Machine Vision Giving Eyes to Robots. Resources in Technology.

ERIC Educational Resources Information Center

Technology Teacher, 1990

1990-01-01

This module introduces machine vision, which can be used for inspection, robot guidance and part sorting. The future for machine vision will include new technology and will bring vision systems closer to the ultimate vision processor, the human eye. Includes a student quiz, outcomes, and activities. (JOW)

Intrinsic dimensionality predicts the saliency of natural dynamic scenes.

PubMed

Vig, Eleonora; Dorr, Michael; Martinetz, Thomas; Barth, Erhardt

2012-06-01

Since visual attention-based computer vision applications have gained popularity, ever more complex, biologically inspired models seem to be needed to predict salient locations (or interest points) in naturalistic scenes. In this paper, we explore how far one can go in predicting eye movements by using only basic signal processing, such as image representations derived from efficient coding principles, and machine learning. To this end, we gradually increase the complexity of a model from simple single-scale saliency maps computed on grayscale videos to spatiotemporal multiscale and multispectral representations. Using a large collection of eye movements on high-resolution videos, supervised learning techniques fine-tune the free parameters whose addition is inevitable with increasing complexity. The proposed model, although very simple, demonstrates significant improvement in predicting salient locations in naturalistic videos over four selected baseline models and two distinct data labeling scenarios.

Machine vision for digital microfluidics

NASA Astrophysics Data System (ADS)

Shin, Yong-Jun; Lee, Jeong-Bong

2010-01-01

Machine vision is widely used in an industrial environment today. It can perform various tasks, such as inspecting and controlling production processes, that may require humanlike intelligence. The importance of imaging technology for biological research or medical diagnosis is greater than ever. For example, fluorescent reporter imaging enables scientists to study the dynamics of gene networks with high spatial and temporal resolution. Such high-throughput imaging is increasingly demanding the use of machine vision for real-time analysis and control. Digital microfluidics is a relatively new technology with expectations of becoming a true lab-on-a-chip platform. Utilizing digital microfluidics, only small amounts of biological samples are required and the experimental procedures can be automatically controlled. There is a strong need for the development of a digital microfluidics system integrated with machine vision for innovative biological research today. In this paper, we show how machine vision can be applied to digital microfluidics by demonstrating two applications: machine vision-based measurement of the kinetics of biomolecular interactions and machine vision-based droplet motion control. It is expected that digital microfluidics-based machine vision system will add intelligence and automation to high-throughput biological imaging in the future.

Progressive Vector Quantization on a massively parallel SIMD machine with application to multispectral image data

NASA Technical Reports Server (NTRS)

Manohar, Mareboyana; Tilton, James C.

1994-01-01

A progressive vector quantization (VQ) compression approach is discussed which decomposes image data into a number of levels using full search VQ. The final level is losslessly compressed, enabling lossless reconstruction. The computational difficulties are addressed by implementation on a massively parallel SIMD machine. We demonstrate progressive VQ on multispectral imagery obtained from the Advanced Very High Resolution Radiometer instrument and other Earth observation image data, and investigate the trade-offs in selecting the number of decomposition levels and codebook training method.

A multilevel multispectral data set analysis in the visible and infrared wavelength regions. [for land use remote sensing

NASA Technical Reports Server (NTRS)

Biehl, L. L.; Silva, L. F.

1975-01-01

Skylab multispectral scanner data, digitized Skylab color infrared (IR) photography, digitized Skylab black and white multiband photography, and Earth Resources Technology Satellite (ERTS) multispectral scanner data collected within a 24-hr time period over an area in south-central Indiana near Bloomington on June 9 and 10, 1973, were compared in a machine-aided land use analysis of the area. The overall classification performance results, obtained with nine land use classes, were 87% correct classification using the 'best' 4 channels of the Skylab multispectral scanner, 80% for the channels on the Skylab multispectral scanner which are spectrally comparable to the ERTS multispectral scanner, 88% for the ERTS multispectral scanner, 83% for the digitized color IR photography, and 76% for the digitized black and white multiband photography. The results indicate that the Skylab multispectral scanner may yield even higher classification accuracies when a noise-filtered multispectral scanner data set becomes available in the near future.

The Employment Effects of High-Technology: A Case Study of Machine Vision. Research Report No. 86-19.

ERIC Educational Resources Information Center

Chen, Kan; Stafford, Frank P.

A case study of machine vision was conducted to identify and analyze the employment effects of high technology in general. (Machine vision is the automatic acquisition and analysis of an image to obtain desired information for use in controlling an industrial activity, such as the visual sensor system that gives eyes to a robot.) Machine vision as…

Machine processing of remotely sensed data; Proceedings of the Conference, Purdue University, West Lafayette, Ind., October 16-18, 1973

NASA Technical Reports Server (NTRS)

1973-01-01

Topics discussed include the management and processing of earth resources information, special-purpose processors for the machine processing of remotely sensed data, digital image registration by a mathematical programming technique, the use of remote-sensor data in land classification (in particular, the use of ERTS-1 multispectral scanning data), the use of remote-sensor data in geometrical transformations and mapping, earth resource measurement with the aid of ERTS-1 multispectral scanning data, the use of remote-sensor data in the classification of turbidity levels in coastal zones and in the identification of ecological anomalies, the problem of feature selection and the classification of objects in multispectral images, the estimation of proportions of certain categories of objects, and a number of special systems and techniques. Individual items are announced in this issue.

Machine vision 1992-1996: technology program to promote research and its utilization in industry

NASA Astrophysics Data System (ADS)

Soini, Antti J.

1994-10-01

Machine vision technology has got a strong interest in Finnish research organizations, which is resulting in many innovative products to industry. Despite this end users were very skeptical towards machine vision and its robustness for harsh industrial environments. Therefore Technology Development Centre, TEKES, who funds technology related research and development projects in universities and individual companies, decided to start a national technology program, Machine Vision 1992 - 1996. Led by industry the program boosts research in machine vision technology and seeks to put the research results to work in practical industrial applications. The emphasis is in nationally important, demanding applications. The program will create new industry and business for machine vision producers and encourage the process and manufacturing industry to take advantage of this new technology. So far 60 companies and all major universities and research centers are working on our forty different projects. The key themes that we have are process control, robot vision and quality control.

