with blade pointing downward, cup presented upside down) and corresponded to the hand orientation needed to realize ESC. © 2017 Wiley Periodicals, Inc.

Passive polarimetric imagery-based material classification robust to illumination source position and viewpoint.

PubMed

Thilak Krishna, Thilakam Vimal; Creusere, Charles D; Voelz, David G

2011-01-01

Polarization, a property of light that conveys information about the transverse electric field orientation, complements other attributes of electromagnetic radiation such as intensity and frequency. Using multiple passive polarimetric images, we develop an iterative, model-based approach to estimate the complex index of refraction and apply it to target classification.

A Cradle-to-Grave Integrated Approach to Using UNIFORMAT II

ERIC Educational Resources Information Center

Schneider, Richard C.; Cain, David A.

2009-01-01

The ASTM E1557/UNIFORMAT II standard is a three-level, function-oriented classification which links the schematic phase Preliminary Project Descriptions (PPD), based on Construction Standard Institute (CSI) Practice FF/180, to elemental cost estimates based on R.S. Means Cost Data. With the UNIFORMAT II Standard Classification for Building…

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

PubMed

Oh, Sang-Il; Kang, Hang-Bong

2017-01-22

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742

Acoustic positioning and orientation prediction

NASA Technical Reports Server (NTRS)

Barmatz, Martin B. (Inventor); Aveni, Glenn (Inventor); Putterman, Seth (Inventor); Rudnick, Joseph (Inventor)

1990-01-01

A method is described for use with an acoustic positioner, which enables a determination of the equilibrium position and orientation which an object assumes in a zero gravity environment, as well as restoring forces and torques of an object in an acoustic standing wave field. An acoustic standing wave field is established in the chamber, and the object is held at several different positions near the expected equilibrium position. While the object is held at each position, the center resonant frequency of the chamber is determined, by noting which frequency results in the greatest pressure of the acoustic field. The object position which results in the lowest center resonant frequency is the equilibrium position. The orientation of a nonspherical object is similarly determined, by holding the object in a plurality of different orientations at its equilibrium position, and noting the center resonant frequency for each orientation. The orientation which results in the lowest center resonant frequency is the equilibrium orientation. Where the acoustic frequency is constant, but the chamber length is variable, the equilibrium position or orientation is that which results in the greatest chamber length at the center resonant frequency.

Mapping changes in the largest continuous Amazonian mangrove belt using object-based classification of multisensor satellite imagery

NASA Astrophysics Data System (ADS)

Nascimento, Wilson R.; Souza-Filho, Pedro Walfir M.; Proisy, Christophe; Lucas, Richard M.; Rosenqvist, Ake

2013-01-01

Mapping and monitoring mangrove ecosystems is a crucial objective for tropical countries, particularly where human disturbance occurs and because of uncertainties associated with sea level and climatic fluctuation. In many tropical regions, such efforts have focused largely on the use of optical data despite low capture rates because of persistent cloud cover. Recognizing the ability of Synthetic Aperture Radar (SAR) for providing cloud-free observations, this study investigated the use of JERS-1 SAR and ALOS PALSAR data, acquired in 1996 and 2008 respectively, for mapping the extent of mangroves along the Brazilian coastline, from east of the Amazon River mouth, Pará State, to the Bay of São José in Maranhão. For each year, an object-orientated classification of major land covers (mangrove, secondary vegetation, gallery and swamp forest, open water, intermittent lakes and bare areas) was performed with the resulting maps then compared to quantify change. Comparison with available ground truth data indicated a general accuracy in the 2008 image classification of all land covers of 96% (kappa = 90.6%, tau = 92.6%). Over the 12 year period, the area of mangrove increased by 718.6 km2 from 6705 m2 to 7423.60 km2, with 1931.0 km² of expansion and 1213 km² of erosion noted; 5493 km² remained unchanged in extent. The general accuracy relating to changes in mangroves was 83.3% (Kappa 66.1%; tau 66.7%). The study confirmed that these mangroves constituted the largest continuous belt globally and were experiencing significant change because of the dynamic coastal environment and the influence of sedimentation from the Amazon River along the shoreline. The study recommends continued observations using combinations of SAR and optical data to establish trends in mangrove distributions and implications for provision of ecosystem services (e.g., fish/invertebrate nurseries, carbon storage and coastal protection).

The effect of implied orientation derived from verbal context on picture recognition.

PubMed

Stanfield, R A; Zwaan, R A

2001-03-01

Perceptual symbol systems assume an analogue relationship between a symbol and its referent, whereas amodal symbol systems assume an arbitrary relationship between a symbol and its referent. According to perceptual symbol theories, the complete representation of an object, called a simulation, should reflect physical characteristics of the object. Amodal theories, in contrast, do not make this prediction. We tested the hypothesis, derived from perceptual symbol theories, that people mentally represent the orientation of an object implied by a verbal description. Orientation (vertical-horizontal) was manipulated by having participants read a sentence that implicitly suggested a particular orientation for an object. Then recognition latencies to pictures of the object in each of the two orientations were measured. Pictures matching the orientation of the object implied by the sentence were responded to faster than pictures that did not match the orientation. This finding is interpreted as offering support for theories positing perceptual symbol systems.

A Study on the Difficulties of Learning Phase Transition in Object-Oriented Analysis and Design from the Viewpoint of Semantic Distance

ERIC Educational Resources Information Center

Shin, Shin-Shing

2015-01-01

Students in object-oriented analysis and design (OOAD) courses typically encounter difficulties transitioning from object-oriented analysis (OOA) to logical design (OOLD). This study conducted an empirical experiment to examine these learning difficulties by evaluating differences between OOA-to-OOLD and OOLD-to-object-oriented-physical-design…

The Nature of an Object-Oriented Program: How Do Practitioners Understand the Nature of What They Are Creating?

ERIC Educational Resources Information Center

Thompson, Errol; Kinshuk

2011-01-01

Object-oriented programming is seen as a difficult skill to master. There is considerable debate about the most appropriate way to introduce novice programmers to object-oriented concepts. Is it possible to uncover what the critical aspects or features are that enhance the learning of object-oriented programming? Practitioners have differing…

Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia

PubMed Central

Lhermitte, L; Mejstrikova, E; van der Sluijs-Gelling, A J; Grigore, G E; Sedek, L; Bras, A E; Gaipa, G; Sobral da Costa, E; Novakova, M; Sonneveld, E; Buracchi, C; de Sá Bacelar, T; te Marvelde, J G; Trinquand, A; Asnafi, V; Szczepanski, T; Matarraz, S; Lopez, A; Vidriales, B; Bulsa, J; Hrusak, O; Kalina, T; Lecrevisse, Q; Martin Ayuso, M; Brüggemann, M; Verde, J; Fernandez, P; Burgos, L; Paiva, B; Pedreira, C E; van Dongen, J J M; Orfao, A; van der Velden, V H J

2018-01-01

Precise classification of acute leukemia (AL) is crucial for adequate treatment. EuroFlow has previously designed an AL orientation tube (ALOT) to guide towards the relevant classification panel (T-cell acute lymphoblastic leukemia (T-ALL), B-cell precursor (BCP)-ALL and/or acute myeloid leukemia (AML)) and final diagnosis. Now we built a reference database with 656 typical AL samples (145 T-ALL, 377 BCP-ALL, 134 AML), processed and analyzed via standardized protocols. Using principal component analysis (PCA)-based plots and automated classification algorithms for direct comparison of single-cells from individual patients against the database, another 783 cases were subsequently evaluated. Depending on the database-guided results, patients were categorized as: (i) typical T, B or Myeloid without or; (ii) with a transitional component to another lineage; (iii) atypical; or (iv) mixed-lineage. Using this automated algorithm, in 781/783 cases (99.7%) the right panel was selected, and data comparable to the final WHO-diagnosis was already provided in >93% of cases (85% T-ALL, 97% BCP-ALL, 95% AML and 87% mixed-phenotype AL patients), even without data on the full-characterization panels. Our results show that database-guided analysis facilitates standardized interpretation of ALOT results and allows accurate selection of the relevant classification panels, hence providing a solid basis for designing future WHO AL classifications. PMID:29089646

A review of supervised object-based land-cover image classification

NASA Astrophysics Data System (ADS)

Ma, Lei; Li, Manchun; Ma, Xiaoxue; Cheng, Liang; Du, Peijun; Liu, Yongxue

2017-08-01

Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial vehicle) or agricultural sites where it also correlates with the number of targeted classes. More than 95.6% of studies involve an area less than 300 ha, and the spatial resolution of images is predominantly between 0 and 2 m. Furthermore, we identify some methods that may advance supervised object-based image classification. For example, deep learning and type-2 fuzzy techniques may further improve classification accuracy. Lastly, scientists are strongly encouraged to report results of uncertainty studies to further explore the effects of varied factors on supervised object-based image classification.

Remote sensing and spatial analysis based study for detecting deforestation and the associated drivers

NASA Astrophysics Data System (ADS)

El-Abbas, Mustafa M.; Csaplovics, Elmar; Deafalla, Taisser H.

2013-10-01

Nowadays, remote-sensing technologies are becoming increasingly interlinked to the issue of deforestation. They offer a systematized and objective strategy to document, understand and simulate the deforestation process and its associated causes. In this context, the main goal of this study, conducted in the Blue Nile region of Sudan, in which most of the natural habitats were dramatically destroyed, was to develop spatial methodologies to assess the deforestation dynamics and its associated factors. To achieve that, optical multispectral satellite scenes (i.e., ASTER and LANDSAT) integrated with field survey in addition to multiple data sources were used for the analyses. Spatiotemporal Object Based Image Analysis (STOBIA) was applied to assess the change dynamics within the period of study. Broadly, the above mentioned analyses include; Object Based (OB) classifications, post-classification change detection, data fusion, information extraction and spatial analysis. Hierarchical multi-scale segmentation thresholds were applied and each class was delimited with semantic meanings by a set of rules associated with membership functions. Consequently, the fused multi-temporal data were introduced to create detailed objects of change classes from the input LU/LC classes. The dynamic changes were quantified and spatially located as well as the spatial and contextual relations from adjacent areas were analyzed. The main finding of the present study is that, the forest areas were drastically decreased, while the agrarian structure in conversion of forest into agricultural fields and grassland was the main force of deforestation. In contrast, the capability of the area to recover was clearly observed. The study concludes with a brief assessment of an 'oriented' framework, focused on the alarming areas where serious dynamics are located and where urgent plans and interventions are most critical, guided with potential solutions based on the identified driving forces.

Object based image analysis for the classification of the growth stages of Avocado crop, in Michoacán State, Mexico

NASA Astrophysics Data System (ADS)

Gao, Yan; Marpu, Prashanth; Morales Manila, Luis M.

2014-11-01

This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and without the four new spectral bands. Classification accuracy assessment results show that object-based classification with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%) method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.

Classification of earth terrain using polarimetric synthetic aperture radar images

NASA Technical Reports Server (NTRS)

Lim, H. H.; Swartz, A. A.; Yueh, H. A.; Kong, J. A.; Shin, R. T.; Van Zyl, J. J.

1989-01-01

Supervised and unsupervised classification techniques are developed and used to classify the earth terrain components from SAR polarimetric images of San Francisco Bay and Traverse City, Michigan. The supervised techniques include the Bayes classifiers, normalized polarimetric classification, and simple feature classification using discriminates such as the absolute and normalized magnitude response of individual receiver channel returns and the phase difference between receiver channels. An algorithm is developed as an unsupervised technique which classifies terrain elements based on the relationship between the orientation angle and the handedness of the transmitting and receiving polariation states. It is found that supervised classification produces the best results when accurate classifier training data are used, while unsupervised classification may be applied when training data are not available.

Object-oriented knowledge representation for expert systems

NASA Technical Reports Server (NTRS)

Scott, Stephen L.

1991-01-01

Object oriented techniques have generated considerable interest in the Artificial Intelligence (AI) community in recent years. This paper discusses an approach for representing expert system knowledge using classes, objects, and message passing. The implementation is in version 4.3 of NASA's C Language Integrated Production System (CLIPS), an expert system tool that does not provide direct support for object oriented design. The method uses programmer imposed conventions and keywords to structure facts, and rules to provide object oriented capabilities.

Lunar Data Information Center: A Shortcut to the Riddle of the Moon

ERIC Educational Resources Information Center

Waranius, Frances B.; Heiken, Jody H.

1975-01-01

The Lunar Data Information Center is a reference and lending collection for researchers, educators, and students of lunar science, worldwide. Such methods as a classification scheme for mission-oriented documentation, sample photo browse files, lunar feature index, and color coding have resulted in a user-oriented collection. Search capability is…

A data set for evaluating the performance of multi-class multi-object video tracking

NASA Astrophysics Data System (ADS)

Chakraborty, Avishek; Stamatescu, Victor; Wong, Sebastien C.; Wigley, Grant; Kearney, David

2017-05-01

One of the challenges in evaluating multi-object video detection, tracking and classification systems is having publically available data sets with which to compare different systems. However, the measures of performance for tracking and classification are different. Data sets that are suitable for evaluating tracking systems may not be appropriate for classification. Tracking video data sets typically only have ground truth track IDs, while classification video data sets only have ground truth class-label IDs. The former identifies the same object over multiple frames, while the latter identifies the type of object in individual frames. This paper describes an advancement of the ground truth meta-data for the DARPA Neovision2 Tower data set to allow both the evaluation of tracking and classification. The ground truth data sets presented in this paper contain unique object IDs across 5 different classes of object (Car, Bus, Truck, Person, Cyclist) for 24 videos of 871 image frames each. In addition to the object IDs and class labels, the ground truth data also contains the original bounding box coordinates together with new bounding boxes in instances where un-annotated objects were present. The unique IDs are maintained during occlusions between multiple objects or when objects re-enter the field of view. This will provide: a solid foundation for evaluating the performance of multi-object tracking of different types of objects, a straightforward comparison of tracking system performance using the standard Multi Object Tracking (MOT) framework, and classification performance using the Neovision2 metrics. These data have been hosted publically.

Object-oriented numerical computing C++

NASA Technical Reports Server (NTRS)

Vanrosendale, John

1994-01-01

An object oriented language is one allowing users to create a set of related types and then intermix and manipulate values of these related types. This paper discusses object oriented numerical computing using C++.

Image classification of unlabeled malaria parasites in red blood cells.

PubMed

Zheng Zhang; Ong, L L Sharon; Kong Fang; Matthew, Athul; Dauwels, Justin; Ming Dao; Asada, Harry

2016-08-01

This paper presents a method to detect unlabeled malaria parasites in red blood cells. The current "gold standard" for malaria diagnosis is microscopic examination of thick blood smear, a time consuming process requiring extensive training. Our goal is to develop an automate process to identify malaria infected red blood cells. Major issues in automated analysis of microscopy images of unstained blood smears include overlapping cells and oddly shaped cells. Our approach creates robust templates to detect infected and uninfected red cells. Histogram of Oriented Gradients (HOGs) features are extracted from templates and used to train a classifier offline. Next, the ViolaJones object detection framework is applied to detect infected and uninfected red cells and the image background. Results show our approach out-performs classification approaches with PCA features by 50% and cell detection algorithms applying Hough transforms by 24%. Majority of related work are designed to automatically detect stained parasites in blood smears where the cells are fixed. Although it is more challenging to design algorithms for unstained parasites, our methods will allow analysis of parasite progression in live cells under different drug treatments.

Segmentation and Classification of Nepal Earthquake Induced Landslides Using SENTINEL-1 Product

NASA Astrophysics Data System (ADS)

Kunwar, Saket

2016-06-01

On April 26, 2015, an earthquake of magnitude 7.8 on the Richter scale occurred, with epicentre at Barpak (28°12'20''N,84°44'19''E), Nepal. Landslides induced due to the earthquake and its aftershock added to the natural disaster claiming more than 9000 lives. Landslides represented as lines that extend from the head scarp to the toe of the deposit were mapped by the staff of the British Geological Survey and is available freely under Open Data Commons Open Database License(ODC-ODbL) license at the Humanitarian Data Exchange Program. This collection of 5578 landslides is used as preliminary ground truth in this study with the aim of producing polygonal delineation of the landslides from the polylines via object oriented segmentation. Texture measures from Sentinel-1a Ground Range Detected (GRD) Amplitude data and eigenvalue-decomposed Single Look Complex (SLC) polarimetry product are stacked for this purpose. This has also enabled the investigation of landslide properties in the H-Alpha plane, while developing a classification mechanism for identifying the occurrence of landslides.

Postural and Object-Oriented Experiences Advance Early Reaching, Object Exploration, and Means-End Behavior

ERIC Educational Resources Information Center

Lobo, Michele A.; Galloway, James C.

2008-01-01

The effects of 3 weeks of social (control), postural, or object-oriented experiences on 9- to 21-week-old infants' (N = 42) reaching, exploration, and means-end behaviors were assessed. Coders recorded object contacts, mouthing, fingering, attention, and affect from video. Postural and object-oriented experiences advanced reaching, haptic…

Object-oriented programming with mixins in Ada

NASA Technical Reports Server (NTRS)

Seidewitz, ED

1992-01-01

Recently, I wrote a paper discussing the lack of 'true' object-oriented programming language features in Ada 83, why one might desire them in Ada, and how they might be added in Ada 9X. The approach I took in this paper was to build the new object-oriented features of Ada 9X as much as possible on the basic constructs and philosophy of Ada 83. The object-oriented features proposed for Ada 9X, while different in detail, are based on the same kind of approach. Further consideration of this approach led me on a long reflection on the nature of object-oriented programming and its application to Ada. The results of this reflection, presented in this paper, show how a fairly natural object-oriented style can indeed be developed even in Ada 83. The exercise of developing this style is useful for at least three reasons: (1) it provides a useful style for programming object-oriented applications in Ada 83 until new features become available with Ada 9X; (2) it demystifies many of the mechanisms that seem to be 'magic' in most object-oriented programming languages by making them explicit; and (3) it points out areas that are and are not in need of change in Ada 83 to make object-oriented programming more natural in Ada 9X. In the next four sections I will address in turn the issues of object-oriented classes, mixins, self-reference and supertyping. The presentation is through a sequence of examples. This results in some overlap with that paper, but all the examples in the present paper are written entirely in Ada 83. I will return to considerations for Ada 9X in the last section of the paper.

Towards an Object-Oriented Model for the Design and Development of Learning Objects

ERIC Educational Resources Information Center

Chrysostomou, Chrysostomos; Papadopoulos, George

2008-01-01

This work introduces the concept of an Object-Oriented Learning Object (OOLO) that is developed in a manner similar to the one that software objects are developed through Object-Oriented Software Engineering (OO SWE) techniques. In order to make the application of the OOLO feasible and efficient, an OOLO model needs to be developed based on…

Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image.

PubMed

Rapinel, Sébastien; Clément, Bernard; Magnanon, Sylvie; Sellin, Vanessa; Hubert-Moy, Laurence

2014-11-01

Identification and mapping of natural vegetation are major issues for biodiversity management and conservation. Remotely sensed data with very high spatial resolution are currently used to study vegetation, but most satellite sensors are limited to four spectral bands, which is insufficient to identify some natural vegetation formations. The study objectives are to discriminate natural vegetation and identify natural vegetation formations using a Worldview-2 satellite image. The classification of the Worldview-2 image and ancillary thematic data was performed using a hybrid pixel-based and object-oriented approach. A hierarchical scheme using three levels was implemented, from land cover at a field scale to vegetation formation. This method was applied on a 48 km² site located on the French Atlantic coast which includes a classified NATURA 2000 dune and marsh system. The classification accuracy was very high, the Kappa index varying between 0.90 and 0.74 at land cover and vegetation formation levels respectively. These results show that Wordlview-2 images are suitable to identify natural vegetation. Vegetation maps derived from Worldview-2 images are more detailed than existing ones. They provide a useful medium for environmental management of vulnerable areas. The approach used to map natural vegetation is reproducible for a wider application by environmental managers. Copyright © 2014 Elsevier Ltd. All rights reserved.

Integration of object-oriented knowledge representation with the CLIPS rule based system

NASA Technical Reports Server (NTRS)

Logie, David S.; Kamil, Hasan

1990-01-01

The paper describes a portion of the work aimed at developing an integrated, knowledge based environment for the development of engineering-oriented applications. An Object Representation Language (ORL) was implemented in C++ which is used to build and modify an object-oriented knowledge base. The ORL was designed in such a way so as to be easily integrated with other representation schemes that could effectively reason with the object base. Specifically, the integration of the ORL with the rule based system C Language Production Systems (CLIPS), developed at the NASA Johnson Space Center, will be discussed. The object-oriented knowledge representation provides a natural means of representing problem data as a collection of related objects. Objects are comprised of descriptive properties and interrelationships. The object-oriented model promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects. Data is inherited through an object network via the relationship links. Together, the two schemes complement each other in that the object-oriented approach efficiently handles problem data while the rule based knowledge is used to simulate the reasoning process. Alone, the object based knowledge is little more than an object-oriented data storage scheme; however, the CLIPS inference engine adds the mechanism to directly and automatically reason with that knowledge. In this hybrid scheme, the expert system dynamically queries for data and can modify the object base with complete access to all the functionality of the ORL from rules.

Object-oriented productivity metrics

NASA Technical Reports Server (NTRS)

Connell, John L.; Eller, Nancy

1992-01-01

Software productivity metrics are useful for sizing and costing proposed software and for measuring development productivity. Estimating and measuring source lines of code (SLOC) has proven to be a bad idea because it encourages writing more lines of code and using lower level languages. Function Point Analysis is an improved software metric system, but it is not compatible with newer rapid prototyping and object-oriented approaches to software development. A process is presented here for counting object-oriented effort points, based on a preliminary object-oriented analysis. It is proposed that this approach is compatible with object-oriented analysis, design, programming, and rapid prototyping. Statistics gathered on actual projects are presented to validate the approach.

Object-oriented analysis and design of an ECG storage and retrieval system integrated with an HIS.

PubMed

Wang, C; Ohe, K; Sakurai, T; Nagase, T; Kaihara, S

1996-03-01

For a hospital information system, object-oriented methodology plays an increasingly important role, especially for the management of digitized data, e.g., the electrocardiogram, electroencephalogram, electromyogram, spirogram, X-ray, CT and histopathological images, which are not yet computerized in most hospitals. As a first step in an object-oriented approach to hospital information management and storing medical data in an object-oriented database, we connected electrocardiographs to a hospital network and established the integration of ECG storage and retrieval systems with a hospital information system. In this paper, the object-oriented analysis and design of the ECG storage and retrieval systems is reported.

Measures of clinical health among female-to-male transgender persons as a function of sexual orientation.

PubMed

Meier, S Colton; Pardo, Seth T; Labuski, Christine; Babcock, Julia

2013-04-01

The present study examined the sexual orientation classification system that was used in the DSM-IV-TR for categorizing those who met the Gender Identity Disorder diagnostic criteria in order to determine the extent to which female-to-male transgender persons (FTMs) differ on psychological variables as a function of sexual orientation. Participants were 605 self-identified FTMs from 19 different countries (83 % U.S.) who completed an internet survey assessing their sexual orientation, sexual identity, symptoms of depression and anxiety, stress (Depression Anxiety Stress Scales), social support (Multidimensional Scale of Perceived Social Support), and health related quality of life (SF-36v2 Health Survey). Over half the sample (52 %) reported sexual attractions to both men and women. The most common sexual identity label reported was "queer." Forty percent of FTMs who had begun to transition reported a shift in sexual orientation; this shift was associated with testosterone use. Overall, FTMs ranged from normal to above average on all psychological measures. FTMs did not significantly differ by sexual attraction on any mental health variables, except for anxiety. FTMs attracted to both men and women reported more symptoms of anxiety than those attracted to men only. Results from the present study did not support a sexual orientation classification system in FTMs with regard to psychological well-being.

Auto-SEIA: simultaneous optimization of image processing and machine learning algorithms

NASA Astrophysics Data System (ADS)

Negro Maggio, Valentina; Iocchi, Luca

2015-02-01

Object classification from images is an important task for machine vision and it is a crucial ingredient for many computer vision applications, ranging from security and surveillance to marketing. Image based object classification techniques properly integrate image processing and machine learning (i.e., classification) procedures. In this paper we present a system for automatic simultaneous optimization of algorithms and parameters for object classification from images. More specifically, the proposed system is able to process a dataset of labelled images and to return a best configuration of image processing and classification algorithms and of their parameters with respect to the accuracy of classification. Experiments with real public datasets are used to demonstrate the effectiveness of the developed system.

Improving the discrimination of hand motor imagery via virtual reality based visual guidance.

PubMed

Liang, Shuang; Choi, Kup-Sze; Qin, Jing; Pang, Wai-Man; Wang, Qiong; Heng, Pheng-Ann

2016-08-01

While research on the brain-computer interface (BCI) has been active in recent years, how to get high-quality electrical brain signals to accurately recognize human intentions for reliable communication and interaction is still a challenging task. The evidence has shown that visually guided motor imagery (MI) can modulate sensorimotor electroencephalographic (EEG) rhythms in humans, but how to design and implement efficient visual guidance during MI in order to produce better event-related desynchronization (ERD) patterns is still unclear. The aim of this paper is to investigate the effect of using object-oriented movements in a virtual environment as visual guidance on the modulation of sensorimotor EEG rhythms generated by hand MI. To improve the classification accuracy on MI, we further propose an algorithm to automatically extract subject-specific optimal frequency and time bands for the discrimination of ERD patterns produced by left and right hand MI. The experimental results show that the average classification accuracy of object-directed scenarios is much better than that of non-object-directed scenarios (76.87% vs. 69.66%). The result of the t-test measuring the difference between them is statistically significant (p = 0.0207). When compared to algorithms based on fixed frequency and time bands, contralateral dominant ERD patterns can be enhanced by using the subject-specific optimal frequency and the time bands obtained by our proposed algorithm. These findings have the potential to improve the efficacy and robustness of MI-based BCI applications. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A multiple-point spatially weighted k-NN method for object-based classification

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.

2016-10-01

Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

Test of spectral/spatial classifier

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator); Kast, J. L.; Davis, B. J.

1977-01-01

The author has identified the following significant results. The supervised ECHO processor (which utilizes class statistics for object identification) successfully exploits the redundancy of states characteristic of sampled imagery of ground scenes to achieve better classification accuracy, reduce the number of classifications required, and reduce the variability of classification results. The nonsupervised ECHO processor (which identifies objects without the benefit of class statistics) successfully reduces the number of classifications required and the variability of the classification results.

Secure access control and large scale robust representation for online multimedia event detection.

PubMed

Liu, Changyu; Lu, Bin; Li, Huiling

2014-01-01

We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches.

Object-oriented requirements analysis: A quick tour

NASA Technical Reports Server (NTRS)

Berard, Edward V.

1990-01-01

Of all the approaches to software development, an object-oriented approach appears to be both the most beneficial and the most popular. The description of the object-oriented approach is presented in the form of the view graphs.

High Performance Object-Oriented Scientific Programming in Fortran 90

NASA Technical Reports Server (NTRS)

Norton, Charles D.; Decyk, Viktor K.; Szymanski, Boleslaw K.

1997-01-01

We illustrate how Fortran 90 supports object-oriented concepts by example of plasma particle computations on the IBM SP. Our experience shows that Fortran 90 and object-oriented methodology give high performance while providing a bridge from Fortran 77 legacy codes to modern programming principles. All of our object-oriented Fortran 90 codes execute more quickly thatn the equeivalent C++ versions, yet the abstraction modelling capabilities used for scentific programming are comparably powereful.

The V-Flex, Triangle Orientation, and Catalan Numbers in Hexaflexagons

ERIC Educational Resources Information Center

Iacob, Ionut E.; McLean, T. Bruce; Wang, Hua

2012-01-01

Regular hexaflexagons mysteriously change faces as you pinch flex them. This paper describes a different flex, the V-flex, which allows the hexahexaflexagon (with only 9 faces under the pinch flex) to have 3420 faces. The article goes on to explain the classification of triangle orientations in a hexaflexagon and gives an example of the…

A case of complex regional pain syndrome with agnosia for object orientation.

PubMed

Robinson, Gail; Cohen, Helen; Goebel, Andreas

2011-07-01

This systematic investigation of the neurocognitive correlates of complex regional pain syndrome (CRPS) in a single case also reports agnosia for object orientation in the context of persistent CRPS. We report a patient (JW) with severe long-standing CRPS who had no difficulty identifying and naming line drawings of objects presented in 1 of 4 cardinal orientations. In contrast, he was extremely poor at reorienting these objects into the correct upright orientation and in judging whether an object was upright or not. Moreover, JW made orientation errors when copying drawings of objects, and he also showed features of mirror reversal in writing single words and reading single letters. The findings are discussed in relation to accounts of visual processing. Agnosia for object orientation is the term for impaired knowledge of an object's orientation despite good recognition and naming of the same misoriented object. This defect has previously only been reported in patients with major structural brain lesions. The neuroanatomical correlates are discussed. The patient had no structural brain lesion, raising the possibility that nonstructural reorganisation of cortical networks may be responsible for his deficits. Other patients with CRPS may have related neurocognitive defects. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

Orientation priming of grasping decision for drawings of objects and blocks, and words.

PubMed

Chainay, Hanna; Naouri, Lucie; Pavec, Alice

2011-05-01

This study tested the influence of orientation priming on grasping decisions. Two groups of 20 healthy participants had to select a preferred grasping orientation (horizontal, vertical) based on drawings of everyday objects, geometric blocks or object names. Three priming conditions were used: congruent, incongruent and neutral. The facilitating effects of priming were observed in the grasping decision task for drawings of objects and blocks but not object names. The visual information about congruent orientation in the prime quickened participants' responses but had no effect on response accuracy. The results are discussed in the context of the hypothesis that an object automatically potentiates grasping associated with it, and that the on-line visual information is necessary for grasping potentiation to occur. The possibility that the most frequent orientation of familiar objects might be included in object-action representation is also discussed.