Application of color mixing for safety and quality inspection of agricultural products

NASA Astrophysics Data System (ADS)

Ding, Fujian; Chen, Yud-Ren; Chao, Kuanglin

2005-11-01

In this paper, color-mixing applications for food safety and quality was studied, including two-color mixing and three-color mixing. It was shown that the chromaticness of the visual signal resulting from two- or three-color mixing is directly related to the band ratio of light intensity at the two or three selected wavebands. An optical visual device using color mixing to implement the band ratio criterion was presented. Inspection through human vision assisted by an optical device that implements the band ratio criterion would offer flexibility and significant cost savings as compared to inspection with a multispectral machine vision system that implements the same criterion. Example applications of this optical color mixing technique were given for the inspection of chicken carcasses with various diseases and for the detection of chilling injury in cucumbers. Simulation results showed that discrimination by chromaticness that has a direct relation with band ratio can work very well with proper selection of the two or three narrow wavebands. This novel color mixing technique for visual inspection can be implemented on visual devices for a variety of applications, ranging from target detection to food safety inspection.

Urban land use mapping by machine processing of ERTS-1 multispectral data: A San Francisco Bay area example

NASA Technical Reports Server (NTRS)

Ellefsen, R.; Swain, P. H.; Wray, J. R.

1973-01-01

The study is reported to develop computer produced urban land use maps using multispectral scanner data from a satellite is reported. Data processing is discussed along with the results of the San Francisco Bay area, which was chosen as the test area.

Improvement of a Harvester Based, Multispectral, Seed Cotton Fiber Quality Sensor

USDA-ARS?s Scientific Manuscript database

A multispectral sensor for in-situ seed cotton fiber quality measurement was developed and tested at Texas A&M University. Results of initial testing of the sensor using machine harvested seed cotton have shown promise. Improvements have been made to the system and the measurement method to meet t...

The fabrication of a multi-spectral lens array and its application in assisting color blindness

NASA Astrophysics Data System (ADS)

Di, Si; Jin, Jian; Tang, Guanrong; Chen, Xianshuai; Du, Ruxu

2016-01-01

This article presents a compact multi-spectral lens array and describes its application in assisting color-blindness. The lens array consists of 9 microlens, and each microlens is coated with a different color filter. Thus, it can capture different light bands, including red, orange, yellow, green, cyan, blue, violet, near-infrared, and the entire visible band. First, the fabrication process is described in detail. Second, an imaging system is setup and a color blindness testing card is selected as the sample. By the system, the vision results of normal people and color blindness can be captured simultaneously. Based on the imaging results, it is possible to be used for helping color-blindness to recover normal vision.

Field Geology/Processes

NASA Technical Reports Server (NTRS)

Allen, Carlton; Jakes, Petr; Jaumann, Ralf; Marshall, John; Moses, Stewart; Ryder, Graham; Saunders, Stephen; Singer, Robert

1996-01-01

The field geology/process group examined the basic operations of a terrestrial field geologist and the manner in which these operations could be transferred to a planetary lander. Four basic requirements for robotic field geology were determined: geologic content; surface vision; mobility; and manipulation. Geologic content requires a combination of orbital and descent imaging. Surface vision requirements include range, resolution, stereo, and multispectral imaging. The minimum mobility for useful field geology depends on the scale of orbital imagery. Manipulation requirements include exposing unweathered surfaces, screening samples, and bringing samples in contact with analytical instruments. To support these requirements, several advanced capabilities for future development are recommended. Capabilities include near-infrared reflectance spectroscopy, hyper-spectral imaging, multispectral microscopy, artificial intelligence in support of imaging, x ray diffraction, x ray fluorescence, and rock chipping.

Digital enhancement of multispectral MSS data for maximum image visibility

NASA Technical Reports Server (NTRS)

Algazi, V. R.

1973-01-01

A systematic approach to the enhancement of images has been developed. This approach exploits two principal features involved in the observation of images: the properties of human vision and the statistics of the images being observed. The rationale of the enhancement procedure is as follows: in the observation of some features of interest in an image, the range of objective luminance-chrominance values being displayed is generally limited and does not use the whole perceptual range of vision of the observer. The purpose of the enhancement technique is to expand and distort in a systematic way the grey scale values of each of the multispectral bands making up a color composite, to enhance the average visibility of the features being observed.

A Review of Imaging Techniques for Plant Phenotyping

PubMed Central

Li, Lei; Zhang, Qin; Huang, Danfeng

2014-01-01

Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review. PMID:25347588

Binary pressure-sensitive paint measurements using miniaturised, colour, machine vision cameras

NASA Astrophysics Data System (ADS)

Quinn, Mark Kenneth

2018-05-01

Recent advances in machine vision technology and capability have led to machine vision cameras becoming applicable for scientific imaging. This study aims to demonstrate the applicability of machine vision colour cameras for the measurement of dual-component pressure-sensitive paint (PSP). The presence of a second luminophore component in the PSP mixture significantly reduces its inherent temperature sensitivity, increasing its applicability at low speeds. All of the devices tested are smaller than the cooled CCD cameras traditionally used and most are of significantly lower cost, thereby increasing the accessibility of such technology and techniques. Comparisons between three machine vision cameras, a three CCD camera, and a commercially available specialist PSP camera are made on a range of parameters, and a detailed PSP calibration is conducted in a static calibration chamber. The findings demonstrate that colour machine vision cameras can be used for quantitative, dual-component, pressure measurements. These results give rise to the possibility of performing on-board dual-component PSP measurements in wind tunnels or on real flight/road vehicles.

Michigan experimental multispectral mapping system: A description of the M7 airborne sensor and its performance

NASA Technical Reports Server (NTRS)

Hasell, P. G., Jr.

1974-01-01

The development and characteristics of a multispectral band scanner for an airborne mapping system are discussed. The sensor operates in the ultraviolet, visual, and infrared frequencies. Any twelve of the bands may be selected for simultaneous, optically registered recording on a 14-track analog tape recorder. Multispectral imagery recorded on magnetic tape in the aircraft can be laboratory reproduced on film strips for visual analysis or optionally machine processed in analog and/or digital computers before display. The airborne system performance is analyzed.

Industrial Inspection with Open Eyes: Advance with Machine Vision Technology

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

Liu, Zheng; Ukida, H.; Niel, Kurt

Machine vision systems have evolved significantly with the technology advances to tackle the challenges from modern manufacturing industry. A wide range of industrial inspection applications for quality control are benefiting from visual information captured by different types of cameras variously configured in a machine vision system. This chapter screens the state of the art in machine vision technologies in the light of hardware, software tools, and major algorithm advances for industrial inspection. The inspection beyond visual spectrum offers a significant complementary to the visual inspection. The combination with multiple technologies makes it possible for the inspection to achieve a bettermore » performance and efficiency in varied applications. The diversity of the applications demonstrates the great potential of machine vision systems for industry.« less

Machine vision systems using machine learning for industrial product inspection

NASA Astrophysics Data System (ADS)

Lu, Yi; Chen, Tie Q.; Chen, Jie; Zhang, Jian; Tisler, Anthony

2002-02-01

Machine vision inspection requires efficient processing time and accurate results. In this paper, we present a machine vision inspection architecture, SMV (Smart Machine Vision). SMV decomposes a machine vision inspection problem into two stages, Learning Inspection Features (LIF), and On-Line Inspection (OLI). The LIF is designed to learn visual inspection features from design data and/or from inspection products. During the OLI stage, the inspection system uses the knowledge learnt by the LIF component to inspect the visual features of products. In this paper we will present two machine vision inspection systems developed under the SMV architecture for two different types of products, Printed Circuit Board (PCB) and Vacuum Florescent Displaying (VFD) boards. In the VFD board inspection system, the LIF component learns inspection features from a VFD board and its displaying patterns. In the PCB board inspection system, the LIF learns the inspection features from the CAD file of a PCB board. In both systems, the LIF component also incorporates interactive learning to make the inspection system more powerful and efficient. The VFD system has been deployed successfully in three different manufacturing companies and the PCB inspection system is the process of being deployed in a manufacturing plant.

Ethical, environmental and social issues for machine vision in manufacturing industry

NASA Astrophysics Data System (ADS)

Batchelor, Bruce G.; Whelan, Paul F.

1995-10-01

Some of the ethical, environmental and social issues relating to the design and use of machine vision systems in manufacturing industry are highlighted. The authors' aim is to emphasize some of the more important issues, and raise general awareness of the need to consider the potential advantages and hazards of machine vision technology. However, in a short article like this, it is impossible to cover the subject comprehensively. This paper should therefore be seen as a discussion document, which it is hoped will provoke more detailed consideration of these very important issues. It follows from an article presented at last year's workshop. Five major topics are discussed: (1) The impact of machine vision systems on the environment; (2) The implications of machine vision for product and factory safety, the health and well-being of employees; (3) The importance of intellectual integrity in a field requiring a careful balance of advanced ideas and technologies; (4) Commercial and managerial integrity; and (5) The impact of machine visions technology on employment prospects, particularly for people with low skill levels.

Basic design principles of colorimetric vision systems

NASA Astrophysics Data System (ADS)

Mumzhiu, Alex M.

1998-10-01

Color measurement is an important part of overall production quality control in textile, coating, plastics, food, paper and other industries. The color measurement instruments such as colorimeters and spectrophotometers, used for production quality control have many limitations. In many applications they cannot be used for a variety of reasons and have to be replaced with human operators. Machine vision has great potential for color measurement. The components for color machine vision systems, such as broadcast quality 3-CCD cameras, fast and inexpensive PCI frame grabbers, and sophisticated image processing software packages are available. However the machine vision industry has only started to approach the color domain. The few color machine vision systems on the market, produced by the largest machine vision manufacturers have very limited capabilities. A lack of understanding that a vision based color measurement system could fail if it ignores the basic principles of colorimetry is the main reason for the slow progress of color vision systems. the purpose of this paper is to clarify how color measurement principles have to be applied to vision systems and how the electro-optical design features of colorimeters have to be modified in order to implement them for vision systems. The subject of this presentation far exceeds the limitations of a journal paper so only the most important aspects will be discussed. An overview of the major areas of applications for colorimetric vision system will be discussed. Finally, the reasons why some customers are happy with their vision systems and some are not will be analyzed.

Biomimetic machine vision system.

PubMed

Harman, William M; Barrett, Steven F; Wright, Cameron H G; Wilcox, Michael

2005-01-01

Real-time application of digital imaging for use in machine vision systems has proven to be prohibitive when used within control systems that employ low-power single processors without compromising the scope of vision or resolution of captured images. Development of a real-time machine analog vision system is the focus of research taking place at the University of Wyoming. This new vision system is based upon the biological vision system of the common house fly. Development of a single sensor is accomplished, representing a single facet of the fly's eye. This new sensor is then incorporated into an array of sensors capable of detecting objects and tracking motion in 2-D space. This system "preprocesses" incoming image data resulting in minimal data processing to determine the location of a target object. Due to the nature of the sensors in the array, hyperacuity is achieved thereby eliminating resolutions issues found in digital vision systems. In this paper, we will discuss the biological traits of the fly eye and the specific traits that led to the development of this machine vision system. We will also discuss the process of developing an analog based sensor that mimics the characteristics of interest in the biological vision system. This paper will conclude with a discussion of how an array of these sensors can be applied toward solving real-world machine vision issues.