Goal oriented soil mapping: applying modern methods supported by local knowledge: A review

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Brevik, Eric; Oliva, Marc; Estebaranz, Ferran; Depellegrin, Daniel; Novara, Agata; Cerda, Artemi; Menshov, Oleksandr

2017-04-01

In the recent years the amount of soil data available increased importantly. This facilitated the production of better and accurate maps, important for sustainable land management (Pereira et al., 2017). Despite these advances, the human knowledge is extremely important to understand the natural characteristics of the landscape. The knowledge accumulated and transmitted generation after generation is priceless, and should be considered as a valuable data source for soil mapping and modelling. The local knowledge and wisdom can complement the new advances in soil analysis. In addition, farmers are the most interested in the participation and incorporation of their knowledge in the models, since they are the end-users of the study that soil scientists produce. Integration of local community's vision and understanding about nature is assumed to be an important step to the implementation of decision maker's policies. Despite this, many challenges appear regarding the integration of local and scientific knowledge, since in some cases there is no spatial correlation between folk and scientific classifications, which may be attributed to the different cultural variables that influence local soil classification. The objective of this work is to review how modern soil methods incorporated local knowledge in their models. References Pereira, P., Brevik, E., Oliva, M., Estebaranz, F., Depellegrin, D., Novara, A., Cerda, A., Menshov, O. (2017) Goal Oriented soil mapping: applying modern methods supported by local knowledge. In: Pereira, P., Brevik, E., Munoz-Rojas, M., Miller, B. (Eds.) Soil mapping and process modelling for sustainable land use management (Elsevier Publishing House) ISBN: 9780128052006

Object-Oriented Programming When Developing Software in Geology and Geophysics

NASA Astrophysics Data System (ADS)

Ahmadulin, R. K.; Bakanovskaya, L. N.

2017-01-01

The paper reviews the role of object-oriented programming when developing software in geology and geophysics. Main stages have been identified at which it is worthwhile to apply principles of object-oriented programming when developing software in geology and geophysics. The research was based on a number of problems solved in Geology and Petroleum Production Institute. Distinctive features of these problems are given and areas of application of the object-oriented approach are identified. Developing applications in the sphere of geology and geophysics has shown that the process of creating such products is simplified due to the use of object-oriented programming, firstly when designing structures for data storage and graphical user interfaces.

Checking an integrated model of web accessibility and usability evaluation for disabled people.

PubMed

Federici, Stefano; Micangeli, Andrea; Ruspantini, Irene; Borgianni, Stefano; Corradi, Fabrizio; Pasqualotto, Emanuele; Olivetti Belardinelli, Marta

2005-07-08

A combined objective-oriented and subjective-oriented method for evaluating accessibility and usability of web pages for students with disability was tested. The objective-oriented approach is devoted to verifying the conformity of interfaces to standard rules stated by national and international organizations responsible for web technology standardization, such as W3C. Conversely, the subjective-oriented approach allows assessing how the final users interact with the artificial system, accessing levels of user satisfaction based on personal factors and environmental barriers. Five kinds of measurements were applied as objective-oriented and subjective-oriented tests. Objective-oriented evaluations were performed on the Help Desk web page for students with disability, included in the website of a large Italian state university. Subjective-oriented tests were administered to 19 students labeled as disabled on the basis of their own declaration at the University enrolment: 13 students were tested by means of the SUMI test and six students by means of the 'Cooperative evaluation'. Objective-oriented and subjective-oriented methods highlighted different and sometimes conflicting results. Both methods have pointed out much more consistency regarding levels of accessibility than of usability. Since usability is largely affected by individual differences in user's own (dis)abilities, subjective-oriented measures underscored the fact that blind students encountered much more web surfing difficulties.

The Assignment of Scale to Object-Oriented Software Measures

NASA Technical Reports Server (NTRS)

Neal, Ralph D.; Weistroffer, H. Roland; Coppins, Richard J.

1997-01-01

In order to improve productivity (and quality), measurement of specific aspects of software has become imperative. As object oriented programming languages have become more widely used, metrics designed specifically for object-oriented software are required. Recently a large number of new metrics for object- oriented software has appeared in the literature. Unfortunately, many of these proposed metrics have not been validated to measure what they purport to measure. In this paper fifty (50) of these metrics are analyzed.

Considerations of persistence and security in CHOICES, an object-oriented operating system

NASA Technical Reports Server (NTRS)

Campbell, Roy H.; Madany, Peter W.

1990-01-01

The current design of the CHOICES persistent object implementation is summarized, and research in progress is outlined. CHOICES is implemented as an object-oriented system, and persistent objects appear to simplify and unify many functions of the system. It is demonstrated that persistent data can be accessed through an object-oriented file system model as efficiently as by an existing optimized commercial file system. The object-oriented file system can be specialized to provide an object store for persistent objects. The problems that arise in building an efficient persistent object scheme in a 32-bit virtual address space that only uses paging are described. Despite its limitations, the solution presented allows quite large numbers of objects to be active simultaneously, and permits sharing and efficient method calls.

A management-oriented classification of pinyon-juniper woodlands of the Great Basin

Treesearch

Neil E. West; Robin J. Tausch; Paul T. Tueller

1998-01-01

A hierarchical framework for the classification of Great Basin pinyon-juniper woodlands was based on a systematic sample of 426 stands from a random selection of 66 of the 110 mountain ranges in the region. That is, mountain ranges were randomly selected, but stands were systematically located on mountain ranges. The National Hierarchical Framework of Ecological Units...

Thermographic image analysis for classification of ACL rupture disease, bone cancer, and feline hyperthyroid, with Gabor filters

NASA Astrophysics Data System (ADS)

Alvandipour, Mehrdad; Umbaugh, Scott E.; Mishra, Deependra K.; Dahal, Rohini; Lama, Norsang; Marino, Dominic J.; Sackman, Joseph

2017-05-01

Thermography and pattern classification techniques are used to classify three different pathologies in veterinary images. Thermographic images of both normal and diseased animals were provided by the Long Island Veterinary Specialists (LIVS). The three pathologies are ACL rupture disease, bone cancer, and feline hyperthyroid. The diagnosis of these diseases usually involves radiology and laboratory tests while the method that we propose uses thermographic images and image analysis techniques and is intended for use as a prescreening tool. Images in each category of pathologies are first filtered by Gabor filters and then various features are extracted and used for classification into normal and abnormal classes. Gabor filters are linear filters that can be characterized by the two parameters wavelength λ and orientation θ. With two different wavelength and five different orientations, a total of ten different filters were studied. Different combinations of camera views, filters, feature vectors, normalization methods, and classification methods, produce different tests that were examined and the sensitivity, specificity and success rate for each test were produced. Using the Gabor features alone, sensitivity, specificity, and overall success rates of 85% for each of the pathologies was achieved.

Choices, Frameworks and Refinement

NASA Technical Reports Server (NTRS)

Campbell, Roy H.; Islam, Nayeem; Johnson, Ralph; Kougiouris, Panos; Madany, Peter

1991-01-01

In this paper we present a method for designing operating systems using object-oriented frameworks. A framework can be refined into subframeworks. Constraints specify the interactions between the subframeworks. We describe how we used object-oriented frameworks to design Choices, an object-oriented operating system.

Stellar Classification Online - Public Exploration

NASA Astrophysics Data System (ADS)

Castelaz, Michael W.; Bedell, W.; Barker, T.; Cline, J.; Owen, L.

2009-01-01

The Michigan Objective Prism Blue Survey (e.g. Sowell et al 2007, AJ, 134, 1089) photographic plates located in the Astronomical Photographic Data Archive at the Pisgah Astronomical Research Institute hold hundreds of thousands of stellar spectra, many of which have not been classified before. The public is invited to participate in a distributed computing online environment to classify the stars on the objective prism plates. The online environment is called Stellar Classification Online - Public Exploration (SCOPE). Through a website, SCOPE participants are given a tutorial on stellar spectra and their classification, and given the chance to practice their skills at classification. After practice, participants register, login, and select stars for classification from scans of the objective prism plates. Their classifications are recorded in a database where the accumulation of classifications of the same star by many users will be statistically analyzed. The project includes stars with known spectral types to help test the reliability of classifications. The SCOPE webpage and the use of results will be described.

OBJECTIVE METEOROLOGICAL CLASSIFICATION SCHEME DESIGNED TO ELUCIDATE OZONE'S DEPENDENCE ON METEOROLOGY

EPA Science Inventory

This paper utilizes a two-stage clustering approach as part of an objective classification scheme designed to elucidate 03's dependence on meteorology. hen applied to ten years (1981-1990) of meteorological data for Birmingham, Alabama, the classification scheme identified seven ...

THE LIMITED EFFECT OF COINCIDENT ORIENTATION ON THE CHOICE OF INTRINSIC AXIS (.).

PubMed

Li, Jing; Su, Wei

2015-06-01

The allocentric system computes and represents general object-to-object spatial relationships to provide a spatial frame of reference other than the egocentric system. The intrinsic frame-of-reference system theory, which suggests people learn the locations of objects based upon an intrinsic axis, is important in research about the allocentric system. The purpose of the current study was to determine whether the effect of coincident orientation on the choice of intrinsic axis was limited. Two groups of participants (24 men, 24 women; M age = 24 yr., SD = 2) encoded different spatial layouts in which the objects shared the coincident orientation of 315° and 225° separately at learning perspective (0°). The response pattern of partial-scene-recognition task following learning reflected different strategies for choosing the intrinsic axis under different conditions. Under the 315° object-orientation condition, the objects' coincident orientation was as important as the symmetric axis in the choice of the intrinsic axis. However, participants were more likely to choose the symmetric axis as the intrinsic axis under the 225° object-orientation condition. The results suggest the effect of coincident orientation on the choice of intrinsic axis is limited.

Using machine learning techniques to automate sky survey catalog generation

NASA Technical Reports Server (NTRS)

Fayyad, Usama M.; Roden, J. C.; Doyle, R. J.; Weir, Nicholas; Djorgovski, S. G.

1993-01-01

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data.

Single-particle cryo-EM using alignment by classification (ABC): the structure of Lumbricus terrestris haemoglobin.

PubMed

Afanasyev, Pavel; Seer-Linnemayr, Charlotte; Ravelli, Raimond B G; Matadeen, Rishi; De Carlo, Sacha; Alewijnse, Bart; Portugal, Rodrigo V; Pannu, Navraj S; Schatz, Michael; van Heel, Marin

2017-09-01

Single-particle cryogenic electron microscopy (cryo-EM) can now yield near-atomic resolution structures of biological complexes. However, the reference-based alignment algorithms commonly used in cryo-EM suffer from reference bias, limiting their applicability (also known as the 'Einstein from random noise' problem). Low-dose cryo-EM therefore requires robust and objective approaches to reveal the structural information contained in the extremely noisy data, especially when dealing with small structures. A reference-free pipeline is presented for obtaining near-atomic resolution three-dimensional reconstructions from heterogeneous ('four-dimensional') cryo-EM data sets. The methodologies integrated in this pipeline include a posteriori camera correction, movie-based full-data-set contrast transfer function determination, movie-alignment algorithms, (Fourier-space) multivariate statistical data compression and unsupervised classification, 'random-startup' three-dimensional reconstructions, four-dimensional structural refinements and Fourier shell correlation criteria for evaluating anisotropic resolution. The procedures exclusively use information emerging from the data set itself, without external 'starting models'. Euler-angle assignments are performed by angular reconstitution rather than by the inherently slower projection-matching approaches. The comprehensive 'ABC-4D' pipeline is based on the two-dimensional reference-free 'alignment by classification' (ABC) approach, where similar images in similar orientations are grouped by unsupervised classification. Some fundamental differences between X-ray crystallography versus single-particle cryo-EM data collection and data processing are discussed. The structure of the giant haemoglobin from Lumbricus terrestris at a global resolution of ∼3.8 Å is presented as an example of the use of the ABC-4D procedure.

The Analysis of Burrows Recognition Accuracy in XINJIANG'S Pasture Area Based on Uav Visible Images with Different Spatial Resolution

NASA Astrophysics Data System (ADS)

Sun, D.; Zheng, J. H.; Ma, T.; Chen, J. J.; Li, X.

2018-04-01

The rodent disaster is one of the main biological disasters in grassland in northern Xinjiang. The eating and digging behaviors will cause the destruction of ground vegetation, which seriously affected the development of animal husbandry and grassland ecological security. UAV low altitude remote sensing, as an emerging technique with high spatial resolution, can effectively recognize the burrows. However, how to select the appropriate spatial resolution to monitor the calamity of the rodent disaster is the first problem we need to pay attention to. The purpose of this study is to explore the optimal spatial scale on identification of the burrows by evaluating the impact of different spatial resolution for the burrows identification accuracy. In this study, we shoot burrows from different flight heights to obtain visible images of different spatial resolution. Then an object-oriented method is used to identify the caves, and we also evaluate the accuracy of the classification. We found that the highest classification accuracy of holes, the average has reached more than 80 %. At the altitude of 24 m and the spatial resolution of 1cm, the accuracy of the classification is the highest We have created a unique and effective way to identify burrows by using UAVs visible images. We draw the following conclusion: the best spatial resolution of burrows recognition is 1 cm using DJI PHANTOM-3 UAV, and the improvement of spatial resolution does not necessarily lead to the improvement of classification accuracy. This study lays the foundation for future research and can be extended to similar studies elsewhere.

Three Object-Oriented enhancement for EPICS

NASA Astrophysics Data System (ADS)

Osberg, E. A.; Dohan, D. A.; Richter, R.; Biggs, R.; Chillara, K.; Wade, D.; Bossom, J.

1994-12-01

In line with our group's intention of producing software using, where possible, Object-Oriented methodologies and techniques in the development of RF control systems, we have undertaken three projects to enhance the EPICS software environment. Two of the projects involve interfaces to EPICs Channel Access from Object-Oriented languages. The third is an enhancement to the EPICS State Notation Language to better support the Shlaer-Mellor Object-Oriented Analysis and Design Methodology. This paper discusses the motivation, approaches, results and future directions of these three projects.

The Diagnosticity of Color for Emotional Objects

PubMed Central

McMenamin, Brenton W.; Radue, Jasmine; Trask, Joanna; Huskamp, Kristin; Kersten, Daniel; Marsolek, Chad J.

2012-01-01

Object classification can be facilitated if simple diagnostic features can be used to determine class membership. Previous studies have found that simple shapes may be diagnostic for emotional content and automatically alter the allocation of visual attention. In the present study, we analyzed whether color is diagnostic of emotional content and tested whether emotionally diagnostic hues alter the allocation of visual attention. Reddish-yellow hues are more common in (i.e., diagnostic of) emotional images, particularly images with positive emotional content. An exogenous cueing paradigm was employed to test whether these diagnostic hues orient attention differently from other hues due to the emotional diagnosticity. In two experiments, we found that participants allocated attention differently to diagnostic hues than to non-diagnostic hues, in a pattern indicating a broadening of spatial attention when cued with diagnostic hues. Moreover, the attentional broadening effect was predicted by self-reported measures of affective style, linking the behavioral effect to emotional processes. These results confirm the existence and use of diagnostic features for the rapid detection of emotional content. PMID:24659831

Mid Term Progress Report: Desertification Assessment and Monitoring in China Based on Remote Sensing

NASA Astrophysics Data System (ADS)

Gao, Zhihai; del Barrio, Gabriel; Li, Xiaosong; Wang, Bengyu; Puigdefabregas, Juan; Sanjuan, Maria E.; Bai, Lina; Wu, Junjun; Sun, Bin; Li, Changlong

2014-11-01

The objective of Dragon 3 Project 10367 is the development of techniques research for desertification assessment and monitoring in China using remote sensing data in combination with climate and environmental-related data. The main achievements acquired since2012could be summarized as follows: (1)Photosynthetic vegetation(PV)and non-photosynthetic vegetation(NPV)fraction were retrieved separately through utilizing Auto Monte Carlo Unmixing technique (AutoMCU), based on BJ-1 data and field measured spectral library. (2) The accuracy of sandy land classification was as high as81.52%when the object-oriented method and Support Vector Machine (SVM) classifiers were used. (3) A new Monthly net primary productivity (NPP)dataset from 2002 to 2010 for the whole China were established with Envisat-MERIS fraction of absorbed photosynthetically active radiation (FPAR) data. (4) The 2dRUE proved to be a good indicator for land degradation, based on which, land degradation status in the general potential extent of desertification in China(PEDC) was assessed preliminarily.

Mid Term Progress Report: Desertification Assessment and Monitoring in China Based on Remote Sensing

NASA Astrophysics Data System (ADS)

Gao, Zhihai; del Barrio, Gabriel; Li, Xiaosong; Wang, Wengyu; Puigdefabregas, Juan; Sanjuan, Maria E.; Bai, Lina; Wu, Junjun; Sun, Bin; Li, Changlong

2014-11-01

The objective of Dragon 3 Project 10367 is the development of techniques research for desertification assessment and monitoring in China using remote sensing data in combination with climate and environmental-related data. The main achievements acquired since 2012 could be summarized as follows:(1) Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) fraction were retrieved separately through utilizing Auto Monte Carlo Unmixing technique (AutoMCU), based on BJ-1 data and field measured spectral library.(2) The accuracy of sandy land classification was as high as 81.52% when the object-oriented method and Support Vector Machine (SVM) classifiers were used.(3) A new Monthly net primary productivity (NPP) dataset from 2002 to 2010 for the whole China were established with Envisat-MERIS fraction of absorbed photosynthetically active radiation (FPAR) data.(4) The 2dRUE proved to be a good indicator for land degradation, based on which, land degradation status in the general potential extent of desertification in China (PEDC) was assessed preliminarily.

Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

PubMed Central

Toure, Sory I.; Stow, Douglas A.; Weeks, John R.; Kumar, Sunil

2013-01-01

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution. PMID:24403648

Parallel-hierarchical processing and classification of laser beam profile images based on the GPU-oriented architecture

NASA Astrophysics Data System (ADS)

Yarovyi, Andrii A.; Timchenko, Leonid I.; Kozhemiako, Volodymyr P.; Kokriatskaia, Nataliya I.; Hamdi, Rami R.; Savchuk, Tamara O.; Kulyk, Oleksandr O.; Surtel, Wojciech; Amirgaliyev, Yedilkhan; Kashaganova, Gulzhan

2017-08-01

The paper deals with a problem of insufficient productivity of existing computer means for large image processing, which do not meet modern requirements posed by resource-intensive computing tasks of laser beam profiling. The research concentrated on one of the profiling problems, namely, real-time processing of spot images of the laser beam profile. Development of a theory of parallel-hierarchic transformation allowed to produce models for high-performance parallel-hierarchical processes, as well as algorithms and software for their implementation based on the GPU-oriented architecture using GPGPU technologies. The analyzed performance of suggested computerized tools for processing and classification of laser beam profile images allows to perform real-time processing of dynamic images of various sizes.

Keypoint Density-Based Region Proposal for Fine-Grained Object Detection and Classification Using Regions with Convolutional Neural Network Features

DTIC Science & Technology

2015-12-15

Keypoint Density-based Region Proposal for Fine-Grained Object Detection and Classification using Regions with Convolutional Neural Network ... Convolutional Neural Networks (CNNs) enable them to outperform conventional techniques on standard object detection and classification tasks, their...detection accuracy and speed on the fine-grained Caltech UCSD bird dataset (Wah et al., 2011). Recently, Convolutional Neural Networks (CNNs), a deep

Object-based classification of earthquake damage from high-resolution optical imagery using machine learning

NASA Astrophysics Data System (ADS)

Bialas, James; Oommen, Thomas; Rebbapragada, Umaa; Levin, Eugene

2016-07-01

Object-based approaches in the segmentation and classification of remotely sensed images yield more promising results compared to pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods are often used, but yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time-sensitive applications, such as earthquake damage assessment. Herein, we used a systematic approach in evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery. We tested a variety of algorithms and parameters on post-event aerial imagery for the 2011 earthquake in Christchurch, New Zealand. Results were compared against manually selected test cases representing different classes. In doing so, we can evaluate the effectiveness of the segmentation and classification of different classes and compare different levels of multistep image segmentations. Our classifier is compared against recent pixel-based and object-based classification studies for postevent imagery of earthquake damage. Our results show an improvement against both pixel-based and object-based methods for classifying earthquake damage in high resolution, post-event imagery.

Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex.

PubMed

Tong, Frank; Harrison, Stephenie A; Dewey, John A; Kamitani, Yukiyasu

2012-11-15

Orientation-selective responses can be decoded from fMRI activity patterns in the human visual cortex, using multivariate pattern analysis (MVPA). To what extent do these feature-selective activity patterns depend on the strength and quality of the sensory input, and might the reliability of these activity patterns be predicted by the gross amplitude of the stimulus-driven BOLD response? Observers viewed oriented gratings that varied in luminance contrast (4, 20 or 100%) or spatial frequency (0.25, 1.0 or 4.0 cpd). As predicted, activity patterns in early visual areas led to better discrimination of orientations presented at high than low contrast, with greater effects of contrast found in area V1 than in V3. A second experiment revealed generally better decoding of orientations at low or moderate as compared to high spatial frequencies. Interestingly however, V1 exhibited a relative advantage at discriminating high spatial frequency orientations, consistent with the finer scale of representation in the primary visual cortex. In both experiments, the reliability of these orientation-selective activity patterns was well predicted by the average BOLD amplitude in each region of interest, as indicated by correlation analyses, as well as decoding applied to a simple model of voxel responses to simulated orientation columns. Moreover, individual differences in decoding accuracy could be predicted by the signal-to-noise ratio of an individual's BOLD response. Our results indicate that decoding accuracy can be well predicted by incorporating the amplitude of the BOLD response into simple simulation models of cortical selectivity; such models could prove useful in future applications of fMRI pattern classification. Copyright © 2012 Elsevier Inc. All rights reserved.

Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex

PubMed Central

Tong, Frank; Harrison, Stephenie A.; Dewey, John A.; Kamitani, Yukiyasu

2012-01-01

Orientation-selective responses can be decoded from fMRI activity patterns in the human visual cortex, using multivariate pattern analysis (MVPA). To what extent do these feature-selective activity patterns depend on the strength and quality of the sensory input, and might the reliability of these activity patterns be predicted by the gross amplitude of the stimulus-driven BOLD response? Observers viewed oriented gratings that varied in luminance contrast (4, 20 or 100%) or spatial frequency (0.25, 1.0 or 4.0 cpd). As predicted, activity patterns in early visual areas led to better discrimination of orientations presented at high than low contrast, with greater effects of contrast found in area V1 than in V3. A second experiment revealed generally better decoding of orientations at low or moderate as compared to high spatial frequencies. Interestingly however, V1 exhibited a relative advantage at discriminating high spatial frequency orientations, consistent with the finer scale of representation in the primary visual cortex. In both experiments, the reliability of these orientation-selective activity patterns was well predicted by the average BOLD amplitude in each region of interest, as indicated by correlation analyses, as well as decoding applied to a simple model of voxel responses to simulated orientation columns. Moreover, individual differences in decoding accuracy could be predicted by the signal-to-noise ratio of an individual's BOLD response. Our results indicate that decoding accuracy can be well predicted by incorporating the amplitude of the BOLD response into simple simulation models of cortical selectivity; such models could prove useful in future applications of fMRI pattern classification. PMID:22917989

Object oriented development of engineering software using CLIPS

NASA Technical Reports Server (NTRS)

Yoon, C. John

1991-01-01

Engineering applications involve numeric complexity and manipulations of a large amount of data. Traditionally, numeric computation has been the concern in developing an engineering software. As engineering application software became larger and more complex, management of resources such as data, rather than the numeric complexity, has become the major software design problem. Object oriented design and implementation methodologies can improve the reliability, flexibility, and maintainability of the resulting software; however, some tasks are better solved with the traditional procedural paradigm. The C Language Integrated Production System (CLIPS), with deffunction and defgeneric constructs, supports the procedural paradigm. The natural blending of object oriented and procedural paradigms has been cited as the reason for the popularity of the C++ language. The CLIPS Object Oriented Language's (COOL) object oriented features are more versatile than C++'s. A software design methodology based on object oriented and procedural approaches appropriate for engineering software, and to be implemented in CLIPS was outlined. A method for sensor placement for Space Station Freedom is being implemented in COOL as a sample problem.

Object Based Image Analysis Combining High Spatial Resolution Imagery and Laser Point Clouds for Urban Land Cover

NASA Astrophysics Data System (ADS)

Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

2016-06-01

With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.

Three-quarter views are subjectively good because object orientation is uncertain.

PubMed

Niimi, Ryosuke; Yokosawa, Kazuhiko

2009-04-01

Because the objects that surround us are three-dimensional, their appearance and our visual perception of them change depending on an object's orientation relative to a viewpoint. One of the most remarkable effects of object orientation is that viewers prefer three-quarter views over others, such as front and back, but the exact source of this preference has not been firmly established. We show that object orientation perception of the three-quarter view is relatively imprecise and that this impreciseness is related to preference for this view. Human vision is largely insensitive to variations among different three-quarter views (e.g., 45 degrees vs. 50 degrees ); therefore, the three-quarter view is perceived as if it corresponds to a wide range of orientations. In other words, it functions as the typical representation of the object.

Object-oriented models of cognitive processing.

PubMed

Mather, G

2001-05-01

Information-processing models of vision and cognition are inspired by procedural programming languages. Models that emphasize object-based representations are closely related to object-oriented programming languages. The concepts underlying object-oriented languages provide a theoretical framework for cognitive processing that differs markedly from that offered by procedural languages. This framework is well-suited to a system designed to deal flexibly with discrete objects and unpredictable events in the world.

Fast and accurate edge orientation processing during object manipulation

PubMed Central

Flanagan, J Randall; Johansson, Roland S

2018-01-01

Quickly and accurately extracting information about a touched object’s orientation is a critical aspect of dexterous object manipulation. However, the speed and acuity of tactile edge orientation processing with respect to the fingertips as reported in previous perceptual studies appear inadequate in these respects. Here we directly establish the tactile system’s capacity to process edge-orientation information during dexterous manipulation. Participants extracted tactile information about edge orientation very quickly, using it within 200 ms of first touching the object. Participants were also strikingly accurate. With edges spanning the entire fingertip, edge-orientation resolution was better than 3° in our object manipulation task, which is several times better than reported in previous perceptual studies. Performance remained impressive even with edges as short as 2 mm, consistent with our ability to precisely manipulate very small objects. Taken together, our results radically redefine the spatial processing capacity of the tactile system. PMID:29611804

Information extraction with object based support vector machines and vegetation indices

NASA Astrophysics Data System (ADS)

Ustuner, Mustafa; Abdikan, Saygin; Balik Sanli, Fusun

2016-07-01

Information extraction through remote sensing data is important for policy and decision makers as extracted information provide base layers for many application of real world. Classification of remotely sensed data is the one of the most common methods of extracting information however it is still a challenging issue because several factors are affecting the accuracy of the classification. Resolution of the imagery, number and homogeneity of land cover classes, purity of training data and characteristic of adopted classifiers are just some of these challenging factors. Object based image classification has some superiority than pixel based classification for high resolution images since it uses geometry and structure information besides spectral information. Vegetation indices are also commonly used for the classification process since it provides additional spectral information for vegetation, forestry and agricultural areas. In this study, the impacts of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) on the classification accuracy of RapidEye imagery were investigated. Object based Support Vector Machines were implemented for the classification of crop types for the study area located in Aegean region of Turkey. Results demonstrated that the incorporation of NDRE increase the classification accuracy from 79,96% to 86,80% as overall accuracy, however NDVI decrease the classification accuracy from 79,96% to 78,90%. Moreover it is proven than object based classification with RapidEye data give promising results for crop type mapping and analysis.

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

PubMed

Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

2017-07-01

Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.

Exploring competitive orientation in a group of athletes participating in the 1996 paralympic trials.

PubMed

Page, S J

2000-10-01

The purposes of this study were to test (1) whether athletes with congenital disabilities exhibited different competitive orientations than athletes with disabilities acquired during their lifespans and (2) whether male athletes with disabilities exhibited different competitive orientations than their female peers. 54 paraplegic, quadriplegic and amputee athletes competing in the 1996 Paralympic Track and Field Trials completed the Sport Orientation Questionnaire. No mean differences were found between men and women, athletes with different onsets of their disabilities across the lifespan, between adolescents and adults, and between athletes with different severity classifications on the Goal orientation, Competitiveness, and Desire to win scales. Larger studies are encouraged to examine competitive orientation, as well as scores on tests specifically constructed to be administered to athletes with disabilities.

Teaching Adaptability of Object-Oriented Programming Language Curriculum

ERIC Educational Resources Information Center

Zhu, Xiao-dong

2012-01-01

The evolution of object-oriented programming languages includes update of their own versions, update of development environments, and reform of new languages upon old languages. In this paper, the evolution analysis of object-oriented programming languages is presented in term of the characters and development. The notion of adaptive teaching upon…

Object-oriented design for accelerator control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stok, P.D.V. van der; Berk, F. van den; Deckers, R.

1994-02-01

An object-oriented design for the distributed computer control system of the accelerator ring EUTERPE is presented. Because of the experimental nature of the ring, flexibility is of the utmost importance. The object-oriented principles have contributed considerably to the flexibility of the design incorporating multiple views, multi-level access and distributed surveillance.

75 FR 82128 - Culturally Significant Objects Imported for Exhibition Determinations: “The Orient Expressed...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-29

... DEPARTMENT OF STATE [Public Notice: 7277] Culturally Significant Objects Imported for Exhibition Determinations: ``The Orient Expressed: Japan's Influence on Western Art, 1854-1918'' SUMMARY: Notice is hereby... hereby determine that the objects to be included in the exhibition ``The Orient Expressed: Japan's...

Object-Oriented Dynamic Bayesian Network-Templates for Modelling Mechatronic Systems

DTIC Science & Technology

2002-05-04

daimlerchrysler.com Abstract are widespread. For modelling mechanical systems The object-oriented paradigma is a new but proven technol- ADAMS [31 or...hardware (sub-)systems. On the Software side thermal flow or hydraulics, see Figure 1. It also contains a the object-oriented paradigma is by now (at

Initiating Formal Requirements Specifications with Object-Oriented Models

NASA Technical Reports Server (NTRS)

Ampo, Yoko; Lutz, Robyn R.