Task-focused modeling in automated agriculture

NASA Astrophysics Data System (ADS)

Vriesenga, Mark R.; Peleg, K.; Sklansky, Jack

1993-01-01

Machine vision systems analyze image data to carry out automation tasks. Our interest is in machine vision systems that rely on models to achieve their designed task. When the model is interrogated from an a priori menu of questions, the model need not be complete. Instead, the machine vision system can use a partial model that contains a large amount of information in regions of interest and less information elsewhere. We propose an adaptive modeling scheme for machine vision, called task-focused modeling, which constructs a model having just sufficient detail to carry out the specified task. The model is detailed in regions of interest to the task and is less detailed elsewhere. This focusing effect saves time and reduces the computational effort expended by the machine vision system. We illustrate task-focused modeling by an example involving real-time micropropagation of plants in automated agriculture.

Three-color mixing for classifying agricultural products for safety and quality

NASA Astrophysics Data System (ADS)

Ding, Fujian; Chen, Yud-Ren; Chao, Kuanglin; Kim, Moon S.

2006-05-01

A three-color mixing application for food safety inspection is presented. It is shown that the chromaticness of the visual signal resulting from the three-color mixing achieved through our device is directly related to the three-band ratio of light intensity at three selected wavebands. An optical visual device using three-color mixing to implement the three-band ratio criterion is presented. Inspection through human vision assisted by an optical device that implements the three-band ratio criterion would offer flexibility and significant cost savings as compared to inspection with a multispectral machine vision system that implements the same criterion. Example applications of this optical three-color mixing technique are given for the inspection of chicken carcasses with various diseases and for apples with fecal contamination. With proper selection of the three narrow wavebands, discrimination by chromaticness that has a direct relation with the three-band ratio can work very well. In particular, compared with the previously presented two-color mixing application, the conditions of chicken carcasses were more easily identified using the three-color mixing application. The novel three-color mixing technique for visual inspection can be implemented on visual devices for a variety of applications, ranging from target detection to food safety inspection.

A Machine Vision System for Automatically Grading Hardwood Lumber - (Proceedings)

Treesearch

Richard W. Conners; Tai-Hoon Cho; Chong T. Ng; Thomas H. Drayer; Joe G. Tront; Philip A. Araman; Robert L. Brisbon

1990-01-01

Any automatic system for grading hardwood lumber can conceptually be divided into two components. One of these is a machine vision system for locating and identifying grading defects. The other is an automatic grading program that accepts as input the output of the machine vision system and, based on these data, determines the grade of a board. The progress that has...

Using Multiple FPGA Architectures for Real-time Processing of Low-level Machine Vision Functions

Treesearch

Thomas H. Drayer; William E. King; Philip A. Araman; Joseph G. Tront; Richard W. Conners

1995-01-01

In this paper, we investigate the use of multiple Field Programmable Gate Array (FPGA) architectures for real-time machine vision processing. The use of FPGAs for low-level processing represents an excellent tradeoff between software and special purpose hardware implementations. A library of modules that implement common low-level machine vision operations is presented...

Machine vision for real time orbital operations

NASA Technical Reports Server (NTRS)

Vinz, Frank L.

1988-01-01

Machine vision for automation and robotic operation of Space Station era systems has the potential for increasing the efficiency of orbital servicing, repair, assembly and docking tasks. A machine vision research project is described in which a TV camera is used for inputing visual data to a computer so that image processing may be achieved for real time control of these orbital operations. A technique has resulted from this research which reduces computer memory requirements and greatly increases typical computational speed such that it has the potential for development into a real time orbital machine vision system. This technique is called AI BOSS (Analysis of Images by Box Scan and Syntax).

Knowledge-based vision and simple visual machines.

PubMed Central

Cliff, D; Noble, J

1997-01-01

The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong. PMID:9304684

Machine vision system for inspecting characteristics of hybrid rice seed

NASA Astrophysics Data System (ADS)

Cheng, Fang; Ying, Yibin

2004-03-01

Obtaining clear images advantaged of improving the classification accuracy involves many factors, light source, lens extender and background were discussed in this paper. The analysis of rice seed reflectance curves showed that the wavelength of light source for discrimination of the diseased seeds from normal rice seeds in the monochromic image recognition mode was about 815nm for jinyou402 and shanyou10. To determine optimizing conditions for acquiring digital images of rice seed using a computer vision system, an adjustable color machine vision system was developed. The machine vision system with 20mm to 25mm lens extender produce close-up images which made it easy to object recognition of characteristics in hybrid rice seeds. White background was proved to be better than black background for inspecting rice seeds infected by disease and using the algorithms based on shape. Experimental results indicated good classification for most of the characteristics with the machine vision system. The same algorithm yielded better results in optimizing condition for quality inspection of rice seed. Specifically, the image processing can correct for details such as fine fissure with the machine vision system.

A Machine Vision System for Automatically Grading Hardwood Lumber - (Industrial Metrology)

Treesearch

Richard W. Conners; Tai-Hoon Cho; Chong T. Ng; Thomas T. Drayer; Philip A. Araman; Robert L. Brisbon

1992-01-01

Any automatic system for grading hardwood lumber can conceptually be divided into two components. One of these is a machine vision system for locating and identifying grading defects. The other is an automatic grading program that accepts as input the output of the machine vision system and, based on these data, determines the grade of a board. The progress that has...

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling.

PubMed

Estelles-Lopez, Lucia; Ropodi, Athina; Pavlidis, Dimitris; Fotopoulou, Jenny; Gkousari, Christina; Peyrodie, Audrey; Panagou, Efstathios; Nychas, George-John; Mohareb, Fady

2017-09-01

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, "MeatReg", a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg" was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC-MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: www.sorfml.com. Copyright © 2017 Elsevier Ltd. All rights reserved.

Color line scan camera technology and machine vision: requirements to consider

NASA Astrophysics Data System (ADS)

Paernaenen, Pekka H. T.

1997-08-01

Color machine vision has shown a dynamic uptrend in use within the past few years as the introduction of new cameras and scanner technologies itself underscores. In the future, the movement from monochrome imaging to color will hasten, as machine vision system users demand more knowledge about their product stream. As color has come to the machine vision, certain requirements for the equipment used to digitize color images are needed. Color machine vision needs not only a good color separation but also a high dynamic range and a good linear response from the camera used. Good dynamic range and linear response is necessary for color machine vision. The importance of these features becomes even more important when the image is converted to another color space. There is always lost some information when converting integer data to another form. Traditionally the color image processing has been much slower technique than the gray level image processing due to the three times greater data amount per image. The same has applied for the three times more memory needed. The advancements in computers, memory and processing units has made it possible to handle even large color images today cost efficiently. In some cases he image analysis in color images can in fact even be easier and faster than with a similar gray level image because of more information per pixel. Color machine vision sets new requirements for lighting, too. High intensity and white color light is required in order to acquire good images for further image processing or analysis. New development in lighting technology is bringing eventually solutions for color imaging.

Data processing 1: Advancements in machine analysis of multispectral data

NASA Technical Reports Server (NTRS)

Swain, P. H.

1972-01-01

Multispectral data processing procedures are outlined beginning with the data display process used to accomplish data editing and proceeding through clustering, feature selection criterion for error probability estimation, and sample clustering and sample classification. The effective utilization of large quantities of remote sensing data by formulating a three stage sampling model for evaluation of crop acreage estimates represents an improvement in determining the cost benefit relationship associated with remote sensing technology.

Machine Learning, deep learning and optimization in computer vision

NASA Astrophysics Data System (ADS)

Canu, Stéphane

2017-03-01

As quoted in the Large Scale Computer Vision Systems NIPS workshop, computer vision is a mature field with a long tradition of research, but recent advances in machine learning, deep learning, representation learning and optimization have provided models with new capabilities to better understand visual content. The presentation will go through these new developments in machine learning covering basic motivations, ideas, models and optimization in deep learning for computer vision, identifying challenges and opportunities. It will focus on issues related with large scale learning that is: high dimensional features, large variety of visual classes, and large number of examples.

Rapid and non-destructive identification of water-injected beef samples using multispectral imaging analysis.

PubMed

Liu, Jinxia; Cao, Yue; Wang, Qiu; Pan, Wenjuan; Ma, Fei; Liu, Changhong; Chen, Wei; Yang, Jianbo; Zheng, Lei

2016-01-01

Water-injected beef has aroused public concern as a major food-safety issue in meat products. In the study, the potential of multispectral imaging analysis in the visible and near-infrared (405-970 nm) regions was evaluated for identifying water-injected beef. A multispectral vision system was used to acquire images of beef injected with up to 21% content of water, and partial least squares regression (PLSR) algorithm was employed to establish prediction model, leading to quantitative estimations of actual water increase with a correlation coefficient (r) of 0.923. Subsequently, an optimized model was achieved by integrating spectral data with feature information extracted from ordinary RGB data, yielding better predictions (r = 0.946). Moreover, the prediction equation was transferred to each pixel within the images for visualizing the distribution of actual water increase. These results demonstrate the capability of multispectral imaging technology as a rapid and non-destructive tool for the identification of water-injected beef. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multispectral Image Compression for Improvement of Colorimetric and Spectral Reproducibility by Nonlinear Spectral Transform

NASA Astrophysics Data System (ADS)

Yu, Shanshan; Murakami, Yuri; Obi, Takashi; Yamaguchi, Masahiro; Ohyama, Nagaaki

2006-09-01

The article proposes a multispectral image compression scheme using nonlinear spectral transform for better colorimetric and spectral reproducibility. In the method, we show the reduction of colorimetric error under a defined viewing illuminant and also that spectral accuracy can be improved simultaneously using a nonlinear spectral transform called Labplus, which takes into account the nonlinearity of human color vision. Moreover, we show that the addition of diagonal matrices to Labplus can further preserve the spectral accuracy and has a generalized effect of improving the colorimetric accuracy under other viewing illuminants than the defined one. Finally, we discuss the usage of the first-order Markov model to form the analysis vectors for the higher order channels in Labplus to reduce the computational complexity. We implement a multispectral image compression system that integrates Labplus with JPEG2000 for high colorimetric and spectral reproducibility. Experimental results for a 16-band multispectral image show the effectiveness of the proposed scheme.

Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers

NASA Astrophysics Data System (ADS)

Maier, Oskar; Wilms, Matthias; von der Gablentz, Janina; Krämer, Ulrike; Handels, Heinz

2014-03-01

Automatic segmentation of ischemic stroke lesions in magnetic resonance (MR) images is important in clinical practice and for neuroscientific trials. The key problem is to detect largely inhomogeneous regions of varying sizes, shapes and locations. We present a stroke lesion segmentation method based on local features extracted from multi-spectral MR data that are selected to model a human observer's discrimination criteria. A support vector machine classifier is trained on expert-segmented examples and then used to classify formerly unseen images. Leave-one-out cross validation on eight datasets with lesions of varying appearances is performed, showing our method to compare favourably with other published approaches in terms of accuracy and robustness. Furthermore, we compare a number of feature selectors and closely examine each feature's and MR sequence's contribution.

A study of the utilization of EREP data from the Wabash River basin

NASA Technical Reports Server (NTRS)

Silva, L. F. (Principal Investigator)

1976-01-01

The author has identified the following significant results. The study of the multispectral data sets indicate that better land use delineation using machine processing techniques can be obtained with data from multispectral scanners than digitized S190A photographic sensor data. Results of the multiemulsion photographic data set were a little better than the multiband photographic data set. Comparison results of the interim and filtered S191 data indicate that the data were improved some for machine processing techniques. Results of the S191 X-5 detector array studied over a wintertime scene indicate that a good quality far infrared channel can be useful. The S191 spectroradiometer study results indicate that the data from the S191 was usable, and it was possible to estimate the path radiance.

Neural Networks for Computer Vision: A Framework for Specifications of a General Purpose Vision System

NASA Astrophysics Data System (ADS)

Skrzypek, Josef; Mesrobian, Edmond; Gungner, David J.