1994-01-01

This paper reports results of an investigation into the suitability of object-oriented models as an initial step in developing formal specifications. The requirements for two critical system-level software modules were used as target applications. It was found that creating object-oriented diagrams prior to formally specifying the requirements enhanced the accuracy of the initial formal specifications and reduced the effort required to produce them. However, the formal specifications incorporated some information not found in the object-oriented diagrams, such as higher-level strategy or goals of the software.

Mapping forested wetlands in the Great Zhan River Basin through integrating optical, radar, and topographical data classification techniques.

PubMed

Na, X D; Zang, S Y; Wu, C S; Li, W L

2015-11-01

Knowledge of the spatial extent of forested wetlands is essential to many studies including wetland functioning assessment, greenhouse gas flux estimation, and wildlife suitable habitat identification. For discriminating forested wetlands from their adjacent land cover types, researchers have resorted to image analysis techniques applied to numerous remotely sensed data. While with some success, there is still no consensus on the optimal approaches for mapping forested wetlands. To address this problem, we examined two machine learning approaches, random forest (RF) and K-nearest neighbor (KNN) algorithms, and applied these two approaches to the framework of pixel-based and object-based classifications. The RF and KNN algorithms were constructed using predictors derived from Landsat 8 imagery, Radarsat-2 advanced synthetic aperture radar (SAR), and topographical indices. The results show that the objected-based classifications performed better than per-pixel classifications using the same algorithm (RF) in terms of overall accuracy and the difference of their kappa coefficients are statistically significant (p<0.01). There were noticeably omissions for forested and herbaceous wetlands based on the per-pixel classifications using the RF algorithm. As for the object-based image analysis, there were also statistically significant differences (p<0.01) of Kappa coefficient between results performed based on RF and KNN algorithms. The object-based classification using RF provided a more visually adequate distribution of interested land cover types, while the object classifications based on the KNN algorithm showed noticeably commissions for forested wetlands and omissions for agriculture land. This research proves that the object-based classification with RF using optical, radar, and topographical data improved the mapping accuracy of land covers and provided a feasible approach to discriminate the forested wetlands from the other land cover types in forestry area.

Solar system science with ESA Euclid

NASA Astrophysics Data System (ADS)

Carry, B.

2018-01-01

Context. The ESA Euclid mission has been designed to map the geometry of the dark Universe. Scheduled for launch in 2020, it will conduct a six-year visible and near-infrared imaging and spectroscopic survey over 15 000 deg2 down to VAB 24.5. Although the survey will avoid ecliptic latitudes below 15°, the survey pattern in repeated sequences of four broadband filters seems well-adapted to detect and characterize solar system objects (SSOs). Aims: We aim at evaluating the capability of Euclid of discovering SSOs and of measuring their position, apparent magnitude, and spectral energy distribution. We also investigate how the SSO orbits, morphology (activity and multiplicity), physical properties (rotation period, spin orientation, and 3D shape), and surface composition can be determined based on these measurements. Methods: We used the current census of SSOs to extrapolate the total amount of SSOs that will be detectable by Euclid, that is, objects within the survey area and brighter than the limiting magnitude. For each different population of SSO, from neighboring near-Earth asteroids to distant Kuiper-belt objects (KBOs) and including comets, we compared the expected Euclid astrometry, photometry, and spectroscopy with the SSO properties to estimate how Euclid will constrain the SSOs dynamical, physical, and compositional properties. Results: With the current survey design, about 150 000 SSOs, mainly from the asteroid main-belt, should be observable by Euclid. These objects will all have high inclination, which is a difference to many SSO surveys that focus on the ecliptic plane. Euclid may be able to discover several 104 SSOs, in particular, distant KBOs at high declination. The Euclid observations will consist of a suite of four sequences of four measurements and will refine the spectral classification of SSOs by extending the spectral coverage provided by Gaia and the LSST, for instance, to 2 microns. Combined with sparse photometry such as measured by Gaia and the LSST, the time-resolved photometry will contribute to determining the SSO rotation period, spin orientation, and 3D shape model. The sharp and stable point-spread function of Euclid will also allow us to resolve binary systems in the Kuiper belt and detect activity around Centaurs. Conclusions: The depth of the Euclid survey (VAB 24.5), its spectral coverage (0.5 to 2.0 μm), and its observation cadence has great potential for solar system research. A dedicated processing for SSOs is being set up within the Euclid consortium to produce astrometry catalogs, multicolor and time-resolved photometry, and spectral classification of some 105 SSOs, which will be delivered as Legacy Science.

An Assessment of GEO Orbital Debris Photometric Properties Derived from Laboratory-Based Measurements

NASA Astrophysics Data System (ADS)

Cowardin, H.; Abercromby, K.; Barker, E.; Seitzer, P.; Mulrooney, M.; Schildknecht, T.

Optical observations of orbital debris offer insights that differ from radar measurements (specifically the size parameter and wavelength regime). For example, time-dependent photometric data yield lightcurves in multiple bandpasses that aid in material identification and possible periodic orientations. This data can also be used to help identify shapes and optical properties at multiple phase angles. Capitalizing on optical data products and applying them to generate a more complete understanding of orbital space objects, is a key objective of NASA's Optical Measurement Program, and a primary driver for creation of the Optical Measurements Center (OMC). The OMC attempts to emulate space-based illumination conditions using equipment and techniques that parallel telescopic observations and source-target-sensor orientations. The OMC uses a 300 Watt Xenon arc lamp as a solar simulator, a CCD camera with Johnson/Bessel colored filters, and a robotic arm to orientate/rotate objects to simulate an objects orbit/rotational period. A high-resolution, high bandwidth (350nm-2500nm) Analytical Spectral Devices (ASD) spectrometer is also employed to baseline various material types. Since observation of GEO targets are generally restricted to the optical regime (due to radar range limitations), analysis of their properties is tailored to those revealed by optical data products. In this connection, much attention has been directed towards understanding the lightcurves of orbital debris with high area-to-mass (A/m) ratios (> 0.9 m2/kg). A small population of GEO debris was recently identified, which exhibits the properties of high A/m objects, such as variable eccentricities and inclinations -- a dynamical characteristic generally resulting from varying solar radiation pressure on high A/m objects. Materials such as multi-layered insulation (MLI) and solar panels are two examples of materials with high area-to mass ratios. Lightcurves for such objects can vary greatly (even for the same object under different illumination conditions). For example, specular reflections from multiple facets of the target surface (e.g. Mylar or Aluminized Kapton) can lead to erratic, orientation-dependent lightcurves. This paper will investigate published color photometric data for a series of orbital debris targets and compare it to the empirical photometric measurements generated in the OMC. The specific materials investigated (known to exist in GEO) are: an intact piece of MLI, separated layers of MLI, and multiple solar cells materials. Using the data acquired over specific rotational angles through different filters (B, V, R, I), a color index is acquired (B-R, R-I). As a secondary check, the spectrometer is used to define color indexes for the same material. Using these values and their associated lightcurves, this laboratory data is compared to observational data obtained on the 1m telescope of the Astronomical Institute of the University of Bern (AUIB) and the 0.9 m Small and Moderate Aperture Research Telescope System (SMARTS) telescope at Cerro Tololo Inter-American Observatory (CTIO). We will present laboratory generated lightcurves with color indexes of the high A/m materials alongside telescopic data of targets with high A/m values. We will discuss the relationship of laboratory to telescope data in the context of classification of GEO debris objects.

Representing object oriented specifications and designs with extended data flow notations

NASA Technical Reports Server (NTRS)

Buser, Jon Franklin; Ward, Paul T.

1988-01-01

The issue of using extended data flow notations to document object oriented designs and specifications is discussed. Extended data flow notations, for the purposes here, refer to notations that are based on the rules of Yourdon/DeMarco data flow analysis. The extensions include additional notation for representing real-time systems as well as some proposed extensions specific to object oriented development. Some advantages of data flow notations are stated. How data flow diagrams are used to represent software objects are investigated. Some problem areas with regard to using data flow notations for object oriented development are noted. Some initial solutions to these problems are proposed.

Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor.

PubMed

Prasanna, Prateek; Tiwari, Pallavi; Madabhushi, Anant

2016-11-22

In this paper, we introduce a new radiomic descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) for capturing subtle differences between benign and pathologic phenotypes which may be visually indistinguishable on routine anatomic imaging. CoLlAGe seeks to capture and exploit local anisotropic differences in voxel-level gradient orientations to distinguish similar appearing phenotypes. CoLlAGe involves assigning every image voxel an entropy value associated with the co-occurrence matrix of gradient orientations computed around every voxel. The hypothesis behind CoLlAGe is that benign and pathologic phenotypes even though they may appear similar on anatomic imaging, will differ in their local entropy patterns, in turn reflecting subtle local differences in tissue microarchitecture. We demonstrate CoLlAGe's utility in three clinically challenging classification problems: distinguishing (1) radiation necrosis, a benign yet confounding effect of radiation treatment, from recurrent tumors on T1-w MRI in 42 brain tumor patients, (2) different molecular sub-types of breast cancer on DCE-MRI in 65 studies and (3) non-small cell lung cancer (adenocarcinomas) from benign fungal infection (granulomas) on 120 non-contrast CT studies. For each of these classification problems, CoLlAGE in conjunction with a random forest classifier outperformed state of the art radiomic descriptors (Haralick, Gabor, Histogram of Gradient Orientations).

Determining Object Orientation from a Single Image Using Multiple Information Sources.

DTIC Science & Technology

1984-06-01

object surface. Location of the image ellipse is accomplished by exploiting knowledge about object boundaries and image intensity gradients . -. The...Using Intensity Gradient Information for Ellipse fitting ........ .51 4.3.7 Orientation From Ellipses .............................. 53 4.3.8 Application...object boundaries and image intensity gradients . The orientation information from each of these three methods is combined using a "plausibility" function

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

PubMed

S K, Somasundaram; P, Alli

2017-11-09

The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed. Recently, few research works have been designed for analyzing texture discrimination capacity in FI to distinguish the healthy images. However, the feature extraction (FE) process was not performed well, due to the high dimensionality. Therefore, to identify retinal features for DR disease diagnosis and early detection using Machine Learning and Ensemble Classification method, called, Machine Learning Bagging Ensemble Classifier (ML-BEC) is designed. The ML-BEC method comprises of two stages. The first stage in ML-BEC method comprises extraction of the candidate objects from Retinal Images (RI). The candidate objects or the features for DR disease diagnosis include blood vessels, optic nerve, neural tissue, neuroretinal rim, optic disc size, thickness and variance. These features are initially extracted by applying Machine Learning technique called, t-distributed Stochastic Neighbor Embedding (t-SNE). Besides, t-SNE generates a probability distribution across high-dimensional images where the images are separated into similar and dissimilar pairs. Then, t-SNE describes a similar probability distribution across the points in the low-dimensional map. This lessens the Kullback-Leibler divergence among two distributions regarding the locations of the points on the map. The second stage comprises of application of ensemble classifiers to the extracted features for providing accurate analysis of digital FI using machine learning. In this stage, an automatic detection of DR screening system using Bagging Ensemble Classifier (BEC) is investigated. With the help of voting the process in ML-BEC, bagging minimizes the error due to variance of the base classifier. With the publicly available retinal image databases, our classifier is trained with 25% of RI. Results show that the ensemble classifier can achieve better classification accuracy (CA) than single classification models. Empirical experiments suggest that the machine learning-based ensemble classifier is efficient for further reducing DR classification time (CT).

Two-tier tissue decomposition for histopathological image representation and classification.

PubMed

Gultekin, Tunc; Koyuncu, Can Fahrettin; Sokmensuer, Cenk; Gunduz-Demir, Cigdem

2015-01-01

In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly using image pixels, since a histopathological image may contain a considerable amount of noise typically at the pixel-level. These previous studies mostly employ color information to define their objects, which approximately represent histological tissue components in an image, and then use the spatial distribution of these objects for image representation and classification. Thus, object definition has a direct effect on the way of representing the image, which in turn affects classification accuracies. In this paper, our aim is to design a classification system for histopathological images. Towards this end, we present a new model for effective representation of these images that will be used by the classification system. The contributions of this model are twofold. First, it introduces a new two-tier tissue decomposition method for defining a set of multityped objects in an image. Different than the previous studies, these objects are defined combining texture, shape, and size information and they may correspond to individual histological tissue components as well as local tissue subregions of different characteristics. As its second contribution, it defines a new metric, which we call dominant blob scale, to characterize the shape and size of an object with a single scalar value. Our experiments on colon tissue images reveal that this new object definition and characterization provides distinguishing representation of normal and cancerous histopathological images, which is effective to obtain more accurate classification results compared to its counterparts.

Contour symmetry detection: the influence of axis orientation and number of objects.

PubMed

Friedenberg, J; Bertamini, M

2000-09-01

Participants discriminated symmetrical from random contours connected by straight lines to form part of one- or two-objects. In experiment one, symmetrical contours were translated or reflected and presented at vertical, horizontal, and oblique axis orientations with orientation constant within blocks. Translated two-object contours were detected more easily than one, replicating a "lock-and-key" effect obtained previously for vertical orientations only [M. Bertamini, J.D. Friedenberg, M. Kubovy, Acta Psychologica, 95 (1997) 119-140]. A second experiment extended these results to a wider variety of axis orientations under mixed block conditions. The pattern of performance for translation and reflection at different orientations corresponded in both experiments, suggesting that orientation is processed similarly in the detection of these symmetries.

Towards a general object-oriented software development methodology

NASA Technical Reports Server (NTRS)

Seidewitz, ED; Stark, Mike

1986-01-01

Object diagrams were used to design a 5000 statement team training exercise and to design the entire dynamics simulator. The object diagrams are also being used to design another 50,000 statement Ada system and a personal computer based system that will be written in Modula II. The design methodology evolves out of these experiences as well as the limitations of other methods that were studied. Object diagrams, abstraction analysis, and associated principles provide a unified framework which encompasses concepts from Yourdin, Booch, and Cherry. This general object-oriented approach handles high level system design, possibly with concurrency, through object-oriented decomposition down to a completely functional level. How object-oriented concepts can be used in other phases of the software life-cycle, such as specification and testing is being studied concurrently.

Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography

NASA Astrophysics Data System (ADS)

Li, Xiaoxiao; Myint, Soe W.; Zhang, Yujia; Galletti, Chritopher; Zhang, Xiaoxiang; Turner, Billie L.

2014-12-01

Detailed land-cover mapping is essential for a range of research issues addressed by the sustainability and land system sciences and planning. This study uses an object-based approach to create a 1 m land-cover classification map of the expansive Phoenix metropolitan area through the use of high spatial resolution aerial photography from National Agricultural Imagery Program. It employs an expert knowledge decision rule set and incorporates the cadastral GIS vector layer as auxiliary data. The classification rule was established on a hierarchical image object network, and the properties of parcels in the vector layer were used to establish land cover types. Image segmentations were initially utilized to separate the aerial photos into parcel sized objects, and were further used for detailed land type identification within the parcels. Characteristics of image objects from contextual and geometrical aspects were used in the decision rule set to reduce the spectral limitation of the four-band aerial photography. Classification results include 12 land-cover classes and subclasses that may be assessed from the sub-parcel to the landscape scales, facilitating examination of scale dynamics. The proposed object-based classification method provides robust results, uses minimal and readily available ancillary data, and reduces computational time.

Face Verification across Age Progression using Discriminative Methods

DTIC Science & Technology

2008-01-01

progression. The most related study to our work is [30], where the probabilistic eigenspace frame - work [22] is adapted for face identification across...solution has the same CAR and CRR, is frequently used to measure verification performance, B. Gradient Orientation and Gradient Orientation Pyramid Now we...proposed GOP representation. The other five approaches are different from our method in both representations and classification frame - works. For

Orientation congruency effects for familiar objects: coordinate transformations in object recognition.

PubMed

Graf, M; Kaping, D; Bülthoff, H H

2005-03-01

How do observers recognize objects after spatial transformations? Recent neurocomputational models have proposed that object recognition is based on coordinate transformations that align memory and stimulus representations. If the recognition of a misoriented object is achieved by adjusting a coordinate system (or reference frame), then recognition should be facilitated when the object is preceded by a different object in the same orientation. In the two experiments reported here, two objects were presented in brief masked displays that were in close temporal contiguity; the objects were in either congruent or incongruent picture-plane orientations. Results showed that naming accuracy was higher for congruent than for incongruent orientations. The congruency effect was independent of superordinate category membership (Experiment 1) and was found for objects with different main axes of elongation (Experiment 2). The results indicate congruency effects for common familiar objects even when they have dissimilar shapes. These findings are compatible with models in which object recognition is achieved by an adjustment of a perceptual coordinate system.

Mirror-Image Confusions: Implications for Representation and Processing of Object Orientation

ERIC Educational Resources Information Center

Gregory, Emma; McCloskey, Michael

2010-01-01

Perceiving the orientation of objects is important for interacting with the world, yet little is known about the mental representation or processing of object orientation information. The tendency of humans and other species to confuse mirror images provides a potential clue. However, the appropriate characterization of this phenomenon is not…

Advanced software development workstation: Object-oriented methodologies and applications for flight planning and mission operations

NASA Technical Reports Server (NTRS)

Izygon, Michel

1993-01-01

The work accomplished during the past nine months in order to help three different organizations involved in Flight Planning and in Mission Operations systems, to transition to Object-Oriented Technology, by adopting one of the currently most widely used Object-Oriented analysis and Design Methodology is summarized.

Developing Formal Object-oriented Requirements Specifications: A Model, Tool and Technique.

ERIC Educational Resources Information Center

Jackson, Robert B.; And Others

1995-01-01

Presents a formal object-oriented specification model (OSS) for computer software system development that is supported by a tool that automatically generates a prototype from an object-oriented analysis model (OSA) instance, lets the user examine the prototype, and permits the user to refine the OSA model instance to generate a requirements…

Object-Oriented Implementation of the NAS Parallel Benchmarks using Charm++

NASA Technical Reports Server (NTRS)

Krishnan, Sanjeev; Bhandarkar, Milind; Kale, Laxmikant V.

1996-01-01

This report describes experiences with implementing the NAS Computational Fluid Dynamics benchmarks using a parallel object-oriented language, Charm++. Our main objective in implementing the NAS CFD kernel benchmarks was to develop a code that could be used to easily experiment with different domain decomposition strategies and dynamic load balancing. We also wished to leverage the object-orientation provided by the Charm++ parallel object-oriented language, to develop reusable abstractions that would simplify the process of developing parallel applications. We first describe the Charm++ parallel programming model and the parallel object array abstraction, then go into detail about each of the Scalar Pentadiagonal (SP) and Lower/Upper Triangular (LU) benchmarks, along with performance results. Finally we conclude with an evaluation of the methodology used.

Classification of subsurface objects using singular values derived from signal frames

DOEpatents

Chambers, David H; Paglieroni, David W

2014-05-06

The classification system represents a detected object with a feature vector derived from the return signals acquired by an array of N transceivers operating in multistatic mode. The classification system generates the feature vector by transforming the real-valued return signals into complex-valued spectra, using, for example, a Fast Fourier Transform. The classification system then generates a feature vector of singular values for each user-designated spectral sub-band by applying a singular value decomposition (SVD) to the N.times.N square complex-valued matrix formed from sub-band samples associated with all possible transmitter-receiver pairs. The resulting feature vector of singular values may be transformed into a feature vector of singular value likelihoods and then subjected to a multi-category linear or neural network classifier for object classification.

Integration of the Gene Ontology into an object-oriented architecture.

PubMed

Shegogue, Daniel; Zheng, W Jim

2005-05-10

To standardize gene product descriptions, a formal vocabulary defined as the Gene Ontology (GO) has been developed. GO terms have been categorized into biological processes, molecular functions, and cellular components. However, there is no single representation that integrates all the terms into one cohesive model. Furthermore, GO definitions have little information explaining the underlying architecture that forms these terms, such as the dynamic and static events occurring in a process. In contrast, object-oriented models have been developed to show dynamic and static events. A portion of the TGF-beta signaling pathway, which is involved in numerous cellular events including cancer, differentiation and development, was used to demonstrate the feasibility of integrating the Gene Ontology into an object-oriented model. Using object-oriented models we have captured the static and dynamic events that occur during a representative GO process, "transforming growth factor-beta (TGF-beta) receptor complex assembly" (GO:0007181). We demonstrate that the utility of GO terms can be enhanced by object-oriented technology, and that the GO terms can be integrated into an object-oriented model by serving as a basis for the generation of object functions and attributes.

Integration of the Gene Ontology into an object-oriented architecture

PubMed Central

Shegogue, Daniel; Zheng, W Jim

2005-01-01

Background To standardize gene product descriptions, a formal vocabulary defined as the Gene Ontology (GO) has been developed. GO terms have been categorized into biological processes, molecular functions, and cellular components. However, there is no single representation that integrates all the terms into one cohesive model. Furthermore, GO definitions have little information explaining the underlying architecture that forms these terms, such as the dynamic and static events occurring in a process. In contrast, object-oriented models have been developed to show dynamic and static events. A portion of the TGF-beta signaling pathway, which is involved in numerous cellular events including cancer, differentiation and development, was used to demonstrate the feasibility of integrating the Gene Ontology into an object-oriented model. Results Using object-oriented models we have captured the static and dynamic events that occur during a representative GO process, "transforming growth factor-beta (TGF-beta) receptor complex assembly" (GO:0007181). Conclusion We demonstrate that the utility of GO terms can be enhanced by object-oriented technology, and that the GO terms can be integrated into an object-oriented model by serving as a basis for the generation of object functions and attributes. PMID:15885145

A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments

NASA Astrophysics Data System (ADS)

Li, Manchun; Ma, Lei; Blaschke, Thomas; Cheng, Liang; Tiede, Dirk

2016-07-01

Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of such supervised classification techniques in GEOBIA. However, these studies did not systematically investigate all relevant factors affecting the classification (segmentation scale, training set size, feature selection and mixed objects). In this study, statistical methods and visual inspection were used to compare these factors systematically in two agricultural case studies in China. The results indicate that Random Forest (RF) and Support Vector Machines (SVM) are highly suitable for GEOBIA classifications in agricultural areas and confirm the expected general tendency, namely that the overall accuracies decline with increasing segmentation scale. All other investigated methods except for RF and SVM are more prone to obtain a lower accuracy due to the broken objects at fine scales. In contrast to some previous studies, the RF classifiers yielded the best results and the k-nearest neighbor classifier were the worst results, in most cases. Likewise, the RF and Decision Tree classifiers are the most robust with or without feature selection. The results of training sample analyses indicated that the RF and adaboost. M1 possess a superior generalization capability, except when dealing with small training sample sizes. Furthermore, the classification accuracies were directly related to the homogeneity/heterogeneity of the segmented objects for all classifiers. Finally, it was suggested that RF should be considered in most cases for agricultural mapping.

Significance of clustering and classification applications in digital and physical libraries

NASA Astrophysics Data System (ADS)

Triantafyllou, Ioannis; Koulouris, Alexandros; Zervos, Spiros; Dendrinos, Markos; Giannakopoulos, Georgios

2015-02-01

Applications of clustering and classification techniques can be proved very significant in both digital and physical (paper-based) libraries. The most essential application, document classification and clustering, is crucial for the content that is produced and maintained in digital libraries, repositories, databases, social media, blogs etc., based on various tags and ontology elements, transcending the traditional library-oriented classification schemes. Other applications with very useful and beneficial role in the new digital library environment involve document routing, summarization and query expansion. Paper-based libraries can benefit as well since classification combined with advanced material characterization techniques such as FTIR (Fourier Transform InfraRed spectroscopy) can be vital for the study and prevention of material deterioration. An improved two-level self-organizing clustering architecture is proposed in order to enhance the discrimination capacity of the learning space, prior to classification, yielding promising results when applied to the above mentioned library tasks.

C++, objected-oriented programming, and astronomical data models

NASA Technical Reports Server (NTRS)

Farris, A.

1992-01-01

Contemporary astronomy is characterized by increasingly complex instruments and observational techniques, higher data collection rates, and large data archives, placing severe stress on software analysis systems. The object-oriented paradigm represents a significant new approach to software design and implementation that holds great promise for dealing with this increased complexity. The basic concepts of this approach will be characterized in contrast to more traditional procedure-oriented approaches. The fundamental features of objected-oriented programming will be discussed from a C++ programming language perspective, using examples familiar to astronomers. This discussion will focus on objects, classes and their relevance to the data type system; the principle of information hiding; and the use of inheritance to implement generalization/specialization relationships. Drawing on the object-oriented approach, features of a new database model to support astronomical data analysis will be presented.

Object Oriented Learning Objects

ERIC Educational Resources Information Center

Morris, Ed

2005-01-01

We apply the object oriented software engineering (OOSE) design methodology for software objects (SOs) to learning objects (LOs). OOSE extends and refines design principles for authoring dynamic reusable LOs. Our learning object class (LOC) is a template from which individualised LOs can be dynamically created for, or by, students. The properties…

A physical classification scheme for blazars

NASA Astrophysics Data System (ADS)

Landt, Hermine; Padovani, Paolo; Perlman, Eric S.; Giommi, Paolo

2004-06-01

Blazars are currently separated into BL Lacertae objects (BL Lacs) and flat spectrum radio quasars based on the strength of their emission lines. This is performed rather arbitrarily by defining a diagonal line in the Ca H&K break value-equivalent width plane, following Marchã et al. We readdress this problem and put the classification scheme for blazars on firm physical grounds. We study ~100 blazars and radio galaxies from the Deep X-ray Radio Blazar Survey (DXRBS) and 2-Jy radio survey and find a significant bimodality for the narrow emission line [OIII]λ5007. This suggests the presence of two physically distinct classes of radio-loud active galactic nuclei (AGN). We show that all radio-loud AGN, blazars and radio galaxies, can be effectively separated into weak- and strong-lined sources using the [OIII]λ5007-[OII]λ3727 equivalent width plane. This plane allows one to disentangle orientation effects from intrinsic variations in radio-loud AGN. Based on DXRBS, the strongly beamed sources of the new class of weak-lined radio-loud AGN are made up of BL Lacs at the ~75 per cent level, whereas those of the strong-lined radio-loud AGN include mostly (~97 per cent) quasars.

[Analgesia in intensive care medicine].

PubMed

Ortlepp, J R; Luethje, F; Walz, R

2016-02-01

The administration of sedatives and analgesics on the intensive care unit (ICU) is routine daily practice. The correct discrimination between delirium, pain and anxiety or confusion is essential for the strategy and selection of medication. The correct pain therapy and sedation are essential for patient quality of life on the ICU and for the prognosis. The aim of this article is to present state of the art recommendations on the classification of pain and pain therapy on the ICU. An online search was carried out in PubMed for publications on the topics of "pain" and "ICU". Critical care patients are frequently subjected to many procedures and situations which can cause pain. The perception of pain is, among other things, influenced by the degree of orientation, anxiety and the degree of sedation. The administration of analgesics and non-pharmacological approaches are effective in reducing the stress perceived by patients. The main aim is improvement in the awareness of nursing and medical personnel for pain inducers and pain perception in ICU patients. The classification of pain must be made objectively. Therapeutic targets must be defined and in addition to the correct selection of pain medication, non-pharmacological approaches must also be consistently implemented.

Use of Binary Partition Tree and energy minimization for object-based classification of urban land cover

NASA Astrophysics Data System (ADS)

Li, Mengmeng; Bijker, Wietske; Stein, Alfred

2015-04-01

Two main challenges are faced when classifying urban land cover from very high resolution satellite images: obtaining an optimal image segmentation and distinguishing buildings from other man-made objects. For optimal segmentation, this work proposes a hierarchical representation of an image by means of a Binary Partition Tree (BPT) and an unsupervised evaluation of image segmentations by energy minimization. For building extraction, we apply fuzzy sets to create a fuzzy landscape of shadows which in turn involves a two-step procedure. The first step is a preliminarily image classification at a fine segmentation level to generate vegetation and shadow information. The second step models the directional relationship between building and shadow objects to extract building information at the optimal segmentation level. We conducted the experiments on two datasets of Pléiades images from Wuhan City, China. To demonstrate its performance, the proposed classification is compared at the optimal segmentation level with Maximum Likelihood Classification and Support Vector Machine classification. The results show that the proposed classification produced the highest overall accuracies and kappa coefficients, and the smallest over-classification and under-classification geometric errors. We conclude first that integrating BPT with energy minimization offers an effective means for image segmentation. Second, we conclude that the directional relationship between building and shadow objects represented by a fuzzy landscape is important for building extraction.

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

PubMed Central

Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, UK Arvind

2016-01-01

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available. PMID:26513781

An outline of object-oriented philosophy.

PubMed

Harman, Graham

2013-01-01

This article summarises the principles of object-oriented philosophy and explains its similarities with, and differences from, the outlook of the natural sciences. Like science, the object-oriented position avoids the notion (quite common in philosophy) that the human-world relation is the ground of all others, such that scientific statements about the world would only be statements about the world as it is for humans. But unlike science, object-oriented metaphysics treats artificial, social, and fictional entities in the same way as natural ones, and also holds that the world can only be known allusively rather than directly.

Diagnostic Classification Models and Multidimensional Adaptive Testing: A Commentary on Rupp and Templin

ERIC Educational Resources Information Center

Frey, Andreas; Carstensen, Claus H.

2009-01-01

On a general level, the objective of diagnostic classifications models (DCMs) lies in a classification of individuals regarding multiple latent skills. In this article, the authors show that this objective can be achieved by multidimensional adaptive testing (MAT) as well. The authors discuss whether or not the restricted applicability of DCMs can…

Concepts of Classification and Taxonomy Phylogenetic Classification

NASA Astrophysics Data System (ADS)

Fraix-Burnet, D.

2016-05-01

Phylogenetic approaches to classification have been heavily developed in biology by bioinformaticians. But these techniques have applications in other fields, in particular in linguistics. Their main characteristics is to search for relationships between the objects or species in study, instead of grouping them by similarity. They are thus rather well suited for any kind of evolutionary objects. For nearly fifteen years, astrocladistics has explored the use of Maximum Parsimony (or cladistics) for astronomical objects like galaxies or globular clusters. In this lesson we will learn how it works.

Karl Heinrich Ulrichs: First Theorist of Erotic Age Orientation.