1989-03-01

The development of autonomous land vehicles (ALV) capable of operating in an unconstrained environment has proven to be a formidable research effort. The unpredictability of events in such an environment calls for the design of a robust perceptual system, an impossible task requiring the programming of a system bases on the expectation of future, unconstrained events. Hence, the need for a "general purpose" machine vision system that is capable of perceiving and understanding images in an unconstrained environment in real-time. The research undertaken at the UCLA Machine Perception Laboratory addresses this need by focusing on two specific issues: 1) the long term goals for machine vision research as a joint effort between the neurosciences and computer science; and 2) a framework for evaluating progress in machine vision. In the past, vision research has been carried out independently within different fields including neurosciences, psychology, computer science, and electrical engineering. Our interdisciplinary approach to vision research is based on the rigorous combination of computational neuroscience, as derived from neurophysiology and neuropsychology, with computer science and electrical engineering. The primary motivation behind our approach is that the human visual system is the only existing example of a "general purpose" vision system and using a neurally based computing substrate, it can complete all necessary visual tasks in real-time.

A Machine Vision Quality Control System for Industrial Acrylic Fibre Production

NASA Astrophysics Data System (ADS)

Heleno, Paulo; Davies, Roger; Correia, Bento A. Brázio; Dinis, João

2002-12-01

This paper describes the implementation of INFIBRA, a machine vision system used in the quality control of acrylic fibre production. The system was developed by INETI under a contract with a leading industrial manufacturer of acrylic fibres. It monitors several parameters of the acrylic production process. This paper presents, after a brief overview of the system, a detailed description of the machine vision algorithms developed to perform the inspection tasks unique to this system. Some of the results of online operation are also presented.

Machine Vision Systems for Processing Hardwood Lumber and Logs

Treesearch

Philip A. Araman; Daniel L. Schmoldt; Tai-Hoon Cho; Dongping Zhu; Richard W. Conners; D. Earl Kline

1992-01-01

Machine vision and automated processing systems are under development at Virginia Tech University with support and cooperation from the USDA Forest Service. Our goals are to help U.S. hardwood producers automate, reduce costs, increase product volume and value recovery, and market higher value, more accurately graded and described products. Any vision system is...

Robust Spatial Autoregressive Modeling for Hardwood Log Inspection

Treesearch

Dongping Zhu; A.A. Beex

1994-01-01

We explore the application of a stochastic texture modeling method toward a machine vision system for log inspection in the forest products industry. This machine vision system uses computerized tomography (CT) imaging to locate and identify internal defects in hardwood logs. The application of CT to such industrial vision problems requires efficient and robust image...

Study on the Feasibility of RGB Substitute CIR for Automatic Removal Vegetation Occlusion Based on Ground Close-Range Building Images

NASA Astrophysics Data System (ADS)

Li, C.; Li, F.; Liu, Y.; Li, X.; Liu, P.; Xiao, B.

2012-07-01

Building 3D reconstruction based on ground remote sensing data (image, video and lidar) inevitably faces the problem that buildings are always occluded by vegetation, so how to automatically remove and repair vegetation occlusion is a very important preprocessing work for image understanding, compute vision and digital photogrammetry. In the traditional multispectral remote sensing which is achieved by aeronautics and space platforms, the Red and Near-infrared (NIR) bands, such as NDVI (Normalized Difference Vegetation Index), are useful to distinguish vegetation and clouds, amongst other targets. However, especially in the ground platform, CIR (Color Infra Red) is little utilized by compute vision and digital photogrammetry which usually only take true color RBG into account. Therefore whether CIR is necessary for vegetation segmentation or not has significance in that most of close-range cameras don't contain such NIR band. Moreover, the CIE L*a*b color space, which transform from RGB, seems not of much interest by photogrammetrists despite its powerfulness in image classification and analysis. So, CIE (L, a, b) feature and support vector machine (SVM) is suggested for vegetation segmentation to substitute for CIR. Finally, experimental results of visual effect and automation are given. The conclusion is that it's feasible to remove and segment vegetation occlusion without NIR band. This work should pave the way for texture reconstruction and repair for future 3D reconstruction.

Two-color mixing for classifying agricultural products for safety and quality

NASA Astrophysics Data System (ADS)

Ding, Fujian; Chen, Yud-Ren; Chao, Kuanglin; Chan, Diane E.

2006-02-01

We show that the chromaticness of the visual signal that results from the two-color mixing achieved through an optically enhanced binocular device is directly related to the band ratio of light intensity at the two selected wavebands. A technique that implements the band-ratio criterion in a visual device by using two-color mixing is presented here. The device will allow inspectors to identify targets visually in accordance with a two-wavelength band ratio. It is a method of inspection by human vision assisted by an optical device, which offers greater flexibility and better cost savings than a multispectral machine vision system that implements the band-ratio criterion. With proper selection of the two narrow wavebands, discrimination by chromaticness that is directly related to the band ratio can work well. An example application of this technique for the inspection of carcasses chickens of afficted with various diseases is given. An optimal pair of wavelengths of 454 and 578 nm was selected to optimize differences in saturation and hue in CIE LUV color space among different types of target. Another example application, for the detection of chilling injury in cucumbers, is given, here the selected wavelength pair was 504 and 652 nm. The novel two-color mixing technique for visual inspection can be included in visual devices for various applications, ranging from target detection to food safety inspection.