PubMed

Janssen, Diederik F

2017-01-01

The nomination of Karl Heinrich Ulrichs (1825-1895) as the first theorist of homosexuality may be placed in the oblique light of his eligibility for the nomination as the first theorist of erotic age orientation. In Ulrichs's pamphlets, "man-manly" homosexuality emerged as a particular age orientation, with a subsequent typological breakdown that, importantly, blended gender orientation and age orientation. Into the early 20th century, erotic age orientation remained bound up with the classification and emancipation of what here was demarcated as Urningsliebe. Ulrichs's pioneering and shifting comments on age eventually fed into his legal model of consenting adults in private. They also provide a starting point for the historical understanding of the trope of "grooming pedophile" as it, arguably, crossfaded with that of the "seducing homosexual" after the latter's depsychiatricization across the Western world.

The Dynamo package for tomography and subtomogram averaging: components for MATLAB, GPU computing and EC2 Amazon Web Services

PubMed Central

Castaño-Díez, Daniel

2017-01-01

Dynamo is a package for the processing of tomographic data. As a tool for subtomogram averaging, it includes different alignment and classification strategies. Furthermore, its data-management module allows experiments to be organized in groups of tomograms, while offering specialized three-dimensional tomographic browsers that facilitate visualization, location of regions of interest, modelling and particle extraction in complex geometries. Here, a technical description of the package is presented, focusing on its diverse strategies for optimizing computing performance. Dynamo is built upon mbtools (middle layer toolbox), a general-purpose MATLAB library for object-oriented scientific programming specifically developed to underpin Dynamo but usable as an independent tool. Its structure intertwines a flexible MATLAB codebase with precompiled C++ functions that carry the burden of numerically intensive operations. The package can be delivered as a precompiled standalone ready for execution without a MATLAB license. Multicore parallelization on a single node is directly inherited from the high-level parallelization engine provided for MATLAB, automatically imparting a balanced workload among the threads in computationally intense tasks such as alignment and classification, but also in logistic-oriented tasks such as tomogram binning and particle extraction. Dynamo supports the use of graphical processing units (GPUs), yielding considerable speedup factors both for native Dynamo procedures (such as the numerically intensive subtomogram alignment) and procedures defined by the user through its MATLAB-based GPU library for three-dimensional operations. Cloud-based virtual computing environments supplied with a pre-installed version of Dynamo can be publicly accessed through the Amazon Elastic Compute Cloud (EC2), enabling users to rent GPU computing time on a pay-as-you-go basis, thus avoiding upfront investments in hardware and longterm software maintenance. PMID:28580909

The Dynamo package for tomography and subtomogram averaging: components for MATLAB, GPU computing and EC2 Amazon Web Services.

PubMed

Castaño-Díez, Daniel

2017-06-01

Dynamo is a package for the processing of tomographic data. As a tool for subtomogram averaging, it includes different alignment and classification strategies. Furthermore, its data-management module allows experiments to be organized in groups of tomograms, while offering specialized three-dimensional tomographic browsers that facilitate visualization, location of regions of interest, modelling and particle extraction in complex geometries. Here, a technical description of the package is presented, focusing on its diverse strategies for optimizing computing performance. Dynamo is built upon mbtools (middle layer toolbox), a general-purpose MATLAB library for object-oriented scientific programming specifically developed to underpin Dynamo but usable as an independent tool. Its structure intertwines a flexible MATLAB codebase with precompiled C++ functions that carry the burden of numerically intensive operations. The package can be delivered as a precompiled standalone ready for execution without a MATLAB license. Multicore parallelization on a single node is directly inherited from the high-level parallelization engine provided for MATLAB, automatically imparting a balanced workload among the threads in computationally intense tasks such as alignment and classification, but also in logistic-oriented tasks such as tomogram binning and particle extraction. Dynamo supports the use of graphical processing units (GPUs), yielding considerable speedup factors both for native Dynamo procedures (such as the numerically intensive subtomogram alignment) and procedures defined by the user through its MATLAB-based GPU library for three-dimensional operations. Cloud-based virtual computing environments supplied with a pre-installed version of Dynamo can be publicly accessed through the Amazon Elastic Compute Cloud (EC2), enabling users to rent GPU computing time on a pay-as-you-go basis, thus avoiding upfront investments in hardware and longterm software maintenance.

Secure Access Control and Large Scale Robust Representation for Online Multimedia Event Detection

PubMed Central

Liu, Changyu; Li, Huiling

2014-01-01

We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches. PMID:25147840

Disorders related to sexuality and gender identity in the ICD‐11: revising the ICD‐10 classification based on current scientific evidence, best clinical practices, and human rights considerations

PubMed Central

Reed, Geoffrey M.; Drescher, Jack; Krueger, Richard B.; Atalla, Elham; Cochran, Susan D.; First, Michael B.; Cohen‐Kettenis, Peggy T.; Arango‐de Montis, Iván; Parish, Sharon J.; Cottler, Sara; Briken, Peer; Saxena, Shekhar

2016-01-01

In the World Health Organization's forthcoming eleventh revision of the International Classification of Diseases and Related Health Problems (ICD‐11), substantial changes have been proposed to the ICD‐10 classification of mental and behavioural disorders related to sexuality and gender identity. These concern the following ICD‐10 disorder groupings: F52 Sexual dysfunctions, not caused by organic disorder or disease; F64 Gender identity disorders; F65 Disorders of sexual preference; and F66 Psychological and behavioural disorders associated with sexual development and orientation. Changes have been proposed based on advances in research and clinical practice, and major shifts in social attitudes and in relevant policies, laws, and human rights standards. This paper describes the main recommended changes, the rationale and evidence considered, and important differences from the DSM‐5. An integrated classification of sexual dysfunctions has been proposed for a new chapter on Conditions Related to Sexual Health, overcoming the mind/body separation that is inherent in ICD‐10. Gender identity disorders in ICD‐10 have been reconceptualized as Gender incongruence, and also proposed to be moved to the new chapter on sexual health. The proposed classification of Paraphilic disorders distinguishes between conditions that are relevant to public health and clinical psychopathology and those that merely reflect private behaviour. ICD‐10 categories related to sexual orientation have been recommended for deletion from the ICD‐11. PMID:27717275

Remote imagery for unmanned ground vehicles: the future of path planning for ground robotics

NASA Astrophysics Data System (ADS)

Frederick, Philip A.; Theisen, Bernard L.; Ward, Derek

2006-10-01

Remote Imagery for Unmanned Ground Vehicles (RIUGV) uses a combination of high-resolution multi-spectral satellite imagery and advanced commercial off-the-self (COTS) object-oriented image processing software to provide automated terrain feature extraction and classification. This information, along with elevation data, infrared imagery, a vehicle mobility model and various meta-data (local weather reports, Zobler Soil map, etc...), is fed into automated path planning software to provide a stand-alone ability to generate rapidly updateable dynamic mobility maps for Manned or Unmanned Ground Vehicles (MGVs or UGVs). These polygon based mobility maps can reside on an individual platform or a tactical network. When new information is available, change files are generated and ingested into existing mobility maps based on user selected criteria. Bandwidth concerns are mitigated by the use of shape files for the representation of the data (e.g. each object in the scene is represented by a shape file and thus can be transmitted individually). User input (desired level of stealth, required time of arrival, etc...) determines the priority in which objects are tagged for updates. This paper will also discuss the planned July 2006 field experiment.

Aspects on Teaching/Learning with Object Oriented Programming for Entry Level Courses of Engineering.

ERIC Educational Resources Information Center

de Oliveira, Clara Amelia; Conte, Marcos Fernando; Riso, Bernardo Goncalves

This work presents a proposal for Teaching/Learning, on Object Oriented Programming for Entry Level Courses of Engineering and Computer Science, on University. The philosophy of Object Oriented Programming comes as a new pattern of solution for problems, where flexibility and reusability appears over the simple data structure and sequential…

Gauging Spatial Symmetries and the Classification of Topological Crystalline Phases

NASA Astrophysics Data System (ADS)

Thorngren, Ryan; Else, Dominic V.

2018-01-01

We put the theory of interacting topological crystalline phases on a systematic footing. These are topological phases protected by space-group symmetries. Our central tool is an elucidation of what it means to "gauge" such symmetries. We introduce the notion of a crystalline topological liquid and argue that most (and perhaps all) phases of interest are likely to satisfy this criterion. We prove a crystalline equivalence principle, which states that in Euclidean space, crystalline topological liquids with symmetry group G are in one-to-one correspondence with topological phases protected by the same symmetry G , but acting internally, where if an element of G is orientation reversing, it is realized as an antiunitary symmetry in the internal symmetry group. As an example, we explicitly compute, using group cohomology, a partial classification of bosonic symmetry-protected topological phases protected by crystalline symmetries in (3 +1 ) dimensions for 227 of the 230 space groups. For the 65 space groups not containing orientation-reversing elements (Sohncke groups), there are no cobordism invariants that may contribute phases beyond group cohomology, so we conjecture that our classification is complete.

Delineation and geometric modeling of road networks

NASA Astrophysics Data System (ADS)

Poullis, Charalambos; You, Suya

In this work we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification. Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies. Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentations with better defined boundaries and continuous linear segments. Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations.

Distributed Object Oriented Programming

DTIC Science & Technology

1990-02-01

of the object oriented model of computation. Therefore, object oriented programming can provide the programmer with good conceptual tools to divide his...LABOR SALES-COMMISSION). The symbol + refers to the addition function and takes any number of numeric arguments. The third subtype of list forms is the...2) ’(:SEND-DONE) (SEWF (AREF OBJECT-i1-MESSAGES-SENT 2) ’(PROGN (FORMAT T "-s methd completely executed instr-ptr -s-V NAME %INSTR-PTR%) (INCF

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

NASA Astrophysics Data System (ADS)

Ghorpade, Vijaya K.; Checchin, Paul; Malaterre, Laurent; Trassoudaine, Laurent

2017-12-01

The accelerated advancement in modeling, digitizing, and visualizing techniques for 3D shapes has led to an increasing amount of 3D models creation and usage, thanks to the 3D sensors which are readily available and easy to utilize. As a result, determining the similarity between 3D shapes has become consequential and is a fundamental task in shape-based recognition, retrieval, clustering, and classification. Several decades of research in Content-Based Information Retrieval (CBIR) has resulted in diverse techniques for 2D and 3D shape or object classification/retrieval and many benchmark data sets. In this article, a novel technique for 3D shape representation and object classification has been proposed based on analyses of spatial, geometric distributions of 3D keypoints. These distributions capture the intrinsic geometric structure of 3D objects. The result of the approach is a probability distribution function (PDF) produced from spatial disposition of 3D keypoints, keypoints which are stable on object surface and invariant to pose changes. Each class/instance of an object can be uniquely represented by a PDF. This shape representation is robust yet with a simple idea, easy to implement but fast enough to compute. Both Euclidean and topological space on object's surface are considered to build the PDFs. Topology-based geodesic distances between keypoints exploit the non-planar surface properties of the object. The performance of the novel shape signature is tested with object classification accuracy. The classification efficacy of the new shape analysis method is evaluated on a new dataset acquired with a Time-of-Flight camera, and also, a comparative evaluation on a standard benchmark dataset with state-of-the-art methods is performed. Experimental results demonstrate superior classification performance of the new approach on RGB-D dataset and depth data.

A Taxonomy of Object-Oriented Measures Modeling the Object-Oriented Space

NASA Technical Reports Server (NTRS)

Neal, Ralph D.; Weistroffer, H. Roland; Coppins, Richard J.

1997-01-01

In order to control the quality of software and the software development process, it is important to understand the measurement of software. A first step toward a better comprehension of software measurement is the categorization of software measures by some meaningful taxonomy. The most worthwhile taxonomy would capture the fundamental nature of the object-oriented (O-O) space. The principal characteristics of object-oriented software offer a starting point for such a categorization of measures. This paper introduces a taxonomy of measures based upon fourteen characteristics of object-oriented software gathered from the literature. This taxonomy allows us to easily see gaps or redundancies in the existing O-O measures. The taxonomy also clearly differentiates among taxa so that there is no ambiguity as to the taxon to which a measure belongs. The taxonomy has been populated with measures taken from the literature.

A Taxonomy of Object-Oriented Measures Modeling the Object Oriented Space

NASA Technical Reports Server (NTRS)

Neal, Ralph D.; Weistroffer, H. Roland; Coppins, Richard J.

1997-01-01

In order to control the quality of software and the software development process, it is important to understand the measurement of software. A first step toward a better comprehension of software measurement is the categorization of software measures by some meaningful taxonomy. The most worthwhile taxonomy would capture the fundamental nature of the object-oriented (O-O) space. The principal characteristics of object-oriented software offer a starting point for such a categorization of measures. This paper introduces a taxonomy of measures based upon fourteen characteristics of object-oriented software gathered from the literature. This taxonomy allows us to easily see gaps or redundancies in the existing O-O measures. The taxonomy also clearly differentiates among taxa so that there is no ambiguity as to the taxon to which a measure belongs. The taxonomy has been populated with measures taken from the literature.

Object-oriented technologies in a multi-mission data system

NASA Technical Reports Server (NTRS)

Murphy, Susan C.; Miller, Kevin J.; Louie, John J.

1993-01-01

The Operations Engineering Laboratory (OEL) at JPL is developing new technologies that can provide more efficient and productive ways of doing business in flight operations. Over the past three years, we have worked closely with the Multi-Mission Control Team to develop automation tools, providing technology transfer into operations and resulting in substantial cost savings and error reduction. The OEL development philosophy is characterized by object-oriented design, extensive reusability of code, and an iterative development model with active participation of the end users. Through our work, the benefits of object-oriented design became apparent for use in mission control data systems. Object-oriented technologies and how they can be used in a mission control center to improve efficiency and productivity are explained. The current research and development efforts in the JPL Operations Engineering Laboratory are also discussed to architect and prototype a new paradigm for mission control operations based on object-oriented concepts.

Description and status update on GELLO: a proposed standardized object-oriented expression language for clinical decision support.

PubMed

Sordo, Margarita; Boxwala, Aziz A; Ogunyemi, Omolola; Greenes, Robert A

2004-01-01

A major obstacle to sharing computable clinical knowledge is the lack of a common language for specifying expressions and criteria. Such a language could be used to specify decision criteria, formulae, and constraints on data and action. Al-though the Arden Syntax addresses this problem for clinical rules, its generalization to HL7's object-oriented data model is limited. The GELLO Expression language is an object-oriented language used for expressing logical conditions and computations in the GLIF3 (GuideLine Interchange Format, v. 3) guideline modeling language. It has been further developed under the auspices of the HL7 Clinical Decision Support Technical Committee, as a proposed HL7 standard., GELLO is based on the Object Constraint Language (OCL), because it is vendor-independent, object-oriented, and side-effect-free. GELLO expects an object-oriented data model. Although choice of model is arbitrary, standardization is facilitated by ensuring that the data model is compatible with the HL7 Reference Information Model (RIM).

Advanced Protected Services: A Concept Paper on Survivable Service-Oriented Systems

DTIC Science & Technology

2010-05-07

resiliency and protection of such systems to a level where they can withstand sustained attacks from well-motivated adversaries. In this paper we...that are designed for the protection of systems that are based on service-oriented architectures. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF...resilient against malicious attacks , and to demonstrate the utility of the developed advanced protection techniques in settings that exhibit various

Classification of trabeculae into three-dimensional rodlike and platelike structures via local inertial anisotropy.

PubMed

Vasilić, Branimir; Rajapakse, Chamith S; Wehrli, Felix W

2009-07-01

Trabecular bone microarchitecture is a significant determinant of the bone's mechanical properties and is thus of major clinical relevance in predicting fracture risk. The three-dimensional nature of trabecular bone is characterized by parameters describing scale, topology, and orientation of structural elements. However, none of the current methods calculates all three types of parameters simultaneously and in three dimensions. Here the authors present a method that produces a continuous classification of voxels as belonging to platelike or rodlike structures that determines their orientation and estimates their thickness. The method, dubbed local inertial anisotropy (LIA), treats the image as a distribution of mass density and the orientation of trabeculae is determined from a locally calculated tensor of inertia at each voxel. The orientation entropies of rods and plates are introduced, which can provide new information about microarchitecture not captured by existing parameters. The robustness of the method to noise corruption, resolution reduction, and image rotation is demonstrated. Further, the method is compared with established three-dimensional parameters including the structure-model index and topological surface-to-curve ratio. Finally, the method is applied to data acquired in a previous translational pilot study showing that the trabecular bone of untreated hypogonadal men is less platelike than that of their eugonadal peers.

Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

PubMed

Ma, Xu; Cheng, Yongmei; Hao, Shuai

2016-12-10

Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

Underwater object classification using scattering transform of sonar signals

NASA Astrophysics Data System (ADS)

Saito, Naoki; Weber, David S.

2017-08-01

In this paper, we apply the scattering transform (ST)-a nonlinear map based off of a convolutional neural network (CNN)-to classification of underwater objects using sonar signals. The ST formalizes the observation that the filters learned by a CNN have wavelet-like structure. We achieve effective binary classification both on a real dataset of Unexploded Ordinance (UXOs), as well as synthetically generated examples. We also explore the effects on the waveforms with respect to changes in the object domain (e.g., translation, rotation, and acoustic impedance, etc.), and examine the consequences coming from theoretical results for the scattering transform. We show that the scattering transform is capable of excellent classification on both the synthetic and real problems, thanks to having more quasi-invariance properties that are well-suited to translation and rotation of the object.

Land cover classification of VHR airborne images for citrus grove identification

NASA Astrophysics Data System (ADS)

Amorós López, J.; Izquierdo Verdiguier, E.; Gómez Chova, L.; Muñoz Marí, J.; Rodríguez Barreiro, J. Z.; Camps Valls, G.; Calpe Maravilla, J.

Managing land resources using remote sensing techniques is becoming a common practice. However, data analysis procedures should satisfy the high accuracy levels demanded by users (public or private companies and governments) in order to be extensively used. This paper presents a multi-stage classification scheme to update the citrus Geographical Information System (GIS) of the Comunidad Valenciana region (Spain). Spain is the first citrus fruit producer in Europe and the fourth in the world. In particular, citrus fruits represent 67% of the agricultural production in this region, with a total production of 4.24 million tons (campaign 2006-2007). The citrus GIS inventory, created in 2001, needs to be regularly updated in order to monitor changes quickly enough, and allow appropriate policy making and citrus production forecasting. Automatic methods are proposed in this work to facilitate this update, whose processing scheme is summarized as follows. First, an object-oriented feature extraction process is carried out for each cadastral parcel from very high spatial resolution aerial images (0.5 m). Next, several automatic classifiers (decision trees, artificial neural networks, and support vector machines) are trained and combined to improve the final classification accuracy. Finally, the citrus GIS is automatically updated if a high enough level of confidence, based on the agreement between classifiers, is achieved. This is the case for 85% of the parcels and accuracy results exceed 94%. The remaining parcels are classified by expert photo-interpreters in order to guarantee the high accuracy demanded by policy makers.

Orienting Attention to Sound Object Representations Attenuates Change Deafness

ERIC Educational Resources Information Center

Backer, Kristina C.; Alain, Claude

2012-01-01

According to the object-based account of attention, multiple objects coexist in short-term memory (STM), and we can selectively attend to a particular object of interest. Although there is evidence that attention can be directed to visual object representations, the assumption that attention can be oriented to sound object representations has yet…

Average Orientation Is More Accessible through Object Boundaries than Surface Features

ERIC Educational Resources Information Center

Choo, Heeyoung; Levinthal, Brian R.; Franconeri, Steven L.

2012-01-01

In a glance, the visual system can provide a summary of some kinds of information about objects in a scene. We explore how summary information about "orientation" is extracted and find that some representations of orientation are privileged over others. Participants judged the average orientation of either a set of 6 bars or 6 circular…

Object-oriented Technology for Compressor Simulation

NASA Technical Reports Server (NTRS)

Drummond, C. K.; Follen, G. J.; Cannon, M. R.

1994-01-01

An object-oriented basis for interdisciplinary compressor simulation can, in principle, overcome several barriers associated with the traditional structured (procedural) development approach. This paper presents the results of a research effort with the objective to explore the repercussions on design, analysis, and implementation of a compressor model in an object oriented (OO) language, and to examine the ability of the OO system design to accommodate computational fluid dynamics (CFD) code for compressor performance prediction. Three fundamental results are that: (1) the selection of the object oriented language is not the central issue; enhanced (interdisciplinary) analysis capability derives from a broader focus on object-oriented technology; (2) object-oriented designs will produce more effective and reusable computer programs when the technology is applied to issues involving complex system inter-relationships (more so than when addressing the complex physics of an isolated discipline); and (3) the concept of disposable prototypes is effective for exploratory research programs, but this requires organizations to have a commensurate long-term perspective. This work also suggests that interdisciplinary simulation can be effectively accomplished (over several levels of fidelity) with a mixed language treatment (i.e., FORTRAN-C++), reinforcing the notion the OO technology implementation into simulations is a 'journey' in which the syntax can, by design, continuously evolve.

People vs. Object Orientation in Preschool Boys and Girls.

ERIC Educational Resources Information Center

Jennings, Kay D.

This study was undertaken to explore the cultural stereotype that boys are more object-oriented and girls are more people-oriented. A total of 38 white, middle class, preschool children were observed during their free play hour at nursery school when a variety of people and objects were freely available to them. Each child was observed with a time…

Experiences Building an Object-Oriented System in C++

NASA Technical Reports Server (NTRS)

Madany, Peter W.; Campbell, Roy H.; Kougiouris, Panagiotis

1991-01-01

This paper describes tools that we built to support the construction of an object-oriented operating system in C++. The tools provide the automatic deletion of unwanted objects, first-class classes, dynamically loadable classes, and class-oriented debugging. As a consequence of our experience building Choices, we advocate these features as useful, simplifying and unifying many aspects of system programming.

Gas turbine system simulation: An object-oriented approach

NASA Technical Reports Server (NTRS)

Drummond, Colin K.; Follen, Gregory J.; Putt, Charles W.

1993-01-01

A prototype gas turbine engine simulation has been developed that offers a generalized framework for the simulation of engines subject to steady-state and transient operating conditions. The prototype is in preliminary form, but it successfully demonstrates the viability of an object-oriented approach for generalized simulation applications. Although object oriented programming languages are-relative to FORTRAN-somewhat austere, it is proposed that gas turbine simulations of an interdisciplinary nature will benefit significantly in terms of code reliability, maintainability, and manageability. This report elucidates specific gas turbine simulation obstacles that an object-oriented framework can overcome and describes the opportunity for interdisciplinary simulation that the approach offers.

Cognitive characteristics of learning Java, an object-oriented programming language

NASA Astrophysics Data System (ADS)

White, Garry Lynn

Industry and Academia are moving from procedural programming languages (e.g., COBOL) to object-oriented programming languages, such as Java for the Internet. Past studies in the cognitive aspects of programming have focused primarily on procedural programming languages. Some of the languages used have been Pascal, C, Basic, FORTAN, and COBOL. Object-oriented programming (OOP) represents a new paradigm for computing. Industry is finding that programmers are having difficulty shifting to this new programming paradigm. This instruction in OOP is currently starting in colleges and universities across the country. What are the cognitive aspects for this new OOP language Java? When is a student developmentally ready to handle the cognitive characteristics of the OOP language Java? Which cognitive teaching style is best for this OOP language Java? Questions such as the aforementioned are the focus of this research Such research is needed to improve understanding of the learning process and identify students' difficulties with OOP methods. This can enhance academic teaching and industry training (Scholtz, 1993; Sheetz, 1997; Rosson, 1990). Cognitive development as measured by the Propositional Logic Test, cognitive style as measured by the Hemispheric Mode Indicator, and physical hemispheric dominance as measured by a self-report survey were obtained from thirty-six university students studying Java programming. Findings reveal that physical hemispheric dominance is unrelated to cognitive and programming language variables. However, both procedural and object oriented programming require Piaget's formal operation cognitive level as indicated by the Propositional Logic Test. This is consistent with prior research A new finding is that object oriented programming also requires formal operation cognitive level. Another new finding is that object oriented programming appears to be unrelated to hemispheric cognitive style as indicated by the Hemispheric Mode Indicator (HMI). This research suggests that object oriented programming is hemispheric thinking style friendly, while procedural programming is left hemispheric cognitive style. The conclusion is that cognitive characteristics are not the cause for the difficulty in shifting from procedural to this new programming paradigm of object oriented programming. An alternative possibility to the difficulty is proactive interference. Prior learning of procedural programming makes it harder to learning object oriented programming. Further research is needed to determine if proactive interference is the cause for the difficulty in shifting from procedural programming to object oriented programming.

Attitudes Toward Children's Rights: Nurturance or Self-Determination?

ERIC Educational Resources Information Center

Rogers, Carl M.; Wrightsman, Lawrence S.

1978-01-01

This article describes the development of a classification schema consisting of two conceptual orientations toward the rights of children (nurturance and self-determination) which cut across five different content areas. (Author)

Object-orientated DBMS techniques for time-oriented medical record.

PubMed

Pinciroli, F; Combi, C; Pozzi, G

1992-01-01

In implementing time-orientated medical record (TOMR) management systems, use of a relational model played a big role. Many applications have been developed to extend query and data manipulation languages to temporal aspects of information. Our experience in developing TOMR revealed some deficiencies inside the relational model, such as: (a) abstract data type definition; (b) unified view of data, at a programming level; (c) management of temporal data; (d) management of signals and images. We identified some first topics to face by an object-orientated approach to database design. This paper describes the first steps in designing and implementing a TOMR by an object-orientated DBMS.

Reducing uncertainty on satellite image classification through spatiotemporal reasoning

NASA Astrophysics Data System (ADS)

Partsinevelos, Panagiotis; Nikolakaki, Natassa; Psillakis, Periklis; Miliaresis, George; Xanthakis, Michail

2014-05-01

The natural habitat constantly endures both inherent natural and human-induced influences. Remote sensing has been providing monitoring oriented solutions regarding the natural Earth surface, by offering a series of tools and methodologies which contribute to prudent environmental management. Processing and analysis of multi-temporal satellite images for the observation of the land changes include often classification and change-detection techniques. These error prone procedures are influenced mainly by the distinctive characteristics of the study areas, the remote sensing systems limitations and the image analysis processes. The present study takes advantage of the temporal continuity of multi-temporal classified images, in order to reduce classification uncertainty, based on reasoning rules. More specifically, pixel groups that temporally oscillate between classes are liable to misclassification or indicate problematic areas. On the other hand, constant pixel group growth indicates a pressure prone area. Computational tools are developed in order to disclose the alterations in land use dynamics and offer a spatial reference to the pressures that land use classes endure and impose between them. Moreover, by revealing areas that are susceptible to misclassification, we propose specific target site selection for training during the process of supervised classification. The underlying objective is to contribute to the understanding and analysis of anthropogenic and environmental factors that influence land use changes. The developed algorithms have been tested upon Landsat satellite image time series, depicting the National Park of Ainos in Kefallinia, Greece, where the unique in the world Abies cephalonica grows. Along with the minor changes and pressures indicated in the test area due to harvesting and other human interventions, the developed algorithms successfully captured fire incidents that have been historically confirmed. Overall, the results have shown that the use of the suggested procedures can contribute to the reduction of the classification uncertainty and support the existing knowledge regarding the pressure among land-use changes.

Pearson-Readhead Survey Sources. II. The Long-Term Centimeter-Band Total Flux and Linear Polarization Properties of a Complete Radio Sample

NASA Astrophysics Data System (ADS)

Aller, M. F.; Aller, H. D.; Hughes, P. A.

2003-03-01

Using UMRAO centimeter-band total flux density and linear polarization monitoring observations of the complete Pearson-Readhead extragalactic source sample obtained between 1984 August and 2001 March, we identify the range of variability in extragalactic objects as functions of optical and radio morphological classification and relate total flux density variations to structural changes in published coeval VLBI maps in selected objects. As expected, variability is common in flat- or inverted-spectrum (α<=0.5) core-dominated QSOs and BL Lac objects. Unexpectedly, we find flux variations in several steep-spectrum sample members, including the commonly adopted flux standard 3C 147. Such variations are characteristically several-year rises or declines or infrequent outbursts, requiring long-term observations for detection: we attribute them to the brightening of weak core components, a change that is suppressed by contributions from extended structure in all but the strongest events, and identify a wavelength dependence for the amplitude of this variability consistent with the presence of opacity in some portions of the jet flow. One morphological class of steep-spectrum objects, the compact symmetric objects (CSOs), characteristically shows only low-level variability. We examine the statistical relation between fractional polarization and radio class based on the data at 14.5 and 4.8 GHz. The blazars typically exhibit flat-to-inverted polarization spectra, a behavior attributed to opacity effects. Among the steep-spectrum objects, the lobe-dominated FR I galaxies have steep fractional polarization spectra, while the FR II galaxies exhibit fractional polarization spectra ranging from inverted to steep, with no identifiable common property that accounts for the range in behavior. For the CSO/gigahertz-peaked spectrum sources, we verify that the fractional polarizations at 4.8 GHz are only of the order of a few tenths of a percent, but at 14.5 GHz we find significantly higher polarizations, ranging from 1% to 3%; this frequency dependence supports a scenario invoking Faraday depolarization by a circumnuclear torus. We have identified preferred orientations of the electric vector of the polarized emission (EVPA) at 14.5 and 4.8 GHz in roughly half of the objects and compared these with orientations of the flow direction indicated by VLBI morphology. When comparing the distributions of the orientation offsets for the BL Lac objects and the QSOs, we find differences in both range and mean value, in support of intrinsic class differences. In the shock-in-jet scenario, we attribute this to the allowed range of obliquities of shocks developing in the flow relative to the flow direction: in the BL Lac objects the shocks are nearly transverse to the flow direction, while in the QSOs they include a broader range of obliquities and can be at large angles to it. The fact that we find long-term stability in EVPA over many events implies that a dominant magnetic field orientation persists; in the core-dominated objects, with small contribution from the underlying quiescent jet, this plausibly suggests that the magnetic field has a long-term memory, with subsequent shock events exhibiting similar EVPA orientation, or, alternatively, the presence of a standing shock in the core. We have looked for systematic, monotonic changes in EVPA, which might be expected in the emission from a precessing jet, a model currently invoked for some AGNs; none were identified. Further, we carried out a Scargle periodogram analysis of the total flux density observations, but found no strong evidence for periodicity in any of the sample sources. The only well-established case in support of both jet precession and periodic variability remains the non-sample member OJ 287.

Principal axes estimation using the vibration modes of physics-based deformable models.

PubMed

Krinidis, Stelios; Chatzis, Vassilios

2008-06-01

This paper addresses the issue of accurate, effective, computationally efficient, fast, and fully automated 2-D object orientation and scaling factor estimation. The object orientation is calculated using object principal axes estimation. The approach relies on the object's frequency-based features. The frequency-based features used by the proposed technique are extracted by a 2-D physics-based deformable model that parameterizes the objects shape. The method was evaluated on synthetic and real images. The experimental results demonstrate the accuracy of the method, both in orientation and the scaling estimations.

Remote sensing imagery classification using multi-objective gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2016-10-01

Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

Object-oriented fault tree evaluation program for quantitative analyses

NASA Technical Reports Server (NTRS)

Patterson-Hine, F. A.; Koen, B. V.

1988-01-01

Object-oriented programming can be combined with fault free techniques to give a significantly improved environment for evaluating the safety and reliability of large complex systems for space missions. Deep knowledge about system components and interactions, available from reliability studies and other sources, can be described using objects that make up a knowledge base. This knowledge base can be interrogated throughout the design process, during system testing, and during operation, and can be easily modified to reflect design changes in order to maintain a consistent information source. An object-oriented environment for reliability assessment has been developed on a Texas Instrument (TI) Explorer LISP workstation. The program, which directly evaluates system fault trees, utilizes the object-oriented extension to LISP called Flavors that is available on the Explorer. The object representation of a fault tree facilitates the storage and retrieval of information associated with each event in the tree, including tree structural information and intermediate results obtained during the tree reduction process. Reliability data associated with each basic event are stored in the fault tree objects. The object-oriented environment on the Explorer also includes a graphical tree editor which was modified to display and edit the fault trees.

Orientation estimation of anatomical structures in medical images for object recognition

NASA Astrophysics Data System (ADS)

Bağci, Ulaş; Udupa, Jayaram K.; Chen, Xinjian

2011-03-01

Recognition of anatomical structures is an important step in model based medical image segmentation. It provides pose estimation of objects and information about "where" roughly the objects are in the image and distinguishing them from other object-like entities. In,1 we presented a general method of model-based multi-object recognition to assist in segmentation (delineation) tasks. It exploits the pose relationship that can be encoded, via the concept of ball scale (b-scale), between the binary training objects and their associated grey images. The goal was to place the model, in a single shot, close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. Unlike position and scale parameters, we observe that orientation parameters require more attention when estimating the pose of the model as even small differences in orientation parameters can lead to inappropriate recognition. Motivated from the non-Euclidean nature of the pose information, we propose in this paper the use of non-Euclidean metrics to estimate orientation of the anatomical structures for more accurate recognition and segmentation. We statistically analyze and evaluate the following metrics for orientation estimation: Euclidean, Log-Euclidean, Root-Euclidean, Procrustes Size-and-Shape, and mean Hermitian metrics. The results show that mean Hermitian and Cholesky decomposition metrics provide more accurate orientation estimates than other Euclidean and non-Euclidean metrics.

The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel; Ershov, Maksim; Strotov, Valery

2016-10-01

This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

Contour-based object orientation estimation

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel

2016-04-01

Real-time object orientation estimation is an actual problem of computer vision nowadays. In this paper we propose an approach to estimate an orientation of objects lacking axial symmetry. Proposed algorithm is intended to estimate orientation of a specific known 3D object, so 3D model is required for learning. The proposed orientation estimation algorithm consists of 2 stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. It minimizes the training image set. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy (mean error value less than 6°) in all case studies. The real-time performance of the algorithm was also demonstrated.

Growth of Highly c-axis Oriented and/or Epitaxial Single-Domain b-axis Oriented La5Ca9Cu24O41 Thin Films by Pulsed Laser Deposition

DTIC Science & Technology

2016-04-01

project attempted to grow La5Ca9Cu24O41 (LCCO) films on important substrates with the high- thermal -conductivity direction parallel or perpendicular...to the surface of the substrate, counting success as demonstration of b-axis or c-axis oriented LCCO films along with measurement of bulk thermal ...deposition, LCCO, La5Ca9Cu24O41, thermal conductivity, epitaxy 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18. NUMBER OF PAGES 24

[Current state of competence assessment in nursing].

PubMed

Darmann-Finck, Ingrid; Reuschenbach, Bernd

2013-01-01

Competency measurement is central to the optimisation of outcome oriented educational processes in nursing, similar to the concept of evidence based practice. The classification of measurement tools provides the basis for describing the current state of research and development in relation to competence measurement in nursing science, and any gaps are identified. The article concludes with questioning the importance of outcome oriented quality orientation in order to achieve an increase in quality during training. Further methodological developments and qualitative studies are needed to examine the context specific processes of interaction and learning, beyond competence diagnostics. Copyright © 2012. Published by Elsevier GmbH.

Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

PubMed Central

Lahmiri, Salim; Boukadoum, Mounir

2013-01-01

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction. PMID:27006906

LSST Astroinformatics And Astrostatistics: Data-oriented Astronomical Research

NASA Astrophysics Data System (ADS)

Borne, Kirk D.; Stassun, K.; Brunner, R. J.; Djorgovski, S. G.; Graham, M.; Hakkila, J.; Mahabal, A.; Paegert, M.; Pesenson, M.; Ptak, A.; Scargle, J.; Informatics, LSST; Statistics Team

2011-01-01

The LSST Informatics and Statistics Science Collaboration (ISSC) focuses on research and scientific discovery challenges posed by the very large and complex data collection that LSST will generate. Application areas include astroinformatics, machine learning, data mining, astrostatistics, visualization, scientific data semantics, time series analysis, and advanced signal processing. Research problems to be addressed with these methodologies include transient event characterization and classification, rare class discovery, correlation mining, outlier/anomaly/surprise detection, improved estimators (e.g., for photometric redshift or early onset supernova classification), exploration of highly dimensional (multivariate) data catalogs, and more. We present sample science results from these data-oriented approaches to large-data astronomical research. We present results from LSST ISSC team members, including the EB (Eclipsing Binary) Factory, the environmental variations in the fundamental plane of elliptical galaxies, and outlier detection in multivariate catalogs.

Hierarchical classification method and its application in shape representation

NASA Astrophysics Data System (ADS)

Ireton, M. A.; Oakley, John P.; Xydeas, Costas S.

1992-04-01

In this paper we describe a technique for performing shaped-based content retrieval of images from a large database. In order to be able to formulate such user-generated queries about visual objects, we have developed an hierarchical classification technique. This hierarchical classification technique enables similarity matching between objects, with the position in the hierarchy signifying the level of generality to be used in the query. The classification technique is unsupervised, robust, and general; it can be applied to any suitable parameter set. To establish the potential of this classifier for aiding visual querying, we have applied it to the classification of the 2-D outlines of leaves.

Effect of Process-Oriented Guided-Inquiry Learning on Non-majors Biology Students' Understanding of Biological Classification

NASA Astrophysics Data System (ADS)

Wozniak, Breann M.

The purpose of this study was to examine the effect of process-oriented guided-inquiry learning (POGIL) on non-majors college biology students' understanding of biological classification. This study addressed an area of science instruction, POGIL in the non-majors college biology laboratory, which has yet to be qualitatively and quantitatively researched. A concurrent triangulation mixed methods approach was used. Students' understanding of biological classification was measured in two areas: scores on pre and posttests (consisting of 11 multiple choice questions), and conceptions of classification as elicited in pre and post interviews and instructor reflections. Participants were Minnesota State University, Mankato students enrolled in BIOL 100 Summer Session. One section was taught with the traditional curriculum (n = 6) and the other section in the POGIL curriculum (n = 10) developed by the researcher. Three students from each section were selected to take part in pre and post interviews. There were no significant differences within each teaching method (p < .05). There was a tendency of difference in the means. The POGIL group may have scored higher on the posttest (M = 8.830 +/- .477 vs. M = 7.330 +/- .330; z =-1.729, p = .084) and the traditional group may have scored higher on the pretest than the posttest (M = 8.333 +/- .333 vs M = 7.333 +/- .333; z = -1.650 , p = .099). Two themes emerged after the interviews and instructor reflections: 1) After instruction students had a more extensive understanding of classification in three areas: vocabulary terms, physical characteristics, and types of evidence used to classify. Both groups extended their understanding, but only POGIL students could explain how molecular evidence is used in classification. 2) The challenges preventing students from understanding classification were: familiar animal categories and aquatic habitats, unfamiliar organisms, combining and subdividing initial groupings, and the hierarchical nature of classification. The POGIL students were the only group to surpass these challenges after the teaching intervention. This study shows that POGIL is an effective technique at eliciting students' misconceptions, and addressing these misconceptions, leading to an increase in student understanding of biological classification.

Shallow-Water Mud Acoustics

DTIC Science & Technology

2015-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Shallow- Water Mud Acoustics William L. Siegmann...shallow water over mud sediments and of acoustic detection, localization, and classification of objects buried in mud. OBJECTIVES • Develop...including long-range conveyance of information; detection, localization, and classification of objects buried in mud; and improvement of shallow water

Multidimensional Shape Similarity in the Development of Visual Object Classification

ERIC Educational Resources Information Center

Mash, Clay

2006-01-01

The current work examined age differences in the classification of novel object images that vary in continuous dimensions of structural shape. The structural dimensions employed are two that share a privileged status in the visual analysis and representation of objects: the shape of discrete prominent parts and the attachment positions of those…

A Method of Face Detection with Bayesian Probability

NASA Astrophysics Data System (ADS)

Sarker, Goutam

2010-10-01

The objective of face detection is to identify all images which contain a face, irrespective of its orientation, illumination conditions etc. This is a hard problem, because the faces are highly variable in size, shape lighting conditions etc. Many methods have been designed and developed to detect faces in a single image. The present paper is based on one `Appearance Based Method' which relies on learning the facial and non facial features from image examples. This in its turn is based on statistical analysis of examples and counter examples of facial images and employs Bayesian Conditional Classification Rule to detect the probability of belongingness of a face (or non-face) within an image frame. The detection rate of the present system is very high and thereby the number of false positive and false negative detection is substantially low.

Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

Treesearch

Weiqi Zhou; Austin Troy; Morgan Grove

2008-01-01

Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...

Human Factors Engineering. Student Supplement,

DTIC Science & Technology

1981-08-01

a job TASK TAXONOMY A classification scheme for the different levels of activities in a system, i.e., job - task - sub-task, etc. TASK-AN~ALYSIS...with the classification of learning objectives by learning category so as to identify learningPhas III guidelines necessary for optimum learning to...correct. .4... .the sequencing of all dependent tasks. .1.. .the classification of learning objectives by learning category and the Identification of

ESTCP Pilot Program - Classification Approaches in Munitions Response

DTIC Science & Technology

2008-11-17

Electromagnetic induction sensors detect ferrous and 57 nonferrous metallic objects and can be effective in geology that challenges magnetometers. EM...harmless metallic objects or geology. Application of technology to separate the munitions from other objects, known as classification, offers the potential...detectable signals are excavated. Many of these detections do not correspond to munitions, but rather to other harmless metallic objects or geology, termed

Registration of Laser Scanning Point Clouds and Aerial Images Using either Artificial or Natural Tie Features

NASA Astrophysics Data System (ADS)

Rönnholm, P.; Haggrén, H.

2012-07-01

Integration of laser scanning data and photographs is an excellent combination regarding both redundancy and complementary. Applications of integration vary from sensor and data calibration to advanced classification and scene understanding. In this research, only airborne laser scanning and aerial images are considered. Currently, the initial registration is solved using direct orientation sensors GPS and inertial measurements. However, the accuracy is not usually sufficient for reliable integration of data sets, and thus the initial registration needs to be improved. A registration of data from different sources requires searching and measuring of accurate tie features. Usually, points, lines or planes are preferred as tie features. Therefore, the majority of resent methods rely highly on artificial objects, such as buildings, targets or road paintings. However, in many areas no such objects are available. For example in forestry areas, it would be advantageous to be able to improve registration between laser data and images without making additional ground measurements. Therefore, there is a need to solve registration using only natural features, such as vegetation and ground surfaces. Using vegetation as tie features is challenging, because the shape and even location of vegetation can change because of wind, for example. The aim of this article was to compare registration accuracies derived by using either artificial or natural tie features. The test area included urban objects as well as trees and other vegetation. In this area, two registrations were performed, firstly, using mainly built objects and, secondly, using only vegetation and ground surface. The registrations were solved applying the interactive orientation method. As a result, using artificial tie features leaded to a successful registration in all directions of the coordinate system axes. In the case of using natural tie features, however, the detection of correct heights was difficult causing also some tilt errors. The planimetric registration was accurate.

Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery.

PubMed

Belgiu, Mariana; Dr Guţ, Lucian

2014-10-01

Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, however, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies have suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches. Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. One supervised and two unsupervised segmentation methods were tested on three areas using QuickBird and WorldView-2 satellite imagery. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The two approaches yielded remarkably similar classification results, with overall accuracies ranging from 82% to 86%. The performance of one of the unsupervised methods was unexpectedly similar to that of the supervised method; they identified almost identical scale parameters as being optimal for segmenting buildings, resulting in very similar geometries for the resulting image objects. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications. The first is that object-based image analysis can be automated without sacrificing classification accuracy, and the second is that the previously accepted idea that classification is dependent on segmentation is challenged by our unexpected results, casting doubt on the value of pursuing 'optimal segmentation'. Our results rather suggest that as long as under-segmentation remains at acceptable levels, imperfections in segmentation can be ruled out, so that a high level of classification accuracy can still be achieved.

The Visual Representation of 3D Object Orientation in Parietal Cortex

PubMed Central

Cowan, Noah J.; Angelaki, Dora E.

2013-01-01

An accurate representation of three-dimensional (3D) object orientation is essential for interacting with the environment. Where and how the brain visually encodes 3D object orientation remains unknown, but prior studies suggest the caudal intraparietal area (CIP) may be involved. Here, we develop rigorous analytical methods for quantifying 3D orientation tuning curves, and use these tools to the study the neural coding of surface orientation. Specifically, we show that single neurons in area CIP of the rhesus macaque jointly encode the slant and tilt of a planar surface, and that across the population, the distribution of preferred slant-tilts is not statistically different from uniform. This suggests that all slant-tilt combinations are equally represented in area CIP. Furthermore, some CIP neurons are found to also represent the third rotational degree of freedom that determines the orientation of the image pattern on the planar surface. Together, the present results suggest that CIP is a critical neural locus for the encoding of all three rotational degrees of freedom specifying an object's 3D spatial orientation. PMID:24305830

The influence of object similarity and orientation on object-based cueing.

PubMed

Hein, Elisabeth; Blaschke, Stefan; Rolke, Bettina

2017-01-01

Responses to targets that appear at a noncued position within the same object (invalid-same) compared to a noncued position at an equidistant different object (invalid-different) tend to be faster and more accurate. These cueing effects have been taken as evidence that visual attention can be object based (Egly, Driver, & Rafal, Journal of Experimental Psychology: General, 123, 161-177, 1994). Recent findings, however, have shown that the object-based cueing effect is influenced by object orientation, suggesting that the cueing effect might be due to a more general facilitation of attentional shifts across the horizontal meridian (Al-Janabi & Greenberg, Attention, Perception, & Psychophysics, 1-17, 2016; Pilz, Roggeveen, Creighton, Bennet, & Sekuler, PLOS ONE, 7, e30693, 2012). The aim of this study was to investigate whether the object-based cueing effect is influenced by object similarity and orientation. According to the object-based attention account, objects that are less similar to each other should elicit stronger object-based cueing effects independent of object orientation, whereas the horizontal meridian theory would not predict any effect of object similarity. We manipulated object similarity by using a color (Exp. 1, Exp. 2A) or shape change (Exp. 2B) to distinguish two rectangles in a variation of the classic two-rectangle paradigm (Egly et al., 1994). We found that the object-based cueing effects were influenced by the orientation of the rectangles and strengthened by object dissimilarity. We suggest that object-based cueing effects are strongly affected by the facilitation of attention along the horizontal meridian, but that they also have an object-based attentional component, which is revealed when the dissimilarity between the presented objects is accentuated.

Orientation Control Method and System for Object in Motion

NASA Technical Reports Server (NTRS)

Whorton, Mark Stephen (Inventor); Redmon, Jr., John W. (Inventor); Cox, Mark D. (Inventor)

2012-01-01

An object in motion has a force applied thereto at a point of application. By moving the point of application such that the distance between the object's center-of-mass and the point of application is changed, the object's orientation can be changed/adjusted.

A Generalized Orienteering Problem for Optimal Search and Interdiction Planning

DTIC Science & Technology

2013-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA DISSERTATION A GENERALIZED ORIENTEERING PROBLEM FOR OPTIMAL SEARCH AND INTERDICTION PLANNING by Jesse...provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently...16. SECURITY CLASSIFICATION OF: a . REPORT b. ABSTRACT c. THIS PAGE 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON 19b

An Assessment of GEO Orbital Debris Photometric Properties Derived from Laboratory-Based Measurements

NASA Technical Reports Server (NTRS)

Rodriquez-Cowardin, H.; Abercromby, K.; Barker, E.; Mulrooney, M.; Seitzer, P.; Schildknecht, T.

2009-01-01

Optical observations of orbital debris offer insights that differ from radar measurements (specifically the size parameter and wavelength regime). For example, time-dependent photometric data yield lightcurves in multiple bandpasses that aid in material identification and possible periodic orientations. This data can also be used to help identify shapes and optical properties at multiple phase angles. Capitalizing on optical data products and applying them to generate a more complete understanding of orbital space objects, is a key objective of NASA s Optical Measurement Program, and a primary driver for creation of the Optical Measurements Center (OMC). The OMC attempts to emulate space-based illumination conditions using equipment and techniques that parallel telescopic observations and source-target-sensor orientations. The OMC uses a 300 Watt Xenon arc lamp as a solar simulator, a CCD camera with Johnson/Bessel colored filters, and a robotic arm to orientate/rotate objects to simulate an object's orbit/rotational period. A high-resolution, high bandwidth (350nm-2500nm) Analytical Spectral Devices (ASD) spectrometer is also employed to baseline various material types. Since observation of GEO targets are generally restricted to the optical regime (due to radar range limitations), analysis of their properties is tailored to those revealed by optical data products. In this connection, much attention has been directed towards understanding the lightcurves of orbital debris with high area-to-mass (A/m) ratios (greater than 0.9 square meters per kilogram). A small population of GEO debris was recently identified, which exhibits the properties of high A/m objects, such as variable eccentricities and inclinations a dynamical characteristic generally resulting from varying solar radiation pressure on high A/m objects. Materials such as multi-layered insulation (MLI) and solar panels are two examples of materials with high area-to mass ratios. Lightcurves for such objects can vary greatly (even for the same object under different illumination conditions). For example, specular reflections from multiple facets of the target surface (e.g. Mylar or Aluminized Kapton) can lead to erratic, orientation-dependent light curves. This paper will investigate published color photometric data for a series of orbital debris targets and compare it to the empirical photometric measurements generated in the OMC. The specific materials investigated (known to exist in GEO) are: an intact piece of MLI, separated layers of MLI, and multiple solar cells materials. Using the data acquired over specific rotational angles through different filters (B, V, R, I), a color index is acquired (B-R, R-I). As a secondary check, the spectrometer is used to define color indexes for the same material. Using these values and their associated lightcurves, this laboratory data is compared to observational data obtained on the 1m telescope of the Astronomical Institute of the University of Bern (AUIB) and the 0.9 m Small and Moderate Aperture Research Telescope System (SMARTS) telescope at Cerro Tololo Inter-American Observatory (CTIO). We will present laboratory generated lightcurves with color indexes of the high A/m materials alongside telescopic data of targets with high A/m values. We will discuss the relationship of laboratory to telescope data in the context of classification of GEO debris objects.

A conditioned visual orientation requires the ellipsoid body in Drosophila

PubMed Central

Guo, Chao; Du, Yifei; Yuan, Deliang; Li, Meixia; Gong, Haiyun; Gong, Zhefeng

2015-01-01

Orientation, the spatial organization of animal behavior, is an essential faculty of animals. Bacteria and lower animals such as insects exhibit taxis, innate orientation behavior, directly toward or away from a directional cue. Organisms can also orient themselves at a specific angle relative to the cues. In this study, using Drosophila as a model system, we established a visual orientation conditioning paradigm based on a flight simulator in which a stationary flying fly could control the rotation of a visual object. By coupling aversive heat shocks to a fly's orientation toward one side of the visual object, we found that the fly could be conditioned to orientate toward the left or right side of the frontal visual object and retain this conditioned visual orientation. The lower and upper visual fields have different roles in conditioned visual orientation. Transfer experiments showed that conditioned visual orientation could generalize between visual targets of different sizes, compactness, or vertical positions, but not of contour orientation. Rut—Type I adenylyl cyclase and Dnc—phosphodiesterase were dispensable for visual orientation conditioning. Normal activity and scb signaling in R3/R4d neurons of the ellipsoid body were required for visual orientation conditioning. Our studies established a visual orientation conditioning paradigm and examined the behavioral properties and neural circuitry of visual orientation, an important component of the insect's spatial navigation. PMID:25512578

Neural Network Based Sensory Fusion for Landmark Detection

NASA Technical Reports Server (NTRS)

Kumbla, Kishan -K.; Akbarzadeh, Mohammad R.

1997-01-01

NASA is planning to send numerous unmanned planetary missions to explore the space. This requires autonomous robotic vehicles which can navigate in an unstructured, unknown, and uncertain environment. Landmark based navigation is a new area of research which differs from the traditional goal-oriented navigation, where a mobile robot starts from an initial point and reaches a destination in accordance with a pre-planned path. The landmark based navigation has the advantage of allowing the robot to find its way without communication with the mission control station and without exact knowledge of its coordinates. Current algorithms based on landmark navigation however pose several constraints. First, they require large memories to store the images. Second, the task of comparing the images using traditional methods is computationally intensive and consequently real-time implementation is difficult. The method proposed here consists of three stages, First stage utilizes a heuristic-based algorithm to identify significant objects. The second stage utilizes a neural network (NN) to efficiently classify images of the identified objects. The third stage combines distance information with the classification results of neural networks for efficient and intelligent navigation.

Object-oriented design and programming in medical decision support.

PubMed

Heathfield, H; Armstrong, J; Kirkham, N

1991-12-01

The concept of object-oriented design and programming has recently received a great deal of attention from the software engineering community. This paper highlights the realisable benefits of using the object-oriented approach in the design and development of clinical decision support systems. These systems seek to build a computational model of some problem domain and therefore tend to be exploratory in nature. Conventional procedural design techniques do not support either the process of model building or rapid prototyping. The central concepts of the object-oriented paradigm are introduced, namely encapsulation, inheritance and polymorphism, and their use illustrated in a case study, taken from the domain of breast histopathology. In particular, the dual roles of inheritance in object-oriented programming are examined, i.e., inheritance as a conceptual modelling tool and inheritance as a code reuse mechanism. It is argued that the use of the former is not entirely intuitive and may be difficult to incorporate into the design process. However, inheritance as a means of optimising code reuse offers substantial technical benefits.

Landscape object-based analysis of wetland plant functional types: the effects of spatial scale, vegetation classes and classifier methods

NASA Astrophysics Data System (ADS)

Dronova, I.; Gong, P.; Wang, L.; Clinton, N.; Fu, W.; Qi, S.

2011-12-01

Remote sensing-based vegetation classifications representing plant function such as photosynthesis and productivity are challenging in wetlands with complex cover and difficult field access. Recent advances in object-based image analysis (OBIA) and machine-learning algorithms offer new classification tools; however, few comparisons of different algorithms and spatial scales have been discussed to date. We applied OBIA to delineate wetland plant functional types (PFTs) for Poyang Lake, the largest freshwater lake in China and Ramsar wetland conservation site, from 30-m Landsat TM scene at the peak of spring growing season. We targeted major PFTs (C3 grasses, C3 forbs and different types of C4 grasses and aquatic vegetation) that are both key players in system's biogeochemical cycles and critical providers of waterbird habitat. Classification results were compared among: a) several object segmentation scales (with average object sizes 900-9000 m2); b) several families of statistical classifiers (including Bayesian, Logistic, Neural Network, Decision Trees and Support Vector Machines) and c) two hierarchical levels of vegetation classification, a generalized 3-class set and more detailed 6-class set. We found that classification benefited from object-based approach which allowed including object shape, texture and context descriptors in classification. While a number of classifiers achieved high accuracy at the finest pixel-equivalent segmentation scale, the highest accuracies and best agreement among algorithms occurred at coarser object scales. No single classifier was consistently superior across all scales, although selected algorithms of Neural Network, Logistic and K-Nearest Neighbors families frequently provided the best discrimination of classes at different scales. The choice of vegetation categories also affected classification accuracy. The 6-class set allowed for higher individual class accuracies but lower overall accuracies than the 3-class set because individual classes differed in scales at which they were best discriminated from others. Main classification challenges included a) presence of C3 grasses in C4-grass areas, particularly following harvesting of C4 reeds and b) mixtures of emergent, floating and submerged aquatic plants at sub-object and sub-pixel scales. We conclude that OBIA with advanced statistical classifiers offers useful instruments for landscape vegetation analyses, and that spatial scale considerations are critical in mapping PFTs, while multi-scale comparisons can be used to guide class selection. Future work will further apply fuzzy classification and field-collected spectral data for PFT analysis and compare results with MODIS PFT products.

Real-time, resource-constrained object classification on a micro-air vehicle

NASA Astrophysics Data System (ADS)

Buck, Louis; Ray, Laura

2013-12-01

A real-time embedded object classification algorithm is developed through the novel combination of binary feature descriptors, a bag-of-visual-words object model and the cortico-striatal loop (CSL) learning algorithm. The BRIEF, ORB and FREAK binary descriptors are tested and compared to SIFT descriptors with regard to their respective classification accuracies, execution times, and memory requirements when used with CSL on a 12.6 g ARM Cortex embedded processor running at 800 MHz. Additionally, the effect of x2 feature mapping and opponent-color representations used with these descriptors is examined. These tests are performed on four data sets of varying sizes and difficulty, and the BRIEF descriptor is found to yield the best combination of speed and classification accuracy. Its use with CSL achieves accuracies between 67% and 95% of those achieved with SIFT descriptors and allows for the embedded classification of a 128x192 pixel image in 0.15 seconds, 60 times faster than classification with SIFT. X2 mapping is found to provide substantial improvements in classification accuracy for all of the descriptors at little cost, while opponent-color descriptors are offer accuracy improvements only on colorful datasets.

Object-oriented design tools for supramolecular devices and biomedical nanotechnology.

PubMed

Lee, Stephen C; Bhalerao, Khaustaub; Ferrari, Mauro

2004-05-01

Nanotechnology provides multifunctional agents for in vivo use that increasingly blur the distinction between pharmaceuticals and medical devices. Realization of such therapeutic nanodevices requires multidisciplinary effort that is difficult for individual device developers to sustain, and identification of appropriate collaborations outside ones own field can itself be challenging. Further, as in vivo nanodevices become increasingly complex, their design will increasingly demand systems level thinking. System engineering tools such as object-oriented analysis, object-oriented design (OOA/D) and unified modeling language (UML) are applicable to nanodevices built from biological components, help logically manage the knowledge needed to design them, and help identify useful collaborative relationships for device designers. We demonstrate the utility of these systems engineering tools by reverse engineering an existing molecular device (the bacmid molecular cloning system) using them, and illustrate how object-oriented approaches identify fungible components (objects) in nanodevices in a way that facilitates design of families of related devices, rather than single inventions. We also explore the utility of object-oriented approaches for design of another class of therapeutic nanodevices, vaccines. While they are useful for design of current nanodevices, the power of systems design tools for biomedical nanotechnology will become increasingly apparent as the complexity and sophistication of in vivo nanosystems increases. The nested, hierarchical nature of object-oriented approaches allows treatment of devices as objects in higher-order structures, and so will facilitate concatenation of multiple devices into higher-order, higher-function nanosystems.

[Patient orientation and reference to the ICF as challenges in outcome assessment in rehabilitation].

PubMed

Farin, E

2008-04-01

Measuring the results of rehabilitation interventions presents a number of issues regarding content and method, two of which have been selected for discussion in view of the findings of current research--the significance of patient orientation and the relation to the International Classification of Functioning, Disability and Health, ICF. Compilation of patients' treatment goals, expectations and preferences, patient participation in the development of assessment instruments, compilation of patients' evaluations of treatment results and consideration of the subjectivity of the physician as well as interaction between patient and physician were discussed with respect to involving patients in measuring results. The ICF is a terminology system presenting a uniform international classification for describing health conditions that could assume the function of a common language for the members of various occupations involved in medical rehabilitation. Orienting the measurement of results to the ICF is an obvious next step. This can promote patient orientation, as the categories used by the ICF--in particular for the domains of activities and participation--are formulated in terms relevant to daily routine and are thus pertinent to the patients' lifestyle. The consequences resulting from this overview concern future research needs on the one hand, and on the other hand tips for carrying out a patient-oriented, ICF-based measurement of results. The need for research becomes especially clear regarding the measurement of results based on patient preferences, the determination of the participation relevance perceived by the patient as a criterion for "patient significance" (analogous to "clinical significance"), the integration and weighting of patient and physician assessments of success, the consideration of physicians' subjective concepts and patients' communication preferences, and the design of new, ICF-oriented assessment instruments.

Linguistic Relativity in Japanese and English: Is Language the Primary Determinant in Object Classification?

ERIC Educational Resources Information Center

Mazuka, Reiko; Friedman, Ronald S.