Parallel Algorithms for Computer Vision

DTIC Science & Technology

1990-04-01

NA86-1, Thinking Machines Corporation, Cambridge, MA, December 1986. [43] J. Little, G. Blelloch, and T. Cass. How to program the connection machine for... to program the connection machine for computer vision. In Proc. Workshop on Comp. Architecture for Pattern Analysis and Machine Intell., 1987. [92] J...In Proceedings of SPIE Conf. on Advances in Intelligent Robotics Systems, Bellingham, VA, 1987. SPIE. [91] J. Little, G. Blelloch, and T. Cass. How

Color image processing and vision system for an automated laser paint-stripping system

NASA Astrophysics Data System (ADS)

Hickey, John M., III; Hise, Lawson

1994-10-01

Color image processing in machine vision systems has not gained general acceptance. Most machine vision systems use images that are shades of gray. The Laser Automated Decoating System (LADS) required a vision system which could discriminate between substrates of various colors and textures and paints ranging from semi-gloss grays to high gloss red, white and blue (Air Force Thunderbirds). The changing lighting levels produced by the pulsed CO2 laser mandated a vision system that did not require a constant color temperature lighting for reliable image analysis.

Machine learning and computer vision approaches for phenotypic profiling.

PubMed

Grys, Ben T; Lo, Dara S; Sahin, Nil; Kraus, Oren Z; Morris, Quaid; Boone, Charles; Andrews, Brenda J

2017-01-02

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. © 2017 Grys et al.

Machine learning and computer vision approaches for phenotypic profiling

PubMed Central

Morris, Quaid

2017-01-01

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. PMID:27940887

IMAGE 100: The interactive multispectral image processing system

NASA Technical Reports Server (NTRS)

Schaller, E. S.; Towles, R. W.

1975-01-01

The need for rapid, cost-effective extraction of useful information from vast quantities of multispectral imagery available from aircraft or spacecraft has resulted in the design, implementation and application of a state-of-the-art processing system known as IMAGE 100. Operating on the general principle that all objects or materials possess unique spectral characteristics or signatures, the system uses this signature uniqueness to identify similar features in an image by simultaneously analyzing signatures in multiple frequency bands. Pseudo-colors, or themes, are assigned to features having identical spectral characteristics. These themes are displayed on a color CRT, and may be recorded on tape, film, or other media. The system was designed to incorporate key features such as interactive operation, user-oriented displays and controls, and rapid-response machine processing. Owing to these features, the user can readily control and/or modify the analysis process based on his knowledge of the input imagery. Effective use can be made of conventional photographic interpretation skills and state-of-the-art machine analysis techniques in the extraction of useful information from multispectral imagery. This approach results in highly accurate multitheme classification of imagery in seconds or minutes rather than the hours often involved in processing using other means.

Trends and developments in industrial machine vision: 2013

NASA Astrophysics Data System (ADS)

Niel, Kurt; Heinzl, Christoph

2014-03-01

When following current advancements and implementations in the field of machine vision there seems to be no borders for future developments: Calculating power constantly increases, and new ideas are spreading and previously challenging approaches are introduced in to mass market. Within the past decades these advances have had dramatic impacts on our lives. Consumer electronics, e.g. computers or telephones, which once occupied large volumes, now fit in the palm of a hand. To note just a few examples e.g. face recognition was adopted by the consumer market, 3D capturing became cheap, due to the huge community SW-coding got easier using sophisticated development platforms. However, still there is a remaining gap between consumer and industrial applications. While the first ones have to be entertaining, the second have to be reliable. Recent studies (e.g. VDMA [1], Germany) show a moderately increasing market for machine vision in industry. Asking industry regarding their needs the main challenges for industrial machine vision are simple usage and reliability for the process, quick support, full automation, self/easy adjustment at changing process parameters, "forget it in the line". Furthermore a big challenge is to support quality control: Nowadays the operator has to accurately define the tested features for checking the probes. There is an upcoming development also to let automated machine vision applications find out essential parameters in a more abstract level (top down). In this work we focus on three current and future topics for industrial machine vision: Metrology supporting automation, quality control (inline/atline/offline) as well as visualization and analysis of datasets with steadily growing sizes. Finally the general trend of the pixel orientated towards object orientated evaluation is addressed. We do not directly address the field of robotics taking advances from machine vision. This is actually a fast changing area which is worth an own contribution.

75 FR 71146 - In the Matter of Certain Machine Vision Software, Machine Vision Systems, and Products Containing...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-22

...''); Amistar Automation, Inc. (``Amistar'') of San Marcos, California; Techno Soft Systemnics, Inc. (``Techno..., the ALJ's construction of the claim terms ``test,'' ``match score surface,'' and ``gradient direction...

Miniaturisation of Pressure-Sensitive Paint Measurement Systems Using Low-Cost, Miniaturised Machine Vision Cameras.

PubMed

Quinn, Mark Kenneth; Spinosa, Emanuele; Roberts, David A

2017-07-25

Measurements of pressure-sensitive paint (PSP) have been performed using new or non-scientific imaging technology based on machine vision tools. Machine vision camera systems are typically used for automated inspection or process monitoring. Such devices offer the benefits of lower cost and reduced size compared with typically scientific-grade cameras; however, their optical qualities and suitability have yet to be determined. This research intends to show relevant imaging characteristics and also show the applicability of such imaging technology for PSP. Details of camera performance are benchmarked and compared to standard scientific imaging equipment and subsequent PSP tests are conducted using a static calibration chamber. The findings demonstrate that machine vision technology can be used for PSP measurements, opening up the possibility of performing measurements on-board small-scale model such as those used for wind tunnel testing or measurements in confined spaces with limited optical access.

Miniaturisation of Pressure-Sensitive Paint Measurement Systems Using Low-Cost, Miniaturised Machine Vision Cameras

PubMed Central

Spinosa, Emanuele; Roberts, David A.