2000-01-01

Tested claims by Lucy (1992a, 1992b) that differences between the number marking systems used by Yucatec Maya and English lead speakers of these languages to differentially attend to either the material composition or the shape of objects. Replicated Lucy's critical objects' classification experiments using speakers of English and Japanese.…

Classification of malignant and benign liver tumors using a radiomics approach

NASA Astrophysics Data System (ADS)

Starmans, Martijn P. A.; Miclea, Razvan L.; van der Voort, Sebastian R.; Niessen, Wiro J.; Thomeer, Maarten G.; Klein, Stefan

2018-03-01

Correct diagnosis of the liver tumor phenotype is crucial for treatment planning, especially the distinction between malignant and benign lesions. Clinical practice includes manual scoring of the tumors on Magnetic Resonance (MR) images by a radiologist. As this is challenging and subjective, it is often followed by a biopsy. In this study, we propose a radiomics approach as an objective and non-invasive alternative for distinguishing between malignant and benign phenotypes. T2-weighted (T2w) MR sequences of 119 patients from multiple centers were collected. We developed an efficient semi-automatic segmentation method, which was used by a radiologist to delineate the tumors. Within these regions, features quantifying tumor shape, intensity, texture, heterogeneity and orientation were extracted. Patient characteristics and semantic features were added for a total of 424 features. Classification was performed using Support Vector Machines (SVMs). The performance was evaluated using internal random-split cross-validation. On the training set within each iteration, feature selection and hyperparameter optimization were performed. To this end, another cross validation was performed by splitting the training sets in training and validation parts. The optimal settings were evaluated on the independent test sets. Manual scoring by a radiologist was also performed. The radiomics approach resulted in 95% confidence intervals of the AUC of [0.75, 0.92], specificity [0.76, 0.96] and sensitivity [0.52, 0.82]. These approach the performance of the radiologist, which were an AUC of 0.93, specificity 0.70 and sensitivity 0.93. Hence, radiomics has the potential to predict the liver tumor benignity in an objective and non-invasive manner.

Three-dimensional obstacle classification in laser range data

NASA Astrophysics Data System (ADS)

Armbruster, Walter; Bers, Karl-Heinz

1998-10-01

The threat of hostile surveillance and weapon systems require military aircraft to fly under extreme conditions such as low altitude, high speed, poor visibility and incomplete terrain information. The probability of collision with natural and man-made obstacles during such contour missions is high if detection capability is restricted to conventional vision aids. Forward-looking scanning laser rangefinders which are presently being flight tested and evaluated at German proving grounds, provide a possible solution, having a large field of view, high angular and range resolution, a high pulse repetition rate, and sufficient pulse energy to register returns from wires at over 500 m range (depends on the system) with a high hit-and-detect probability. Despite the efficiency of the sensor, acceptance of current obstacle warning systems by test pilots is not very high, mainly due to the systems' inadequacies in obstacle recognition and visualization. This has motivated the development and the testing of more advanced 3d-scene analysis algorithm at FGAN-FIM to replace the obstacle recognition component of current warning systems. The basic ideas are to increase the recognition probability and to reduce the false alarm rate for hard-to-extract obstacles such as wires, by using more readily recognizable objects such as terrain, poles, pylons, trees, etc. by implementing a hierarchical classification procedure to generate a parametric description of the terrain surface as well as the class, position, orientation, size and shape of all objects in the scene. The algorithms can be used for other applications such as terrain following, autonomous obstacle avoidance, and automatic target recognition.

The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies

NASA Astrophysics Data System (ADS)

Hu, B.; Li, J.; Wang, J.; Hall, B.

2014-11-01

The objectives of this study were to exploit Light Detection And Ranging (LiDAR) and very high spatial resolution (VHR) data and their synergy with hyperspectral imagery in the early detection of the EAB presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, individual trees were first extracted and then classified into different species based on their spectral information derived from hyperspectral imagery, spatial information from VHR imagery, and for each ash tree its health state and EAB infestation stage were determined based on hyperspectral imagery. The developed framework and methods were demonstrated to be effective according to the results obtained on two study sites in the city of Toronto, Ontario Canada. The individual tree delineation method provided satisfactory results with an overall accuracy of 78 % and 19 % commission and 23 % omission errors when used on the combined very high-spatial resolution imagery and LiDAR data. In terms of the identification of ash trees, given sufficient representative training data, our classification model was able to predict tree species with above 75 % overall accuracy, and mis-classification occurred mainly between ash and maple trees. The hypothesis that a strong correlation exists between general tree stress and EAB infestation was confirmed. Vegetation indices sensitive to leaf chlorophyll content derived from hyperspectral imagery can be used to predict the EAB infestation levels for each ash tree.

Sexual Orientation and Spatial Position Effects on Selective Forms of Object Location Memory

ERIC Educational Resources Information Center

Rahman, Qazi; Newland, Cherie; Smyth, Beatrice Mary

2011-01-01

Prior research has demonstrated robust sex and sexual orientation-related differences in object location memory in humans. Here we show that this sexual variation may depend on the spatial position of target objects and the task-specific nature of the spatial array. We tested the recovery of object locations in three object arrays (object…

14 CFR 16 - Objective Classification-Discontinued Operations

Code of Federal Regulations, 2012 CFR

2012-01-01

... CERTIFICATED AIR CARRIERS Profit and Loss Classification Section 16 Objective Classification—Discontinued... the disposal of investor controlled companies and nontransport ventures whether sold, abandoned, spun... transport or transport-related operations. (b) This account shall be subdivided as follows by all air...

14 CFR Section 16 - Objective Classification-Discontinued Operations

Code of Federal Regulations, 2011 CFR

2011-01-01

... AIR CARRIERS Profit and Loss Classification Section 16 Objective Classification—Discontinued... the disposal of investor controlled companies and nontransport ventures whether sold, abandoned, spun... transport or transport-related operations. (b) This account shall be subdivided as follows by all air...

Perceived Average Orientation Reflects Effective Gist of the Surface.

PubMed

Cha, Oakyoon; Chong, Sang Chul

2018-03-01

The human ability to represent ensemble visual information, such as average orientation and size, has been suggested as the foundation of gist perception. To effectively summarize different groups of objects into the gist of a scene, observers should form ensembles separately for different groups, even when objects have similar visual features across groups. We hypothesized that the visual system utilizes perceptual groups characterized by spatial configuration and represents separate ensembles for different groups. Therefore, participants could not integrate ensembles of different perceptual groups on a task basis. We asked participants to determine the average orientation of visual elements comprising a surface with a contour situated inside. Although participants were asked to estimate the average orientation of all the elements, they ignored orientation signals embedded in the contour. This constraint may help the visual system to keep the visual features of occluding objects separate from those of the occluded objects.

Parameterized hardware description as object oriented hardware model implementation

NASA Astrophysics Data System (ADS)

Drabik, Pawel K.

2010-09-01

The paper introduces novel model for design, visualization and management of complex, highly adaptive hardware systems. The model settles component oriented environment for both hardware modules and software application. It is developed on parameterized hardware description research. Establishment of stable link between hardware and software, as a purpose of designed and realized work, is presented. Novel programming framework model for the environment, named Graphic-Functional-Components is presented. The purpose of the paper is to present object oriented hardware modeling with mentioned features. Possible model implementation in FPGA chips and its management by object oriented software in Java is described.

Study to validate the outcome goal, competencies and educational objectives for use in intensive care orientation programs.

PubMed

Boyle, M; Butcher, R; Kenney, C

1998-03-01

Intensive care orientation programs have become an accepted component of intensive care education. To date, however, there have been no Australian-based standards defining the appropriate level of competence to be attained upon completion of orientation. The aim of this study was to validate a set of aims, competencies and educational objectives that could form the basis of intensive care orientation and which would ensure an outcome standard of safe and effective practice. An initial document containing a statement of the desired outcome goal, six competency statements and 182 educational objectives was developed through a review of the orientation programs developed by the investigators. The Delphi technique was used to gain consensus among 13 nurses recognised for their expertise in intensive care education. The expert group rated the acceptability of each of the study items and provided suggestions for objectives to be included. An approval rating of 80 per cent was required to retain each of the study items, with the document refined through three Delphi rounds. The final document contains a validated statement of outcome goal, competencies and educational objectives for intensive care orientation programs.

Common arc method for diffraction pattern orientation.

PubMed

Bortel, Gábor; Tegze, Miklós

2011-11-01

Very short pulses of X-ray free-electron lasers opened the way to obtaining diffraction signal from single particles beyond the radiation dose limit. For three-dimensional structure reconstruction many patterns are recorded in the object's unknown orientation. A method is described for the orientation of continuous diffraction patterns of non-periodic objects, utilizing intensity correlations in the curved intersections of the corresponding Ewald spheres, and hence named the common arc orientation method. The present implementation of the algorithm optionally takes into account Friedel's law, handles missing data and is capable of determining the point group of symmetric objects. Its performance is demonstrated on simulated diffraction data sets and verification of the results indicates a high orientation accuracy even at low signal levels. The common arc method fills a gap in the wide palette of orientation methods. © 2011 International Union of Crystallography

Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm.

PubMed

Al-Saffar, Ahmed; Awang, Suryanti; Tao, Hai; Omar, Nazlia; Al-Saiagh, Wafaa; Al-Bared, Mohammed

2018-01-01

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach.

Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm

PubMed Central

Awang, Suryanti; Tao, Hai; Omar, Nazlia; Al-Saiagh, Wafaa; Al-bared, Mohammed

2018-01-01

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach. PMID:29684036

Integrating end-to-end threads of control into object-oriented analysis and design

NASA Technical Reports Server (NTRS)

Mccandlish, Janet E.; Macdonald, James R.; Graves, Sara J.

1993-01-01

Current object-oriented analysis and design methodologies fall short in their use of mechanisms for identifying threads of control for the system being developed. The scenarios which typically describe a system are more global than looking at the individual objects and representing their behavior. Unlike conventional methodologies that use data flow and process-dependency diagrams, object-oriented methodologies do not provide a model for representing these global threads end-to-end. Tracing through threads of control is key to ensuring that a system is complete and timing constraints are addressed. The existence of multiple threads of control in a system necessitates a partitioning of the system into processes. This paper describes the application and representation of end-to-end threads of control to the object-oriented analysis and design process using object-oriented constructs. The issue of representation is viewed as a grouping problem, that is, how to group classes/objects at a higher level of abstraction so that the system may be viewed as a whole with both classes/objects and their associated dynamic behavior. Existing object-oriented development methodology techniques are extended by adding design-level constructs termed logical composite classes and process composite classes. Logical composite classes are design-level classes which group classes/objects both logically and by thread of control information. Process composite classes further refine the logical composite class groupings by using process partitioning criteria to produce optimum concurrent execution results. The goal of these design-level constructs is to ultimately provide the basis for a mechanism that can support the creation of process composite classes in an automated way. Using an automated mechanism makes it easier to partition a system into concurrently executing elements that can be run in parallel on multiple processors.

The Usefulness of Learning Objects in Industry Oriented Learning Environments

ERIC Educational Resources Information Center

Fernando, Shantha; Sol, Henk; Dahanayake, Ajantha

2012-01-01

A model is presented to evaluate the usefulness of learning objects for industry oriented learning environments that emphasise training university graduates for job opportunities in a competitive industry oriented economy. Knowledge workers of the industry seek continuous professional development to keep their skills and knowledge up to date. Many…

Object-oriented millisecond timers for the PC.

PubMed

Hamm, J P

2001-11-01

Object-oriented programming provides a useful structure for designing reusable code. Accurate millisecond timing is essential for many areas of research. With this in mind, this paper provides a Turbo Pascal unit containing an object-oriented millisecond timer. This approach allows for multiple timers to be running independently. The timers may also be set at different levels of temporal precision, such as 10(-3) (milliseconds) or 10(-5) sec. The object also is able to store the time of a flagged event for later examination without interrupting the ongoing timing operation.

Advanced Computing Technologies for Rocket Engine Propulsion Systems: Object-Oriented Design with C++

NASA Technical Reports Server (NTRS)

Bekele, Gete

2002-01-01

This document explores the use of advanced computer technologies with an emphasis on object-oriented design to be applied in the development of software for a rocket engine to improve vehicle safety and reliability. The primary focus is on phase one of this project, the smart start sequence module. The objectives are: 1) To use current sound software engineering practices, object-orientation; 2) To improve on software development time, maintenance, execution and management; 3) To provide an alternate design choice for control, implementation, and performance.

Simplified Rotation In Acoustic Levitation

NASA Technical Reports Server (NTRS)

Barmatz, M. B.; Gaspar, M. S.; Trinh, E. H.

1989-01-01

New technique based on old discovery used to control orientation of object levitated acoustically in axisymmetric chamber. Method does not require expensive equipment like additional acoustic drivers of precisely adjustable amplitude, phase, and frequency. Reflecting object acts as second source of sound. If reflecting object large enough, close enough to levitated object, or focuses reflected sound sufficiently, Rayleigh torque exerted on levitated object by reflected sound controls orientation of object.

Persistent maritime surveillance using multi-sensor feature association and classification

NASA Astrophysics Data System (ADS)

van den Broek, Sebastiaan P.; Schwering, Piet B. W.; Liem, Kwan D.; Schleijpen, Ric

2012-06-01

In maritime operational scenarios, such as smuggling, piracy, or terrorist threats, it is not only relevant who or what an observed object is, but also where it is now and in the past in relation to other (geographical) objects. In situation and impact assessment, this information is used to determine whether an object is a threat. Single platform (ship, harbor) or single sensor information will not provide all this information. The work presented in this paper focuses on the sensor and object levels that provide a description of currently observed objects to situation assessment. For use of information of objects at higher information levels, it is necessary to have not only a good description of observed objects at this moment, but also from its past. Therefore, currently observed objects have to be linked to previous occurrences. Kinematic features, as used in tracking, are of limited use, as uncertainties over longer time intervals are so large that no unique associations can be made. Features extracted from different sensors (e.g., ESM, EO/IR) can be used for both association and classification. Features and classifications are used to associate current objects to previous object descriptions, allowing objects to be described better, and provide position history. In this paper a description of a high level architecture in which such a multi-sensor association is used is described. Results of an assessment of the usability of several features from ESM (from spectrum), EO and IR (shape, contour, keypoints) data for association and classification are shown.

Prototyping Visual Database Interface by Object-Oriented Language

DTIC Science & Technology

1988-06-01

approach is to use object-oriented programming. Object-oriented languages are characterized by three criteria [Ref. 4:p. 1.2.1]: - encapsulation of...made it a sub-class of our DMWindow.Cls, which is discussed later in this chapter. This extension to the application had to be intergrated with our... abnormal behaviors similar to Korth’s discussion of pitfalls in relational database designing. Even extensions like GEM [Ref. 8] that are powerful and

SIMOGEN - An Object-Oriented Language for Simulation

DTIC Science & Technology

1989-03-01

program generator must also be written in the same prcgramming languaje . In this case, the C language was chosen, for the following main reasons...3), March 88. 4. PRESTO: A System for Object-Oriented Parallel Programing B N Bershad, E D Lazowska & H M Levy Software Practice and Experience, Vol...U.S. Depare nt of Defence ANSI/ML-STD 1815A. 7. Object-oriented Development Grady Booch Transactions on Software Engineering , February 86. 8. A

An Improved Suite of Object Oriented Software Measures

NASA Technical Reports Server (NTRS)

Neal, Ralph D.; Weistroffer, H. Roland; Coppins, Richard J.

1997-01-01

In the pursuit of ever increasing productivity, the need to be able to measure specific aspects of software is generally agreed upon. As object oriented programming languages are becoming more and more widely used, metrics specifically designed for object oriented software are required. In recent years there has been an explosion of new, object oriented software metrics proposed in the literature. Unfortunately, many or most of these proposed metrics have not been validated to measure what they claim to measure. In fact, an analysis of many of these metrics shows that they do not satisfy basic properties of measurement theory, and thus their application has to be suspect. In this paper ten improved metrics are proposed and are validated using measurement theory.

Direct evaluation of fault trees using object-oriented programming techniques

NASA Technical Reports Server (NTRS)

Patterson-Hine, F. A.; Koen, B. V.

1989-01-01

Object-oriented programming techniques are used in an algorithm for the direct evaluation of fault trees. The algorithm combines a simple bottom-up procedure for trees without repeated events with a top-down recursive procedure for trees with repeated events. The object-oriented approach results in a dynamic modularization of the tree at each step in the reduction process. The algorithm reduces the number of recursive calls required to solve trees with repeated events and calculates intermediate results as well as the solution of the top event. The intermediate results can be reused if part of the tree is modified. An example is presented in which the results of the algorithm implemented with conventional techniques are compared to those of the object-oriented approach.

Object orientation affects spatial language comprehension.

PubMed

Burigo, Michele; Sacchi, Simona

2013-01-01

Typical spatial descriptions, such as "The car is in front of the house," describe the position of a located object (LO; e.g., the car) in space relative to a reference object (RO) whose location is known (e.g., the house). The orientation of the RO affects spatial language comprehension via the reference frame selection process. However, the effects of the LO's orientation on spatial language have not received great attention. This study explores whether the pure geometric information of the LO (e.g., its orientation) affects spatial language comprehension using placing and production tasks. Our results suggest that the orientation of the LO influences spatial language comprehension even in the absence of functional relationships. Copyright © 2013 Cognitive Science Society, Inc.

Towards human behavior recognition based on spatio temporal features and support vector machines

NASA Astrophysics Data System (ADS)

Ghabri, Sawsen; Ouarda, Wael; Alimi, Adel M.

2017-03-01

Security and surveillance are vital issues in today's world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

User oriented ERTS-1 images. [vegetation identification in Canada through image enhancement

NASA Technical Reports Server (NTRS)

Shlien, S.; Goodenough, D.

1974-01-01

Photographic reproduction of ERTS-1 images are capable of displaying only a portion of the total information available from the multispectral scanner. Methods are being developed to generate ERTS-1 images oriented towards special users such as agriculturists, foresters, and hydrologists by applying image enhancement techniques and interactive statistical classification schemes. Spatial boundaries and linear features can be emphasized and delineated using simple filters. Linear and nonlinear transformations can be applied to the spectral data to emphasize certain ground information. An automatic classification scheme was developed to identify particular ground cover classes such as fallow, grain, rape seed or various vegetation covers. The scheme applies the maximum likelihood decision rule to the spectral information and classifies the ERTS-1 image on a pixel by pixel basis. Preliminary results indicate that the classifier has limited success in distinguishing crops, but is well adapted for identifying different types of vegetation.

OOPs!

ERIC Educational Resources Information Center

Margush, Tim

2001-01-01

Discussion of Object Oriented Programming (OOP) focuses on criticism of an earlier article that addressed problems of applying specific functionality to controls across several forms in a Visual Basic project. Examines the Object Oriented techniques, inheritance and composition, commonly employed to extend the functionality of an object.…

Model-based object classification using unification grammars and abstract representations

NASA Astrophysics Data System (ADS)

Liburdy, Kathleen A.; Schalkoff, Robert J.

1993-04-01

The design and implementation of a high level computer vision system which performs object classification is described. General object labelling and functional analysis require models of classes which display a wide range of geometric variations. A large representational gap exists between abstract criteria such as `graspable' and current geometric image descriptions. The vision system developed and described in this work addresses this problem and implements solutions based on a fusion of semantics, unification, and formal language theory. Object models are represented using unification grammars, which provide a framework for the integration of structure and semantics. A methodology for the derivation of symbolic image descriptions capable of interacting with the grammar-based models is described and implemented. A unification-based parser developed for this system achieves object classification by determining if the symbolic image description can be unified with the abstract criteria of an object model. Future research directions are indicated.

An application of object-oriented knowledge representation to engineering expert systems

NASA Technical Reports Server (NTRS)

Logie, D. S.; Kamil, H.; Umaretiya, J. R.

1990-01-01

The paper describes an object-oriented knowledge representation and its application to engineering expert systems. The object-oriented approach promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects and organized by defining relationships between the objects. An Object Representation Language (ORL) was implemented as a tool for building and manipulating the object base. Rule-based knowledge representation is then used to simulate engineering design reasoning. Using a common object base, very large expert systems can be developed, comprised of small, individually processed, rule sets. The integration of these two schemes makes it easier to develop practical engineering expert systems. The general approach to applying this technology to the domain of the finite element analysis, design, and optimization of aerospace structures is discussed.

Development of Automated Image Analysis Software for Suspended Marine Particle Classification

DTIC Science & Technology

2002-09-30

Development of Automated Image Analysis Software for Suspended Marine Particle Classification Scott Samson Center for Ocean Technology...and global water column. 1 OBJECTIVES The project’s objective is to develop automated image analysis software to reduce the effort and time

40 CFR 164.22 - Contents of document setting forth objections.

Code of Federal Regulations, 2011 CFR

2011-07-01

... objections. 164.22 Section 164.22 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... registration, or change the classification of a pesticide, shall clearly and concisely set forth such...

40 CFR 164.22 - Contents of document setting forth objections.

Code of Federal Regulations, 2010 CFR

2010-07-01

... objections. 164.22 Section 164.22 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... registration, or change the classification of a pesticide, shall clearly and concisely set forth such...

Cognitive Precursors of Receptive vs. Expressive Language.

ERIC Educational Resources Information Center

Smolak, Linda

1982-01-01

The relationship of object permanence and classification skills to receptive and expressive language development was investigated in infants. Object permanence, classification, and parent-child verbal interaction ratings were about equally related to language comprehension functioning, while permanence was more strongly related to language…

Predictive Models of target organ and Systemic toxicities (BOSC)

EPA Science Inventory

The objective of this work is to predict the hazard classification and point of departure (PoD) of untested chemicals in repeat-dose animal testing studies. We used supervised machine learning to objectively evaluate the predictive accuracy of different classification and regress...

Orthostatic hypotension

NASA Technical Reports Server (NTRS)

Ninet, J.

1981-01-01

Basic orientation of the article, by the leader of a group of medical researchers associated with hospitals in Lyon, France, is toward definition and classification. A table divides OH (orthostatic hypotension) according to physiopathological classification into sympathicotonic and asympathicotonic types and then each of these into primary and secondary with subdivisions. The figure sketches organization and functioning of the baroreflex arc. Applications to clinical study of circulatory reflexes, listing measurement tests and the biological study of hormonal regulation listing the appropriate kinds of studies. Data are not given.

Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians

PubMed Central

Chen, Wei-Hsin; Hsu, Kai-Ping; Chen, Han-Ping; Su, Xing-Yu; Tseng, Yi-Ju; Chien, Yin-Hsiu; Hwu, Wuh-Liang; Lai, Feipei

2013-01-01

Background A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. Objective The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. Methods The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. Results The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. Conclusions This SOA Web service–based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically. PMID:23702487

Caracterisation des occupations du sol en milieu urbain par imagerie radar

NASA Astrophysics Data System (ADS)

Codjia, Claude

This study aims to test the relevance of medium and high-resolution SAR images on the characterization of the types of land use in urban areas. To this end, we have relied on textural approaches based on second-order statistics. Specifically, we look for texture parameters most relevant for discriminating urban objects. We have used in this regard Radarsat-1 in fine polarization mode and Radarsat-2 HH fine mode in dual and quad polarization and ultrafine mode HH polarization. The land uses sought were dense building, medium density building, low density building, industrial and institutional buildings, low density vegetation, dense vegetation and water. We have identified nine texture parameters for analysis, grouped into families according to their mathematical definitions in a first step. The parameters of similarity / dissimilarity include Homogeneity, Contrast, the Differential Inverse Moment and Dissimilarity. The parameters of disorder are Entropy and the Second Angular Momentum. The Standard Deviation and Correlation are the dispersion parameters and the Average is a separate family. It is clear from experience that certain combinations of texture parameters from different family used in classifications yield good results while others produce kappa of very little interest. Furthermore, we realize that if the use of several texture parameters improves classifications, its performance ceils from three parameters. The calculation of correlations between the textures and their principal axes confirm the results. Despite the good performance of this approach based on the complementarity of texture parameters, systematic errors due to the cardinal effects remain on classifications. To overcome this problem, a radiometric compensation model was developed based on the radar cross section (SER). A radar simulation from the digital surface model of the environment allowed us to extract the building backscatter zones and to analyze the related backscatter. Thus, we were able to devise a strategy of compensation of cardinal effects solely based on the responses of the objects according to their orientation from the plane of illumination through the radar's beam. It appeared that a compensation algorithm based on the radar cross section was appropriate. Some examples of the application of this algorithm on HH polarized RADARSAT-2 images are presented as well. Application of this algorithm will allow considerable gains with regard to certain forms of automation (classification and segmentation) at the level of radar imagery thus generating a higher level of quality in regard to visual interpretation. Application of this algorithm on RADARSAT-1 and RADARSAT-2 images with HH, HV, VH, and VV polarisations helped make considerable gains and eliminate most of the classification errors due to the cardinal effects.

Object-Based Classification as an Alternative Approach to the Traditional Pixel-Based Classification to Identify Potential Habitat of the Grasshopper Sparrow

NASA Astrophysics Data System (ADS)

Jobin, Benoît; Labrecque, Sandra; Grenier, Marcelle; Falardeau, Gilles

2008-01-01

The traditional method of identifying wildlife habitat distribution over large regions consists of pixel-based classification of satellite images into a suite of habitat classes used to select suitable habitat patches. Object-based classification is a new method that can achieve the same objective based on the segmentation of spectral bands of the image creating homogeneous polygons with regard to spatial or spectral characteristics. The segmentation algorithm does not solely rely on the single pixel value, but also on shape, texture, and pixel spatial continuity. The object-based classification is a knowledge base process where an interpretation key is developed using ground control points and objects are assigned to specific classes according to threshold values of determined spectral and/or spatial attributes. We developed a model using the eCognition software to identify suitable habitats for the Grasshopper Sparrow, a rare and declining species found in southwestern Québec. The model was developed in a region with known breeding sites and applied on other images covering adjacent regions where potential breeding habitats may be present. We were successful in locating potential habitats in areas where dairy farming prevailed but failed in an adjacent region covered by a distinct Landsat scene and dominated by annual crops. We discuss the added value of this method, such as the possibility to use the contextual information associated to objects and the ability to eliminate unsuitable areas in the segmentation and land cover classification processes, as well as technical and logistical constraints. A series of recommendations on the use of this method and on conservation issues of Grasshopper Sparrow habitat is also provided.

A method of constructing geo-object ontology in disaster system for prevention and decrease

NASA Astrophysics Data System (ADS)

Li, Bin; Liu, Jiping; Shi, Lihong; Wang, Zhenfeng

2009-10-01

A kind of formal system, which can express clearly a certain entity or information, is needed to express geographical concept. Besides, some rules explaining the interrelationship and action between different components are also required. Therefore, the conception of geo-object ontology is introduced. It is a shared formalization and display specification of conceptual knowledge system in the field of concrete application of spatial information science. It can constitute hierarchy structure, which derives from the concept classification system in the geographical area. Its concepts can be described by the property. Property sets can form a vector space with multi-dimensional characteristics. Geographic space is composed of different types of geographic entities. And its concept is formed by a series of geographic entities with the same properties and actions. Moreover, each of the geographic entities can be mapped to an object, and each object has its spatial property, time information and topology, semantic relationships associated with other objects. The biggest difference between ecumenical information ontology and geo-ontology is that the latter has the spatial characteristics. During the construction process of geo-object ontology, some important components, such as geographic type, spatial relation, spatial entity type and coordinates, time, should be included to make further research. Here, taking disaster as an example, by using Protégé and OWL, combined methods used by constructing the geo-object ontology in the form of being manual made by domanial experts and semi-automatic are investigated oriented to disaster to serve ultimately geographic information retrieval service driven by ontology.

A contour-based shape descriptor for biomedical image classification and retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Contours, object blobs, and specific feature points are utilized to represent object shapes and extract shape descriptors that can then be used for object detection or image classification. In this research we develop a shape descriptor for biomedical image type (or, modality) classification. We adapt a feature extraction method used in optical character recognition (OCR) for character shape representation, and apply various image preprocessing methods to successfully adapt the method to our application. The proposed shape descriptor is applied to radiology images (e.g., MRI, CT, ultrasound, X-ray, etc.) to assess its usefulness for modality classification. In our experiment we compare our method with other visual descriptors such as CEDD, CLD, Tamura, and PHOG that extract color, texture, or shape information from images. The proposed method achieved the highest classification accuracy of 74.1% among all other individual descriptors in the test, and when combined with CSD (color structure descriptor) showed better performance (78.9%) than using the shape descriptor alone.

ON DEPARTURES FROM INDEPENDENCE IN CROSS-CLASSIFICATIONS.

ERIC Educational Resources Information Center

CASE, C. MARSTON

THIS NOTE IS CONCERNED WITH IDEAS AND PROBLEMS INVOLVED IN CROSS-CLASSIFICATION OF OBSERVATIONS ON A GIVEN POPULATION, ESPECIALLY TWO-DIMENSIONAL CROSS-CLASSIFICATIONS. MAIN OBJECTIVES OF THE NOTE INCLUDE--(1) ESTABLISHMENT OF A CONCEPTUAL FRAMEWORK FOR CHARACTERIZATION AND COMPARISON OF CROSS-CLASSIFICATIONS, (2) DISCUSSION OF EXISTING METHODS…

Average Likelihood Methods of Classification of Code Division Multiple Access (CDMA)

DTIC Science & Technology

2016-05-01

case of cognitive radio applications. Modulation classification is part of a broader problem known as blind or uncooperative demodulation the goal of...Introduction 2 2.1 Modulation Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Research Objectives...6 3 Modulation Classification Methods 7 3.0.1 Ad Hoc

The Effects of Object Orientation and Object Type on Children's Interpretation of the Word BIG.

ERIC Educational Resources Information Center

Coley, John D.; Gelman, Susan A.

1989-01-01

Investigated the interpretation of the word "big" by 40 children of 3 to 5 years. The type and orientation of objects used in the study were varied. Results demonstrated that contextual factors influenced children's responses. (RJC)

Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images †

PubMed Central

Ran, Lingyan; Zhang, Yanning; Zhang, Qilin; Yang, Tao

2017-01-01

Vision-based mobile robot navigation is a vibrant area of research with numerous algorithms having been developed, the vast majority of which either belong to the scene-oriented simultaneous localization and mapping (SLAM) or fall into the category of robot-oriented lane-detection/trajectory tracking. These methods suffer from high computational cost and require stringent labelling and calibration efforts. To address these challenges, this paper proposes a lightweight robot navigation framework based purely on uncalibrated spherical images. To simplify the orientation estimation, path prediction and improve computational efficiency, the navigation problem is decomposed into a series of classification tasks. To mitigate the adverse effects of insufficient negative samples in the “navigation via classification” task, we introduce the spherical camera for scene capturing, which enables 360° fisheye panorama as training samples and generation of sufficient positive and negative heading directions. The classification is implemented as an end-to-end Convolutional Neural Network (CNN), trained on our proposed Spherical-Navi image dataset, whose category labels can be efficiently collected. This CNN is capable of predicting potential path directions with high confidence levels based on a single, uncalibrated spherical image. Experimental results demonstrate that the proposed framework outperforms competing ones in realistic applications. PMID:28604624

A Classification of Statistics Courses (A Framework for Studying Statistical Education)

ERIC Educational Resources Information Center

Turner, J. C.

1976-01-01

A classification of statistics courses in presented, with main categories of "course type,""methods of presentation,""objectives," and "syllabus." Examples and suggestions for uses of the classification are given. (DT)

Two-stage neural-network-based technique for Urdu character two-dimensional shape representation, classification, and recognition

NASA Astrophysics Data System (ADS)

Megherbi, Dalila B.; Lodhi, S. M.; Boulenouar, A. J.

2001-03-01

This work is in the field of automated document processing. This work addresses the problem of representation and recognition of Urdu characters using Fourier representation and a Neural Network architecture. In particular, we show that a two-stage Neural Network scheme is used here to make classification of 36 Urdu characters into seven sub-classes namely subclasses characterized by seven proposed and defined fuzzy features specifically related to Urdu characters. We show that here Fourier Descriptors and Neural Network provide a remarkably simple way to draw definite conclusions from vague, ambiguous, noisy or imprecise information. In particular, we illustrate the concept of interest regions and describe a framing method that provides a way to make the proposed technique for Urdu characters recognition robust and invariant to scaling and translation. We also show that a given character rotation is dealt with by using the Hotelling transform. This transform is based upon the eigenvalue decomposition of the covariance matrix of an image, providing a method of determining the orientation of the major axis of an object within an image. Finally experimental results are presented to show the power and robustness of the proposed two-stage Neural Network based technique for Urdu character recognition, its fault tolerance, and high recognition accuracy.

Real-time road detection in infrared imagery

NASA Astrophysics Data System (ADS)

Andre, Haritini E.; McCoy, Keith

1990-09-01

Automatic road detection is an important part in many scene recognition applications. The extraction of roads provides a means of navigation and position update for remotely piloted vehicles or autonomous vehicles. Roads supply strong contextual information which can be used to improve the performance of automatic target recognition (ATh) systems by directing the search for targets and adjusting target classification confidences. This paper will describe algorithmic techniques for labeling roads in high-resolution infrared imagery. In addition, realtime implementation of this structural approach using a processor array based on the Martin Marietta Geometric Arithmetic Parallel Processor (GAPPTh) chip will be addressed. The algorithm described is based on the hypothesis that a road consists of pairs of line segments separated by a distance "d" with opposite gradient directions (antiparallel). The general nature of the algorithm, in addition to its parallel implementation in a single instruction, multiple data (SIMD) machine, are improvements to existing work. The algorithm seeks to identify line segments meeting the road hypothesis in a manner that performs well, even when the side of the road is fragmented due to occlusion or intersections. The use of geometrical relationships between line segments is a powerful yet flexible method of road classification which is independent of orientation. In addition, this approach can be used to nominate other types of objects with minor parametric changes.

Use of topographic and climatological models in a geographical data base to improve Landsat MSS classification for Olympic National Park

NASA Technical Reports Server (NTRS)

Cibula, William G.; Nyquist, Maurice O.

1987-01-01

An unsupervised computer classification of vegetation/landcover of Olympic National Park and surrounding environs was initially carried out using four bands of Landsat MSS data. The primary objective of the project was to derive a level of landcover classifications useful for park management applications while maintaining an acceptably high level of classification accuracy. Initially, nine generalized vegetation/landcover classes were derived. Overall classification accuracy was 91.7 percent. In an attempt to refine the level of classification, a geographic information system (GIS) approach was employed. Topographic data and watershed boundaries (inferred precipitation/temperature) data were registered with the Landsat MSS data. The resultant boolean operations yielded 21 vegetation/landcover classes while maintaining the same level of classification accuracy. The final classification provided much better identification and location of the major forest types within the park at the same high level of accuracy, and these met the project objective. This classification could now become inputs into a GIS system to help provide answers to park management coupled with other ancillary data programs such as fire management.

Object-oriented fault tree models applied to system diagnosis

NASA Technical Reports Server (NTRS)

Iverson, David L.; Patterson-Hine, F. A.

1990-01-01

When a diagnosis system is used in a dynamic environment, such as the distributed computer system planned for use on Space Station Freedom, it must execute quickly and its knowledge base must be easily updated. Representing system knowledge as object-oriented augmented fault trees provides both features. The diagnosis system described here is based on the failure cause identification process of the diagnostic system described by Narayanan and Viswanadham. Their system has been enhanced in this implementation by replacing the knowledge base of if-then rules with an object-oriented fault tree representation. This allows the system to perform its task much faster and facilitates dynamic updating of the knowledge base in a changing diagnosis environment. Accessing the information contained in the objects is more efficient than performing a lookup operation on an indexed rule base. Additionally, the object-oriented fault trees can be easily updated to represent current system status. This paper describes the fault tree representation, the diagnosis algorithm extensions, and an example application of this system. Comparisons are made between the object-oriented fault tree knowledge structure solution and one implementation of a rule-based solution. Plans for future work on this system are also discussed.

Exploring point-cloud features from partial body views for gender classification

NASA Astrophysics Data System (ADS)

Fouts, Aaron; McCoppin, Ryan; Rizki, Mateen; Tamburino, Louis; Mendoza-Schrock, Olga

2012-06-01

In this paper we extend a previous exploration of histogram features extracted from 3D point cloud images of human subjects for gender discrimination. Feature extraction used a collection of concentric cylinders to define volumes for counting 3D points. The histogram features are characterized by a rotational axis and a selected set of volumes derived from the concentric cylinders. The point cloud images are drawn from the CAESAR anthropometric database provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International. This database contains approximately 4400 high resolution LIDAR whole body scans of carefully posed human subjects. Success from our previous investigation was based on extracting features from full body coverage which required integration of multiple camera images. With the full body coverage, the central vertical body axis and orientation are readily obtainable; however, this is not the case with a one camera view providing less than one half body coverage. Assuming that the subjects are upright, we need to determine or estimate the position of the vertical axis and the orientation of the body about this axis relative to the camera. In past experiments the vertical axis was located through the center of mass of torso points projected on the ground plane and the body orientation derived using principle component analysis. In a natural extension of our previous work to partial body views, the absence of rotational invariance about the cylindrical axis greatly increases the difficulty for gender classification. Even the problem of estimating the axis is no longer simple. We describe some simple feasibility experiments that use partial image histograms. Here, the cylindrical axis is assumed to be known. We also discuss experiments with full body images that explore the sensitivity of classification accuracy relative to displacements of the cylindrical axis. Our initial results provide the basis for further investigation of more complex partial body viewing problems and new methods for estimating the two position coordinates for the axis location and the unknown body orientation angle.

Orientation-Invariant Object Recognition: Evidence from Repetition Blindness

ERIC Educational Resources Information Center

Harris, Irina M.; Dux, Paul E.

2005-01-01

The question of whether object recognition is orientation-invariant or orientation-dependent was investigated using a repetition blindness (RB) paradigm. In RB, the second occurrence of a repeated stimulus is less likely to be reported, compared to the occurrence of a different stimulus, if it occurs within a short time of the first presentation.…

Clinical Views: Object-Oriented Views for Clinical Databases

PubMed Central

Portoni, Luisa; Combi, Carlo; Pinciroli, Francesco

1998-01-01

We present here a prototype of a clinical information system for the archiving and the management of multimedia and temporally-oriented clinical data related to PTCA patients. The system is based on an object-oriented DBMS and supports multiple views and view schemas on patients' data. Remote data access is supported too.

Career Orientation Curriculum Guide: 7-8.

ERIC Educational Resources Information Center

Willoughby-Eastlake School District, Willoughby, OH.

The Ohio Career Development Model at the 7th and 8th grade level, the career orientation segment, states that students are to be exposed or oriented to the 15 USOE occupational clusters. Units are outlined relating each subject area to a specific cluster or clusters. Each unit includes a developmental objective, related behavioral objectives, and…

Agricultural Land Use mapping by multi-sensor approach for hydrological water quality monitoring

NASA Astrophysics Data System (ADS)

Brodsky, Lukas; Kodesova, Radka; Kodes, Vit

2010-05-01

The main objective of this study is to demonstrate potential of operational use of the high and medium resolution remote sensing data for hydrological water quality monitoring by mapping agriculture intensity and crop structures. In particular use of remote sensing mapping for optimization of pesticide monitoring. The agricultural mapping task is tackled by means of medium spatial and high temporal resolution ESA Envisat MERIS FR images together with single high spatial resolution IRS AWiFS image covering the whole area of interest (the Czech Republic). High resolution data (e.g. SPOT, ALOS, Landsat) are often used for agricultural land use classification, but usually only at regional or local level due to data availability and financial constraints. AWiFS data (nominal spatial resolution 56 m) due to the wide satellite swath seems to be more suitable for use at national level. Nevertheless, one of the critical issues for such a classification is to have sufficient image acquisitions over the whole vegetation period to describe crop development in appropriate way. ESA MERIS middle-resolution data were used in several studies for crop classification. The high temporal and also spectral resolution of MERIS data has indisputable advantage for crop classification. However, spatial resolution of 300 m results in mixture signal in a single pixel. AWiFS-MERIS data synergy brings new perspectives in agricultural Land Use mapping. Also, the developed methodology procedure is fully compatible with future use of ESA (GMES) Sentinel satellite images. The applied methodology of hybrid multi-sensor approach consists of these main stages: a/ parcel segmentation and spectral pre-classification of high resolution image (AWiFS); b/ ingestion of middle resolution (MERIS) vegetation spectro-temporal features; c/ vegetation signatures unmixing; and d/ semantic object-oriented classification of vegetation classes into final classification scheme. These crop groups were selected to be classified: winter crops, spring crops, oilseed rape, legumes, summer and other crops. This study highlights operational potentials of high temporal full resolution MERIS images in agricultural land use monitoring. Practical application of this methodology is foreseen, among others, in the water quality monitoring. Effective pesticide monitoring relies also on spatial distribution of applied pesticides, which can be derived from crop - plant protection product relationship. Knowledge of areas with predominant occurrence of specific crop based on remote sensing data described above can be used for a forecast of probable plant protection product application, thus cost-effective pesticide monitoring. The remote sensing data used on a continuous basis can be used in other long-term water management issues and provide valuable data for decision makers. Acknowledgement: Authors acknowledge the financial support of the Ministry of Education, Youth and Sports of the Czech Republic (grants No. 2B06095 and No. MSM 6046070901). The study was also supported by ESA CAT-1 (ref. 4358) and SOSI projects (Spatial Observation Services and Infrastructure; ref. GSTP-RTDA-EOPG-SW-08-0004).

Modalities of Infant-Mother Interaction in Japanese, Japanese American Immigrant, and European American Dyads

PubMed Central

Bornstein, Marc H.; Cote, Linda R.; Haynes, O. Maurice; Suwalsky, Joan T. D.; Bakeman, Roger

2011-01-01

Cultural variation in relations and moment-to-moment contingencies of infant-mother person-oriented and object-oriented interactions were examined and compared in 118 Japanese, Japanese American immigrant, and European American dyads with 5.5-month-olds. Infant and mother person-oriented behaviors were positively related in all cultural groups, but infant and mother object-oriented behaviors were positively related only among European Americans. In all groups, infant and mother behaviors within each modality were mutually contingent. Culture moderated lead-lag relations: Japanese infants were more likely than their mothers to respond in object-oriented interactions, European American mothers were more likely than their infants to respond in person-oriented interactions. Japanese American dyads behaved more like European American dyads. Interaction, infant effects, and parent socialization findings are set in cultural and accultural models of transactions between young infants and their mothers. PMID:22860874

Etomica: an object-oriented framework for molecular simulation.

PubMed

Schultz, Andrew J; Kofke, David A

2015-03-30

We describe the design of an object-oriented library of software components that are suitable for constructing simulations of systems of interacting particles. The emphasis of the discussion is on the general design of the components and how they interact, and less on details of the programming interface or its implementation. Example code is provided as an aid to understanding object-oriented programming structures and to demonstrate how the framework is applied. © 2015 Wiley Periodicals, Inc.

Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image

NASA Astrophysics Data System (ADS)

Li, Long; Solana, Carmen; Canters, Frank; Kervyn, Matthieu

2017-10-01

Mapping lava flows using satellite images is an important application of remote sensing in volcanology. Several volcanoes have been mapped through remote sensing using a wide range of data, from optical to thermal infrared and radar images, using techniques such as manual mapping, supervised/unsupervised classification, and elevation subtraction. So far, spectral-based mapping applications mainly focus on the use of traditional pixel-based classifiers, without much investigation into the added value of object-based approaches and into advantages of using machine learning algorithms. In this study, Nyamuragira, characterized by a series of > 20 overlapping lava flows erupted over the last century, was used as a case study. The random forest classifier was tested to map lava flows based on pixels and objects. Image classification was conducted for the 20 individual flows and for 8 groups of flows of similar age using a Landsat 8 image and a DEM of the volcano, both at 30-meter spatial resolution. Results show that object-based classification produces maps with continuous and homogeneous lava surfaces, in agreement with the physical characteristics of lava flows, while lava flows mapped through the pixel-based classification are heterogeneous and fragmented including much "salt and pepper noise". In terms of accuracy, both pixel-based and object-based classification performs well but the former results in higher accuracies than the latter except for mapping lava flow age groups without using topographic features. It is concluded that despite spectral similarity, lava flows of contrasting age can be well discriminated and mapped by means of image classification. The classification approach demonstrated in this study only requires easily accessible image data and can be applied to other volcanoes as well if there is sufficient information to calibrate the mapping.

Attention to memory: orienting attention to sound object representations.

PubMed

Backer, Kristina C; Alain, Claude

2014-01-01

Despite a growing acceptance that attention and memory interact, and that attention can be focused on an active internal mental representation (i.e., reflective attention), there has been a paucity of work focusing on reflective attention to 'sound objects' (i.e., mental representations of actual sound sources in the environment). Further research on the dynamic interactions between auditory attention and memory, as well as its degree of neuroplasticity, is important for understanding how sound objects are represented, maintained, and accessed in the brain. This knowledge can then guide the development of training programs to help individuals with attention and memory problems. This review article focuses on attention to memory with an emphasis on behavioral and neuroimaging studies that have begun to explore the mechanisms that mediate reflective attentional orienting in vision and more recently, in audition. Reflective attention refers to situations in which attention is oriented toward internal representations rather than focused on external stimuli. We propose four general principles underlying attention to short-term memory. Furthermore, we suggest that mechanisms involved in orienting attention to visual object representations may also apply for orienting attention to sound object representations.

20 CFR 632.38 - Classification of costs.

Code of Federal Regulations, 2010 CFR

2010-04-01

...-job training, salaries, fringe benefits, equipment and supplies of personnel engaged in providing...; classroom space and utility costs; employability assessment; job related counseling for participants; job search assistance and labor market orientation; participant allowances, and tuition and entrance fees...

Object Classification in Semi Structured Enviroment Using Forward-Looking Sonar

PubMed Central

dos Santos, Matheus; Ribeiro, Pedro Otávio; Núñez, Pedro; Botelho, Silvia

2017-01-01

The submarine exploration using robots has been increasing in recent years. The automation of tasks such as monitoring, inspection, and underwater maintenance requires the understanding of the robot’s environment. The object recognition in the scene is becoming a critical issue for these systems. On this work, an underwater object classification pipeline applied in acoustic images acquired by Forward-Looking Sonar (FLS) are studied. The object segmentation combines thresholding, connected pixels searching and peak of intensity analyzing techniques. The object descriptor extract intensity and geometric features of the detected objects. A comparison between the Support Vector Machine, K-Nearest Neighbors, and Random Trees classifiers are presented. An open-source tool was developed to annotate and classify the objects and evaluate their classification performance. The proposed method efficiently segments and classifies the structures in the scene using a real dataset acquired by an underwater vehicle in a harbor area. Experimental results demonstrate the robustness and accuracy of the method described in this paper. PMID:28961163

Determining urban land uses through building-associated element attributes derived from lidar and aerial photographs

NASA Astrophysics Data System (ADS)

Meng, Xuelian

Urban land-use research is a key component in analyzing the interactions between human activities and environmental change. Researchers have conducted many experiments to classify urban or built-up land, forest, water, agriculture, and other land-use and land-cover types. Separating residential land uses from other land uses within urban areas, however, has proven to be surprisingly troublesome. Although high-resolution images have recently become more available for land-use classification, an increase in spatial resolution does not guarantee improved classification accuracy by traditional classifiers due to the increase of class complexity. This research presents an approach to detect and separate residential land uses on a building scale directly from remotely sensed imagery to enhance urban land-use analysis. Specifically, the proposed methodology applies a multi-directional ground filter to generate a bare ground surface from lidar data, then utilizes a morphology-based building detection algorithm to identify buildings from lidar and aerial photographs, and finally separates residential buildings using a supervised C4.5 decision tree analysis based on the seven selected building land-use indicators. Successful execution of this study produces three independent methods, each corresponding to the steps of the methodology: lidar ground filtering, building detection, and building-based object-oriented land-use classification. Furthermore, this research provides a prototype as one of the few early explorations of building-based land-use analysis and successful separation of more than 85% of residential buildings based on an experiment on an 8.25-km2 study site located in Austin, Texas.

Measuring the involvement of people with HIV in treatment decision making using the control preferences scale.

PubMed

Kremer, Heidemarie; Ironson, Gail

2008-01-01

Since 1983, HIV patients have been advocating for participatory decision making. This study measures the involvement that HIV-positive people perceive in treatment decision making. A secondary objective is to assess the validity of the instrument used to examine decisional roles, the Control Preferences Scale (CPS). The authors interviewed 79 HIV-positive people, a sub-sample of a study on long survival with HIV, diverse with respect to ethnicity, age, gender, and sexual orientation. They compared the self- and researcher-rated decisional roles of participants on the CPS. They also assessed how well the CPS corresponds with Charles's paternalistic, shared- and informed-choice models of decision making about treatment based on decisional roles and information exchange. Most participants (75%) perceived collaborative/active involvement in decision making. Agreement (Kendall's tau-b) between self- and researcher-rated decisional roles on the CPS was 0.82, whereas agreement between self-ratings on the CPS and researcher ratings on Charles's classification was 0.60. Charles's classification was difficult if participants had chosen not to take their prescribed medication without being adequately informed about the risky consequences of this decision. In this study, HIV-positive people perceived a high level of involvement in decision making. Reliability and convergent validity of the CPS was high. Charles's classification was problematic because decisional roles and information exchange are distinct dimensions. Some people make risky treatment decisions on their own without being adequately informed. The CPS is a useful instrument to measure decisional role perceptions of HIV-positive people but needs to be complemented by an instrument measuring treatment knowledge.

Object Oriented Modeling and Design

NASA Technical Reports Server (NTRS)

Shaykhian, Gholam Ali

2007-01-01

The Object Oriented Modeling and Design seminar is intended for software professionals and students, it covers the concepts and a language-independent graphical notation that can be used to analyze problem requirements, and design a solution to the problem. The seminar discusses the three kinds of object-oriented models class, state, and interaction. The class model represents the static structure of a system, the state model describes the aspects of a system that change over time as well as control behavior and the interaction model describes how objects collaborate to achieve overall results. Existing knowledge of object oriented programming may benefit the learning of modeling and good design. Specific expectations are: Create a class model, Read, recognize, and describe a class model, Describe association and link, Show abstract classes used with multiple inheritance, Explain metadata, reification and constraints, Group classes into a package, Read, recognize, and describe a state model, Explain states and transitions, Read, recognize, and describe interaction model, Explain Use cases and use case relationships, Show concurrency in activity diagram, Object interactions in sequence diagram.

Integrated Approach To Design And Analysis Of Systems

NASA Technical Reports Server (NTRS)

Patterson-Hine, F. A.; Iverson, David L.

1993-01-01

Object-oriented fault-tree representation unifies evaluation of reliability and diagnosis of faults. Programming/fault tree described more fully in "Object-Oriented Algorithm For Evaluation Of Fault Trees" (ARC-12731). Augmented fault tree object contains more information than fault tree object used in quantitative analysis of reliability. Additional information needed to diagnose faults in system represented by fault tree.

Advanced EMI Models and Classification Algorithms: The Next Level of Sophistication to Improve Discrimination of Challenging Targets

DTIC Science & Technology

2017-01-01

Inverted effective ONVMS for an M30 Bomb in a test-stand scenario. The target is oriented 45 degrees at a depth of 150 cm depth (top) and oriented...vertically at a depth of 210 cm (bottom). The red lines are the total ONVMS for a library AN M30 Bomb , and the other lines correspond to the...Centimeter DE Differential Evolution DLL Dynamic Link Libraries DoD Department of Defense EM Electromagnetic EMA Expectation

Intact perception but abnormal orientation towards face-like objects in young children with ASD

PubMed Central

Guillon, Quentin; Rogé, Bernadette; Afzali, Mohammad H.; Baduel, Sophie; Kruck, Jeanne; Hadjikhani, Nouchine

2016-01-01

There is ample behavioral evidence of diminished orientation towards faces as well as the presence of face perception impairments in autism spectrum disorder (ASD), but the underlying mechanisms of these deficits are still unclear. We used face-like object stimuli that have been shown to evoke pareidolia in typically developing (TD) individuals to test the effect of a global face-like configuration on orientation and perceptual processes in young children with ASD and age-matched TD controls. We show that TD children were more likely to look first towards upright face-like objects than children with ASD, showing that a global face-like configuration elicit a stronger orientation bias in TD children as compared to children with ASD. However, once they were looking at the stimuli, both groups spent more time exploring the upright face-like object, suggesting that they both perceived it as a face. Our results are in agreement with abnormal social orienting in ASD, possibly due to an abnormal tuning of the subcortical pathway, leading to poor orienting and attention towards faces. Our results also indicate that young children with ASD can perceive a generic face holistically, such as face-like objects, further demonstrating holistic processing of faces in ASD. PMID:26912096

Intact perception but abnormal orientation towards face-like objects in young children with ASD.

PubMed

Guillon, Quentin; Rogé, Bernadette; Afzali, Mohammad H; Baduel, Sophie; Kruck, Jeanne; Hadjikhani, Nouchine

2016-02-25

There is ample behavioral evidence of diminished orientation towards faces as well as the presence of face perception impairments in autism spectrum disorder (ASD), but the underlying mechanisms of these deficits are still unclear. We used face-like object stimuli that have been shown to evoke pareidolia in typically developing (TD) individuals to test the effect of a global face-like configuration on orientation and perceptual processes in young children with ASD and age-matched TD controls. We show that TD children were more likely to look first towards upright face-like objects than children with ASD, showing that a global face-like configuration elicit a stronger orientation bias in TD children as compared to children with ASD. However, once they were looking at the stimuli, both groups spent more time exploring the upright face-like object, suggesting that they both perceived it as a face. Our results are in agreement with abnormal social orienting in ASD, possibly due to an abnormal tuning of the subcortical pathway, leading to poor orienting and attention towards faces. Our results also indicate that young children with ASD can perceive a generic face holistically, such as face-like objects, further demonstrating holistic processing of faces in ASD.

Object-oriented structures supporting remote sensing databases

NASA Technical Reports Server (NTRS)

Wichmann, Keith; Cromp, Robert F.

1995-01-01

Object-oriented databases show promise for modeling the complex interrelationships pervasive in scientific domains. To examine the utility of this approach, we have developed an Intelligent Information Fusion System based on this technology, and applied it to the problem of managing an active repository of remotely-sensed satellite scenes. The design and implementation of the system is compared and contrasted with conventional relational database techniques, followed by a presentation of the underlying object-oriented data structures used to enable fast indexing into the data holdings.

Scrutinizing UML Activity Diagrams

NASA Astrophysics Data System (ADS)

Al-Fedaghi, Sabah

Building an information system involves two processes: conceptual modeling of the “real world domain” and designing the software system. Object-oriented methods and languages (e.g., UML) are typically used for describing the software system. For the system analysis process that produces the conceptual description, object-oriented techniques or semantics extensions are utilized. Specifically, UML activity diagrams are the “flow charts” of object-oriented conceptualization tools. This chapter proposes an alternative to UML activity diagrams through the development of a conceptual modeling methodology based on the notion of flow.

Large project experiences with object-oriented methods and reuse

NASA Technical Reports Server (NTRS)

Wessale, William; Reifer, Donald J.; Weller, David

1992-01-01

The SSVTF (Space Station Verification and Training Facility) project is completing the Preliminary Design Review of a large software development using object-oriented methods and systematic reuse. An incremental developmental lifecycle was tailored to provide early feedback and guidance on methods and products, with repeated attention to reuse. Object oriented methods were formally taught and supported by realistic examples. Reuse was readily accepted and planned by the developers. Schedule and budget issues were handled by agreements and work sharing arranged by the developers.

Problems and Expectations of University Students Attending Higher Education in Turkey: Orientation Services

ERIC Educational Resources Information Center

Kutlu, Mustafa

2005-01-01

The objective of this research is to find out the problems and expectations of the students in Inonu University (in Malatya, a city in east Turkey) concerning the orientation services. An additional objective is to ascertain whether students' expectations with regard to orientation services differ according to their sex, their place of origin, and…

A Programmer-Oriented Approach to Safe Concurrency

DTIC Science & Technology

2003-05-01

and leaving a synchronized block additionally has effects on the management of memory values in the JMM. The practical outcome of these effects is...object-oriented effects system; (3) analysis to track the association of locks with regions, (4) policy descriptions for allowable method...Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4 An Object-Oriented Effects System 45 4.1 Regions Identify State

Classifying Measures of Biological Variation

PubMed Central

Gregorius, Hans-Rolf; Gillet, Elizabeth M.

2015-01-01

Biological variation is commonly measured at two basic levels: variation within individual communities, and the distribution of variation over communities or within a metacommunity. We develop a classification for the measurement of biological variation on both levels: Within communities into the categories of dispersion and diversity, and within metacommunities into the categories of compositional differentiation and partitioning of variation. There are essentially two approaches to characterizing the distribution of trait variation over communities in that individuals with the same trait state or type tend to occur in the same community (describes differentiation tendencies), and individuals with different types tend to occur in different communities (describes apportionment tendencies). Both approaches can be viewed from the dual perspectives of trait variation distributed over communities (CT perspective) and community membership distributed over trait states (TC perspective). This classification covers most of the relevant descriptors (qualified measures) of biological variation, as is demonstrated with the help of major families of descriptors. Moreover, the classification is shown to open ways to develop new descriptors that meet current needs. Yet the classification also reveals the misclassification of some prominent and widely applied descriptors: Dispersion is often misclassified as diversity, particularly in cases where dispersion descriptor allow for the computation of effective numbers; the descriptor GST of population genetics is commonly misclassified as compositional differentiation and confused with partitioning-oriented differentiation, whereas it actually measures partitioning-oriented apportionment; descriptors of β-diversity are ambiguous about the differentiation effects they are supposed to represent and therefore require conceptual reconsideration. PMID:25807558

Energy efficiency analysis of the manipulation process by the industrial objects with the use of Bernoulli gripping devices

NASA Astrophysics Data System (ADS)

Savkiv, Volodymyr; Mykhailyshyn, Roman; Duchon, Frantisek; Mikhalishin, Mykhailo

2017-11-01

The article deals with the topical issue of reducing energy consumption for transportation of industrial objects. The energy efficiency of the process of objects manipulation with the use of the orientation optimization method while gripping with the help of different methods has been studied. The analysis of the influence of the constituent parts of inertial forces, that affect the object of manipulation, on the necessary force characteristics and energy consumption of Bernoulli gripping device has been proposed. The economic efficiency of the use of the optimal orientation of Bernoulli gripping device while transporting the object of manipulation in comparison to the transportation without re-orientation has been proved.

Representing metabolic pathway information: an object-oriented approach.

PubMed

Ellis, L B; Speedie, S M; McLeish, R

1998-01-01

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD) is a website providing information and dynamic links for microbial metabolic pathways, enzyme reactions, and their substrates and products. The Compound, Organism, Reaction and Enzyme (CORE) object-oriented database management system was developed to contain and serve this information. CORE was developed using Java, an object-oriented programming language, and PSE persistent object classes from Object Design, Inc. CORE dynamically generates descriptive web pages for reactions, compounds and enzymes, and reconstructs ad hoc pathway maps starting from any UM-BBD reaction. CORE code is available from the authors upon request. CORE is accessible through the UM-BBD at: http://www. labmed.umn.edu/umbbd/index.html.

Humanoid Robotics: Real-Time Object Oriented Programming

NASA Technical Reports Server (NTRS)

Newton, Jason E.

2005-01-01

Programming of robots in today's world is often done in a procedural oriented fashion, where object oriented programming is not incorporated. In order to keep a robust architecture allowing for easy expansion of capabilities and a truly modular design, object oriented programming is required. However, concepts in object oriented programming are not typically applied to a real time environment. The Fujitsu HOAP-2 is the test bed for the development of a humanoid robot framework abstracting control of the robot into simple logical commands in a real time robotic system while allowing full access to all sensory data. In addition to interfacing between the motor and sensory systems, this paper discusses the software which operates multiple independently developed control systems simultaneously and the safety measures which keep the humanoid from damaging itself and its environment while running these systems. The use of this software decreases development time and costs and allows changes to be made while keeping results safe and predictable.

Fundamental movement skills testing in children with cerebral palsy.

PubMed

Capio, Catherine M; Sit, Cindy H P; Abernethy, Bruce

2011-01-01

To examine the inter-rater reliability and comparative validity of product-oriented and process-oriented measures of fundamental movement skills among children with cerebral palsy (CP). In total, 30 children with CP aged 6 to 14 years (Mean = 9.83, SD = 2.5) and classified in Gross Motor Function Classification System (GMFCS) levels I-III performed tasks of catching, throwing, kicking, horizontal jumping and running. Process-oriented assessment was undertaken using a number of components of the Test of Gross Motor Development (TGMD-2), while product-oriented assessment included measures of time taken, distance covered and number of successful task completions. Cohen's kappa, Spearman's rank correlation coefficient and tests to compare correlated correlation coefficients were performed. Very good inter-rater reliability was found. Process-oriented measures for running and jumping had significant associations with GMFCS, as did seven product-oriented measures for catching, throwing, kicking, running and jumping. Product-oriented measures of catching, kicking and running had stronger associations with GMFCS than the corresponding process-oriented measures. Findings support the validity of process-oriented measures for running and jumping and of product-oriented measures of catching, throwing, kicking, running and jumping. However, product-oriented measures for catching, kicking and running appear to have stronger associations with functional abilities of children with CP, and are thus recommended for use in rehabilitation processes.

Methodology for object-oriented real-time systems analysis and design: Software engineering

NASA Technical Reports Server (NTRS)

Schoeffler, James D.

1991-01-01

Successful application of software engineering methodologies requires an integrated analysis and design life-cycle in which the various phases flow smoothly 'seamlessly' from analysis through design to implementation. Furthermore, different analysis methodologies often lead to different structuring of the system so that the transition from analysis to design may be awkward depending on the design methodology to be used. This is especially important when object-oriented programming is to be used for implementation when the original specification and perhaps high-level design is non-object oriented. Two approaches to real-time systems analysis which can lead to an object-oriented design are contrasted: (1) modeling the system using structured analysis with real-time extensions which emphasizes data and control flows followed by the abstraction of objects where the operations or methods of the objects correspond to processes in the data flow diagrams and then design in terms of these objects; and (2) modeling the system from the beginning as a set of naturally occurring concurrent entities (objects) each having its own time-behavior defined by a set of states and state-transition rules and seamlessly transforming the analysis models into high-level design models. A new concept of a 'real-time systems-analysis object' is introduced and becomes the basic building block of a series of seamlessly-connected models which progress from the object-oriented real-time systems analysis and design system analysis logical models through the physical architectural models and the high-level design stages. The methodology is appropriate to the overall specification including hardware and software modules. In software modules, the systems analysis objects are transformed into software objects.

Classification and pose estimation of objects using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

Space Object Classification Using Fused Features of Time Series Data

NASA Astrophysics Data System (ADS)

Jia, B.; Pham, K. D.; Blasch, E.; Shen, D.; Wang, Z.; Chen, G.

In this paper, a fused feature vector consisting of raw time series and texture feature information is proposed for space object classification. The time series data includes historical orbit trajectories and asteroid light curves. The texture feature is derived from recurrence plots using Gabor filters for both unsupervised learning and supervised learning algorithms. The simulation results show that the classification algorithms using the fused feature vector achieve better performance than those using raw time series or texture features only.

Cognitive Education with Deaf Adolescents: Effects of Instrumental Enrichment.

ERIC Educational Resources Information Center

Haywood, H. Carl; And Others

1988-01-01

Twenty-six deaf adolescents received instruction in a structured program of cognitive education called "Instrumental Enrichment." The program addresses, among other processes, comparison, classification, logical progression, spatial orientation, analysis and synthesis, and syllogistic thinking. Following training, the subjects showed…

75 FR 420 - Receipt of Domestic Interested Party Petition Concerning the Tariff Classification of Wickless...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-05

...] Receipt of Domestic Interested Party Petition Concerning the Tariff Classification of Wickless Wax Objects... subheading 9602.00.40, HTSUS. The petitioner contends that the proper classification for these wickless wax... classification as a means of circumventing a dumping order that has been placed on petroleum wax candles from...

Classification of Dual-Wavelength Airborne Laser Scanning Point Cloud Based on the Radiometric Properties of the Objects

NASA Astrophysics Data System (ADS)

Pilarska, M.

2018-05-01

Airborne laser scanning (ALS) is a well-known and willingly used technology. One of the advantages of this technology is primarily its fast and accurate data registration. In recent years ALS is continuously developed. One of the latest achievements is multispectral ALS, which consists in obtaining simultaneously the data in more than one laser wavelength. In this article the results of the dual-wavelength ALS data classification are presented. The data were acquired with RIEGL VQ-1560i sensor, which is equipped with two laser scanners operating in different wavelengths: 532 nm and 1064 nm. Two classification approaches are presented in the article: classification, which is based on geometric relationships between points and classification, which mostly relies on the radiometric properties of registered objects. The overall accuracy of the geometric classification was 86 %, whereas for the radiometric classification it was 81 %. As a result, it can be assumed that the radiometric features which are provided by the multispectral ALS have potential to be successfully used in ALS point cloud classification.

Learning viewpoint invariant object representations using a temporal coherence principle.

PubMed

Einhäuser, Wolfgang; Hipp, Jörg; Eggert, Julian; Körner, Edgar; König, Peter

2005-07-01

Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by the discovery that general coding principles could explain many aspects of neuronal response properties. How can such schemes be transferred to system level performance? In the present study we train cells on a particular variant of the general principle of temporal coherence, the "stability" objective. These cells are trained on unlabeled real-world images without a teaching signal. We show that after training, the cells form a representation that is largely independent of the viewpoint from which the stimulus is looked at. This finding includes generalization to previously unseen viewpoints. The achieved representation is better suited for view-point invariant object classification than the cells' input patterns. This property to facilitate view-point invariant classification is maintained even if training and classification take place in the presence of an--also unlabeled--distractor object. In summary, here we show that unsupervised learning using a general coding principle facilitates the classification of real-world objects, that are not segmented from the background and undergo complex, non-isomorphic, transformations.

The Use of Object-Oriented Analysis Methods in Surety Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Craft, Richard L.; Funkhouser, Donald R.; Wyss, Gregory D.

1999-05-01

Object-oriented analysis methods have been used in the computer science arena for a number of years to model the behavior of computer-based systems. This report documents how such methods can be applied to surety analysis. By embodying the causality and behavior of a system in a common object-oriented analysis model, surety analysts can make the assumptions that underlie their models explicit and thus better communicate with system designers. Furthermore, given minor extensions to traditional object-oriented analysis methods, it is possible to automatically derive a wide variety of traditional risk and reliability analysis methods from a single common object model. Automaticmore » model extraction helps ensure consistency among analyses and enables the surety analyst to examine a system from a wider variety of viewpoints in a shorter period of time. Thus it provides a deeper understanding of a system's behaviors and surety requirements. This report documents the underlying philosophy behind the common object model representation, the methods by which such common object models can be constructed, and the rules required to interrogate the common object model for derivation of traditional risk and reliability analysis models. The methodology is demonstrated in an extensive example problem.« less

Vulnerable land ecosystems classification using spatial context and spectral indices

NASA Astrophysics Data System (ADS)

Ibarrola-Ulzurrun, Edurne; Gonzalo-Martín, Consuelo; Marcello, Javier

2017-10-01

Natural habitats are exposed to growing pressure due to intensification of land use and tourism development. Thus, obtaining information on the vegetation is necessary for conservation and management projects. In this context, remote sensing is an important tool for monitoring and managing habitats, being classification a crucial stage. The majority of image classifications techniques are based upon the pixel-based approach. An alternative is the object-based (OBIA) approach, in which a previous segmentation step merges image pixels to create objects that are then classified. Besides, improved results may be gained by incorporating additional spatial information and specific spectral indices into the classification process. The main goal of this work was to implement and assess object-based classification techniques on very-high resolution imagery incorporating spectral indices and contextual spatial information in the classification models. The study area was Teide National Park in Canary Islands (Spain) using Worldview-2 orthoready imagery. In the classification model, two common indices were selected Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI), as well as two specific Worldview-2 sensor indices, Worldview Vegetation Index and Worldview Soil Index. To include the contextual information, Grey Level Co-occurrence Matrices (GLCM) were used. The classification was performed training a Support Vector Machine with sufficient and representative number of vegetation samples (Spartocytisus supranubius, Pterocephalus lasiospermus, Descurainia bourgaeana and Pinus canariensis) as well as urban, road and bare soil classes. Confusion Matrices were computed to evaluate the results from each classification model obtaining the highest overall accuracy (90.07%) combining both Worldview indices with the GLCM-dissimilarity.

Combining High Spatial Resolution Optical and LIDAR Data for Object-Based Image Classification

NASA Astrophysics Data System (ADS)

Li, R.; Zhang, T.; Geng, R.; Wang, L.

2018-04-01

In order to classify high spatial resolution images more accurately, in this research, a hierarchical rule-based object-based classification framework was developed based on a high-resolution image with airborne Light Detection and Ranging (LiDAR) data. The eCognition software is employed to conduct the whole process. In detail, firstly, the FBSP optimizer (Fuzzy-based Segmentation Parameter) is used to obtain the optimal scale parameters for different land cover types. Then, using the segmented regions as basic units, the classification rules for various land cover types are established according to the spectral, morphological and texture features extracted from the optical images, and the height feature from LiDAR respectively. Thirdly, the object classification results are evaluated by using the confusion matrix, overall accuracy and Kappa coefficients. As a result, a method using the combination of an aerial image and the airborne Lidar data shows higher accuracy.

Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification

NASA Astrophysics Data System (ADS)

Liu, Tao; Abd-Elrahman, Amr

2018-05-01

Deep convolutional neural network (DCNN) requires massive training datasets to trigger its image classification power, while collecting training samples for remote sensing application is usually an expensive process. When DCNN is simply implemented with traditional object-based image analysis (OBIA) for classification of Unmanned Aerial systems (UAS) orthoimage, its power may be undermined if the number training samples is relatively small. This research aims to develop a novel OBIA classification approach that can take advantage of DCNN by enriching the training dataset automatically using multi-view data. Specifically, this study introduces a Multi-View Object-based classification using Deep convolutional neural network (MODe) method to process UAS images for land cover classification. MODe conducts the classification on multi-view UAS images instead of directly on the orthoimage, and gets the final results via a voting procedure. 10-fold cross validation results show the mean overall classification accuracy increasing substantially from 65.32%, when DCNN was applied on the orthoimage to 82.08% achieved when MODe was implemented. This study also compared the performances of the support vector machine (SVM) and random forest (RF) classifiers with DCNN under traditional OBIA and the proposed multi-view OBIA frameworks. The results indicate that the advantage of DCNN over traditional classifiers in terms of accuracy is more obvious when these classifiers were applied with the proposed multi-view OBIA framework than when these classifiers were applied within the traditional OBIA framework.

Performance Evaluation of Frequency Transform Based Block Classification of Compound Image Segmentation Techniques

NASA Astrophysics Data System (ADS)

Selwyn, Ebenezer Juliet; Florinabel, D. Jemi

2018-04-01

Compound image segmentation plays a vital role in the compression of computer screen images. Computer screen images are images which are mixed with textual, graphical, or pictorial contents. In this paper, we present a comparison of two transform based block classification of compound images based on metrics like speed of classification, precision and recall rate. Block based classification approaches normally divide the compound images into fixed size blocks of non-overlapping in nature. Then frequency transform like Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are applied over each block. Mean and standard deviation are computed for each 8 × 8 block and are used as features set to classify the compound images into text/graphics and picture/background block. The classification accuracy of block classification based segmentation techniques are measured by evaluation metrics like precision and recall rate. Compound images of smooth background and complex background images containing text of varying size, colour and orientation are considered for testing. Experimental evidence shows that the DWT based segmentation provides significant improvement in recall rate and precision rate approximately 2.3% than DCT based segmentation with an increase in block classification time for both smooth and complex background images.

Technical support for creating an artificial intelligence system for feature extraction and experimental design

NASA Technical Reports Server (NTRS)

Glick, B. J.

1985-01-01

Techniques for classifying objects into groups or clases go under many different names including, most commonly, cluster analysis. Mathematically, the general problem is to find a best mapping of objects into an index set consisting of class identifiers. When an a priori grouping of objects exists, the process of deriving the classification rules from samples of classified objects is known as discrimination. When such rules are applied to objects of unknown class, the process is denoted classification. The specific problem addressed involves the group classification of a set of objects that are each associated with a series of measurements (ratio, interval, ordinal, or nominal levels of measurement). Each measurement produces one variable in a multidimensional variable space. Cluster analysis techniques are reviewed and methods for incuding geographic location, distance measures, and spatial pattern (distribution) as parameters in clustering are examined. For the case of patterning, measures of spatial autocorrelation are discussed in terms of the kind of data (nominal, ordinal, or interval scaled) to which they may be applied.

Applying machine learning classification techniques to automate sky object cataloguing

NASA Astrophysics Data System (ADS)

Fayyad, Usama M.; Doyle, Richard J.; Weir, W. Nick; Djorgovski, Stanislav

1993-08-01

We describe the application of an Artificial Intelligence machine learning techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Mt. Palomar Northern Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 107 galaxies and 108 stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. Unfortunately, the size of this data set precludes analysis in an exclusively manual fashion. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3 (Generalized ID3) and O-B Tree, two inductive learning techniques, learns classification decision trees from examples. These classifiers will then be applied to new data. These developmnent process is highly interactive, with astronomer input playing a vital role. Astronomers refine the feature set used to construct sky object descriptions, and evaluate the performance of the automated classification technique on new data. This paper gives an overview of the machine learning techniques with an emphasis on their general applicability, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our machine learning approach is well-suited to the problem. The primary benefit of the approach is increased data reduction throughput. Another benefit is consistency of classification. The classification rules which are the product of the inductive learning techniques will form an objective, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically catalogued data.

Possible functions of contextual modulations and receptive field nonlinearities: pop-out and texture segmentation

PubMed Central

Schmid, Anita M.; Victor, Jonathan D.

2014-01-01

When analyzing a visual image, the brain has to achieve several goals quickly. One crucial goal is to rapidly detect parts of the visual scene that might be behaviorally relevant, while another one is to segment the image into objects, to enable an internal representation of the world. Both of these processes can be driven by local variations in any of several image attributes such as luminance, color, and texture. Here, focusing on texture defined by local orientation, we propose that the two processes are mediated by separate mechanisms that function in parallel. More specifically, differences in orientation can cause an object to “pop out” and attract visual attention, if its orientation differs from that of the surrounding objects. Differences in orientation can also signal a boundary between objects and therefore provide useful information for image segmentation. We propose that contextual response modulations in primary visual cortex (V1) are responsible for orientation pop-out, while a different kind of receptive field nonlinearity in secondary visual cortex (V2) is responsible for orientation-based texture segmentation. We review a recent experiment that led us to put forward this hypothesis along with other research literature relevant to this notion. PMID:25064441

Interactions between visual working memory representations.

PubMed

Bae, Gi-Yeul; Luck, Steven J

2017-11-01

We investigated whether the representations of different objects are maintained independently in working memory or interact with each other. Observers were shown two sequentially presented orientations and required to reproduce each orientation after a delay. The sequential presentation minimized perceptual interactions so that we could isolate interactions between memory representations per se. We found that similar orientations were repelled from each other whereas dissimilar orientations were attracted to each other. In addition, when one of the items was given greater attentional priority by means of a cue, the representation of the high-priority item was not influenced very much by the orientation of the low-priority item, but the representation of the low-priority item was strongly influenced by the orientation of the high-priority item. This indicates that attention modulates the interactions between working memory representations. In addition, errors in the reported orientations of the two objects were positively correlated under some conditions, suggesting that representations of distinct objects may become grouped together in memory. Together, these results demonstrate that working-memory representations are not independent but instead interact with each other in a manner that depends on attentional priority.

Object-Based Classification and Change Detection of Hokkaido, Japan

NASA Astrophysics Data System (ADS)

Park, J. G.; Harada, I.; Kwak, Y.

2016-06-01

Topography and geology are factors to characterize the distribution of natural vegetation. Topographic contour is particularly influential on the living conditions of plants such as soil moisture, sunlight, and windiness. Vegetation associations having similar characteristics are present in locations having similar topographic conditions unless natural disturbances such as landslides and forest fires or artificial disturbances such as deforestation and man-made plantation bring about changes in such conditions. We developed a vegetation map of Japan using an object-based segmentation approach with topographic information (elevation, slope, slope direction) that is closely related to the distribution of vegetation. The results found that the object-based classification is more effective to produce a vegetation map than the pixel-based classification.

Identification of canonical neural events during continuous gameplay of an 8-bit style video game.

PubMed

Cavanagh, James F; Castellanos, Joel

2016-06-01

Cognitive neuroscience suffers from a unique and pervasive problem of generalizability. Since neural findings are often interpreted in the context of a specific manipulation during a carefully controlled task, it is hard to transfer knowledge from one task to another. In this report we address problems of generalizability with two methodological advancements. First, we aimed to transcend status quo experimental procedures with a continuous, engaging task environment. To this end, we created a novel 8-bit style continuous space shooter video game that elicits a multitude of goal-oriented events, such as crashing into a wall or blowing up an enemy with a missile. Second, we aimed to objectively define the psychological significance of these events. To achieve this aim, we used pattern classification of EEG data to derive predictive weights from carefully controlled pre-game exemplar events (oddball target detection and gambling wins and losses) and transferred those weights to EEG activities during video game events. All major goal-oriented events (crashes into the wall, crashes into an enemy, missile hit on an enemy) had a significant between-task transfer bias towards oddball target weights in the time range of the canonical P3, indicating the presence of similar salience detection processes. Missile hits on an enemy were specifically identified as gambling wins, confirming the hypothesis that this goal-oriented event was appetitive. These findings suggest that it is possible to identify the contribution of canonical neural activities during otherwise ambiguous and uncontrolled task performance. Copyright © 2016 Elsevier Inc. All rights reserved.

Machine learning in infrared object classification - an all-sky selection of YSO candidates

NASA Astrophysics Data System (ADS)

Marton, Gabor; Zahorecz, Sarolta; Toth, L. Viktor; Magnus McGehee, Peregrine; Kun, Maria

2015-08-01

Object classification is a fundamental and challenging problem in the era of big data. I will discuss up-to-date methods and their application to classify infrared point sources.We analysed the ALLWISE catalogue, the most recent public source catalogue of the Wide-field Infrared Survey Explorer (WISE) to compile a reliable list of Young Stellar Object (YSO) candidates. We tested and compared classical and up-to-date statistical methods as well, to discriminate source types like extragalactic objects, evolved stars, main sequence stars, objects related to the interstellar medium and YSO candidates by using their mid-IR WISE properties and associated near-IR 2MASS data.In the particular classification problem the Support Vector Machines (SVM), a class of supervised learning algorithm turned out to be the best tool. As a result we classify Class I and II YSOs with >90% accuracy while the fraction of contaminating extragalactic objects remains well below 1%, based on the number of known objects listed in the SIMBAD and VizieR databases. We compare our results to other classification schemes from the literature and show that the SVM outperforms methods that apply linear cuts on the colour-colour and colour-magnitude space. Our homogenous YSO candidate catalog can serve as an excellent pathfinder for future detailed observations of individual objects and a starting point of statistical studies that aim to add pieces to the big picture of star formation theory.

Serial dependence promotes object stability during occlusion

PubMed Central

Liberman, Alina; Zhang, Kathy; Whitney, David

2016-01-01

Object identities somehow appear stable and continuous over time despite eye movements, disruptions in visibility, and constantly changing visual input. Recent results have demonstrated that the perception of orientation, numerosity, and facial identity is systematically biased (i.e., pulled) toward visual input from the recent past. The spatial region over which current orientations or face identities are pulled by previous orientations or identities, respectively, is known as the continuity field, which is temporally tuned over the past several seconds (Fischer & Whitney, 2014). This perceptual pull could contribute to the visual stability of objects over short time periods, but does it also address how perceptual stability occurs during visual discontinuities? Here, we tested whether the continuity field helps maintain perceived object identity during occlusion. Specifically, we found that the perception of an oriented Gabor that emerged from behind an occluder was significantly pulled toward the random (and unrelated) orientation of the Gabor that was seen entering the occluder. Importantly, this serial dependence was stronger for predictable, continuously moving trajectories, compared to unpredictable ones or static displacements. This result suggests that our visual system takes advantage of expectations about a stable world, helping to maintain perceived object continuity despite interrupted visibility. PMID:28006066

A comparison of autonomous techniques for multispectral image analysis and classification

NASA Astrophysics Data System (ADS)

Valdiviezo-N., Juan C.; Urcid, Gonzalo; Toxqui-Quitl, Carina; Padilla-Vivanco, Alfonso

2012-10-01

Multispectral imaging has given place to important applications related to classification and identification of objects from a scene. Because of multispectral instruments can be used to estimate the reflectance of materials in the scene, these techniques constitute fundamental tools for materials analysis and quality control. During the last years, a variety of algorithms has been developed to work with multispectral data, whose main purpose has been to perform the correct classification of the objects in the scene. The present study introduces a brief review of some classical as well as a novel technique that have been used for such purposes. The use of principal component analysis and K-means clustering techniques as important classification algorithms is here discussed. Moreover, a recent method based on the min-W and max-M lattice auto-associative memories, that was proposed for endmember determination in hyperspectral imagery, is introduced as a classification method. Besides a discussion of their mathematical foundation, we emphasize their main characteristics and the results achieved for two exemplar images conformed by objects similar in appearance, but spectrally different. The classification results state that the first components computed from principal component analysis can be used to highlight areas with different spectral characteristics. In addition, the use of lattice auto-associative memories provides good results for materials classification even in the cases where some spectral similarities appears in their spectral responses.

Hip2Norm: an object-oriented cross-platform program for 3D analysis of hip joint morphology using 2D pelvic radiographs.

PubMed

Zheng, G; Tannast, M; Anderegg, C; Siebenrock, K A; Langlotz, F

2007-07-01

We developed an object-oriented cross-platform program to perform three-dimensional (3D) analysis of hip joint morphology using two-dimensional (2D) anteroposterior (AP) pelvic radiographs. Landmarks extracted from 2D AP pelvic radiographs and optionally an additional lateral pelvic X-ray were combined with a cone beam projection model to reconstruct 3D hip joints. Since individual pelvic orientation can vary considerably, a method for standardizing pelvic orientation was implemented to determine the absolute tilt/rotation. The evaluation of anatomically morphologic differences was achieved by reconstructing the projected acetabular rim and the measured hip parameters as if obtained in a standardized neutral orientation. The program had been successfully used to interactively objectify acetabular version in hips with femoro-acetabular impingement or developmental dysplasia. Hip(2)Norm is written in object-oriented programming language C++ using cross-platform software Qt (TrollTech, Oslo, Norway) for graphical user interface (GUI) and is transportable to any platform.

Bayesian integration of position and orientation cues in perception of biological and non-biological forms.

PubMed

Thurman, Steven M; Lu, Hongjing

2014-01-01

Visual form analysis is fundamental to shape perception and likely plays a central role in perception of more complex dynamic shapes, such as moving objects or biological motion. Two primary form-based cues serve to represent the overall shape of an object: the spatial position and the orientation of locations along the boundary of the object. However, it is unclear how the visual system integrates these two sources of information in dynamic form analysis, and in particular how the brain resolves ambiguities due to sensory uncertainty and/or cue conflict. In the current study, we created animations of sparsely-sampled dynamic objects (human walkers or rotating squares) comprised of oriented Gabor patches in which orientation could either coincide or conflict with information provided by position cues. When the cues were incongruent, we found a characteristic trade-off between position and orientation information whereby position cues increasingly dominated perception as the relative uncertainty of orientation increased and vice versa. Furthermore, we found no evidence for differences in the visual processing of biological and non-biological objects, casting doubt on the claim that biological motion may be specialized in the human brain, at least in specific terms of form analysis. To explain these behavioral results quantitatively, we adopt a probabilistic template-matching model that uses Bayesian inference within local modules to estimate object shape separately from either spatial position or orientation signals. The outputs of the two modules are integrated with weights that reflect individual estimates of subjective cue reliability, and integrated over time to produce a decision about the perceived dynamics of the input data. Results of this model provided a close fit to the behavioral data, suggesting a mechanism in the human visual system that approximates rational Bayesian inference to integrate position and orientation signals in dynamic form analysis.

32 CFR 1636.2 - The claim of conscientious objection.

Code of Federal Regulations, 2010 CFR

2010-07-01

... CLASSIFICATION OF CONSCIENTIOUS OBJECTORS § 1636.2 The claim of conscientious objection. A claim to classification in Class 1-A-0 or Class 1-0, must be made by the registrant in writing. Claims and documents in... or after the Director has made a specific request for submission of such documents. All claims or...

32 CFR 1636.2 - The claim of conscientious objection.

Code of Federal Regulations, 2011 CFR

2011-07-01

... CLASSIFICATION OF CONSCIENTIOUS OBJECTORS § 1636.2 The claim of conscientious objection. A claim to classification in Class 1-A-0 or Class 1-0, must be made by the registrant in writing. Claims and documents in... or after the Director has made a specific request for submission of such documents. All claims or...

Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010

USGS Publications Warehouse

Enwright, Nicholas M.; Hartley, Stephen B.; Couvillion, Brady R.; Michael G. Brasher,; Jenneke M. Visser,; Michael K. Mitchell,; Bart M. Ballard,; Mark W. Parr,; Barry C. Wilson,

2015-07-23

This study incorporates about 9,800 ground reference locations collected via helicopter surveys in coastal wetland areas. Decision-tree analyses were used to classify emergent marsh vegetation types by using ground reference data from helicopter vegetation surveys and independent variables such as multitemporal satellite-based multispectral imagery from 2009 to 2011, bare-earth digital elevation models based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables. Image objects were created from 2010 National Agriculture Imagery Program color-infrared aerial photography. The final classification is a 10-meter raster dataset that was produced by using a majority filter to classify image objects according to the marsh vegetation type covering the majority of each image object. The classification is dated 2010 because the year is both the midpoint of the classified multitemporal satellite-based imagery (2009–11) and the date of the high-resolution airborne imagery that was used to develop image objects. The seamless classification produced through this work can be used to help develop and refine conservation efforts for priority natural resources.

BioBlend.objects: metacomputing with Galaxy.

PubMed

Leo, Simone; Pireddu, Luca; Cuccuru, Gianmauro; Lianas, Luca; Soranzo, Nicola; Afgan, Enis; Zanetti, Gianluigi

2014-10-01

BioBlend.objects is a new component of the BioBlend package, adding an object-oriented interface for the Galaxy REST-based application programming interface. It improves support for metacomputing on Galaxy entities by providing higher-level functionality and allowing users to more easily create programs to explore, query and create Galaxy datasets and workflows. BioBlend.objects is available online at https://github.com/afgane/bioblend. The new object-oriented API is implemented by the galaxy/objects subpackage. © The Author 2014. Published by Oxford University Press.

An object oriented extension to CLIPS

NASA Technical Reports Server (NTRS)

Sobkowicz, Clifford

1990-01-01

A presentation of software sub-system developed to augment C Language Production Systems (CLIPS) with facilities for object oriented Knowledge representation. Functions are provided to define classes, instantiate objects, access attributes, and assert object related facts. This extension is implemented via the CLIPS user function interface and does not require modification of any CLIPS code. It does rely on internal CLIPS functions for memory management and symbol representation.

Selecting a Persistent Data Support Environment for Object-Oriented Applications

DTIC Science & Technology

1998-03-01

key features of most object DBMS products is contained in the <DWAS 9{eeds Assessment for Objects from Barry and Associates. The developer should...data structure and behavior in a self- contained module enhances maintainability of the system and promotes reuse of modules for similar domains...considered together, represent a survey of commercial object-oriented database management systems. These references contain detailed information needed

Virtual Workshop Environment (VWE): A Taxonomy and Service Oriented Architecture (SOA) Framework for Modularized Virtual Learning Environments (VLE)--Applying the Learning Object Concept to the VLE

ERIC Educational Resources Information Center

Paulsson, Fredrik; Naeve, Ambjorn

2006-01-01

Based on existing Learning Object taxonomies, this article suggests an alternative Learning Object taxonomy, combined with a general Service Oriented Architecture (SOA) framework, aiming to transfer the modularized concept of Learning Objects to modularized Virtual Learning Environments. The taxonomy and SOA-framework exposes a need for a clearer…

Object Orientation Affects Spatial Language Comprehension

ERIC Educational Resources Information Center

Burigo, Michele; Sacchi, Simona

2013-01-01

Typical spatial descriptions, such as "The car is in front of the house," describe the position of a located object (LO; e.g., the car) in space relative to a reference object (RO) whose location is known (e.g., the house). The orientation of the RO affects spatial language comprehension via the reference frame selection process.…

Exploring the Synergies between the Object Oriented Paradigm and Mathematics: A Java Led Approach

ERIC Educational Resources Information Center

Conrad, Marc; French, Tim

2004-01-01

While the object oriented paradigm and its instantiation within programming languages such as Java has become a ubiquitous part of both the commercial and educational landscapes, its usage as a visualization technique within mathematics undergraduate programmes of study has perhaps been somewhat underestimated. By regarding the object oriented…

Assessing Students' Structured Programming Skills with Java: The "Blue, Berry, and Blueberry" Assignment

ERIC Educational Resources Information Center

Zhang, Xihui

2010-01-01

Java is an object-oriented programming language. From a software engineering perspective, object-oriented design and programming is used at the architectural design, and structured design and programming is used at the detailed design within methods. As such, structured programming skills are fundamental to more advanced object-oriented…

A Mobile Service Oriented Multiple Object Tracking Augmented Reality Architecture for Education and Learning Experiences

ERIC Educational Resources Information Center

Rattanarungrot, Sasithorn; White, Martin; Newbury, Paul

2014-01-01

This paper describes the design of our service-oriented architecture to support mobile multiple object tracking augmented reality applications applied to education and learning scenarios. The architecture is composed of a mobile multiple object tracking augmented reality client, a web service framework, and dynamic content providers. Tracking of…

Personal Career Orientation. Performance Objectives. Criterion Measures. Home Economics.

ERIC Educational Resources Information Center

Allen, Alveta; And Others

Several intermediate performance objectives and corresponding criterion measures are listed for each of six terminal objectives for a personal career orientation course for seventh grade students. This 6- to 9-week course is designed to acquaint the student with personal qualities and characteristics necessary for success in the world of work.…