2017-01-01

Measurements of pressure-sensitive paint (PSP) have been performed using new or non-scientific imaging technology based on machine vision tools. Machine vision camera systems are typically used for automated inspection or process monitoring. Such devices offer the benefits of lower cost and reduced size compared with typically scientific-grade cameras; however, their optical qualities and suitability have yet to be determined. This research intends to show relevant imaging characteristics and also show the applicability of such imaging technology for PSP. Details of camera performance are benchmarked and compared to standard scientific imaging equipment and subsequent PSP tests are conducted using a static calibration chamber. The findings demonstrate that machine vision technology can be used for PSP measurements, opening up the possibility of performing measurements on-board small-scale model such as those used for wind tunnel testing or measurements in confined spaces with limited optical access. PMID:28757553

Recent advances in the development and transfer of machine vision technologies for space

NASA Technical Reports Server (NTRS)

Defigueiredo, Rui J. P.; Pendleton, Thomas

1991-01-01

Recent work concerned with real-time machine vision is briefly reviewed. This work includes methodologies and techniques for optimal illumination, shape-from-shading of general (non-Lambertian) 3D surfaces, laser vision devices and technology, high level vision, sensor fusion, real-time computing, artificial neural network design and use, and motion estimation. Two new methods that are currently being developed for object recognition in clutter and for 3D attitude tracking based on line correspondence are discussed.

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

NASA Astrophysics Data System (ADS)

Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

2017-12-01

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was obtained by fusion of information from all three sensors with an RMSE of 11.6%. (2) Among the plant biophysical variables, LAI was best predicted by RGB and thermal data fusion while multispectral and thermal data fusion was found to be best for biomass estimation. (3) For estimation of the above mentioned plant traits of soybean from multi-sensor data fusion, ELR yields promising results compared to PLSR and SVR in this study. This research indicates that fusion of low-cost multiple sensor data within a machine learning framework can provide relatively accurate estimation of plant traits and provide valuable insight for high spatial precision in agriculture and plant stress assessment.

WISESight : a multispectral smart video-track intrusion monitor.

DOT National Transportation Integrated Search

2015-05-01

International Electronic Machines : Corporation (IEM) developed, tested, and : validated a unique smart video-based : intrusion monitoring system for use at : highway-rail grade crossings. The system : used both thermal infrared (IR) and : visible/ne...

Classification of high-resolution multispectral satellite remote sensing images using extended morphological attribute profiles and independent component analysis

NASA Astrophysics Data System (ADS)

Wu, Yu; Zheng, Lijuan; Xie, Donghai; Zhong, Ruofei

2017-07-01

In this study, the extended morphological attribute profiles (EAPs) and independent component analysis (ICA) were combined for feature extraction of high-resolution multispectral satellite remote sensing images and the regularized least squares (RLS) approach with the radial basis function (RBF) kernel was further applied for the classification. Based on the major two independent components, the geometrical features were extracted using the EAPs method. In this study, three morphological attributes were calculated and extracted for each independent component, including area, standard deviation, and moment of inertia. The extracted geometrical features classified results using RLS approach and the commonly used LIB-SVM library of support vector machines method. The Worldview-3 and Chinese GF-2 multispectral images were tested, and the results showed that the features extracted by EAPs and ICA can effectively improve the accuracy of the high-resolution multispectral image classification, 2% larger than EAPs and principal component analysis (PCA) method, and 6% larger than APs and original high-resolution multispectral data. Moreover, it is also suggested that both the GURLS and LIB-SVM libraries are well suited for the multispectral remote sensing image classification. The GURLS library is easy to be used with automatic parameter selection but its computation time may be larger than the LIB-SVM library. This study would be helpful for the classification application of high-resolution multispectral satellite remote sensing images.

Machine Vision For Industrial Control:The Unsung Opportunity

NASA Astrophysics Data System (ADS)

Falkman, Gerald A.; Murray, Lawrence A.; Cooper, James E.

1984-05-01

Vision modules have primarily been developed to relieve those pressures newly brought into existence by Inspection (QUALITY) and Robotic (PRODUCTIVITY) mandates. Industrial Control pressure stems on the other hand from the older first industrial revolution mandate of throughput. Satisfying such pressure calls for speed in both imaging and decision making. Vision companies have, however, put speed on a backburner or ignore it entirely because most modules are computer/software based which limits their speed potential. Increasingly, the keynote being struck at machine vision seminars is that "Visual and Computational Speed Must Be Increased and Dramatically!" There are modular hardwired-logic systems that are fast but, all too often, they are not very bright. Such units: Measure the fill factor of bottles as they spin by, Read labels on cans, Count stacked plastic cups or Monitor the width of parts streaming past the camera. Many are only a bit more complex than a photodetector. Once in place, most of these units are incapable of simple upgrading to a new task and are Vision's analog to the robot industry's pick and place (RIA TYPE E) robot. Vision thus finds itself amidst the same quandries that once beset the Robot Industry of America when it tried to define a robot, excluded dumb ones, and was left with only slow machines whose unit volume potential is shatteringly low. This paper develops an approach to meeting the need of a vision system that cuts a swath into the terra incognita of intelligent, high-speed vision processing. Main attention is directed to vision for industrial control. Some presently untapped vision application areas that will be serviced include: Electronics, Food, Sports, Pharmaceuticals, Machine Tools and Arc Welding.

Evaluation of surface water resources from machine-processing of ERTS multispectral data

NASA Technical Reports Server (NTRS)

Mausel, P. W.; Todd, W. J.; Baumgardner, M. F.; Mitchell, R. A.; Cook, J. P.

1976-01-01

The surface water resources of a large metropolitan area, Marion County (Indianapolis), Indiana, are studied in order to assess the potential value of ERTS spectral analysis to water resources problems. The results of the research indicate that all surface water bodies over 0.5 ha were identified accurately from ERTS multispectral analysis. Five distinct classes of water were identified and correlated with parameters which included: degree of water siltiness; depth of water; presence of macro and micro biotic forms in the water; and presence of various chemical concentrations in the water. The machine processing of ERTS spectral data used alone or in conjunction with conventional sources of hydrological information can lead to the monitoring of area of surface water bodies; estimated volume of selected surface water bodies; differences in degree of silt and clay suspended in water and degree of water eutrophication related to chemical concentrations.

Machine vision system for online inspection of freshly slaughtered chickens

USDA-ARS?s Scientific Manuscript database

A machine vision system was developed and evaluated for the automation of online inspection to differentiate freshly slaughtered wholesome chickens from systemically diseased chickens. The system consisted of an electron-multiplying charge-coupled-device camera used with an imaging spectrograph and ...

A self-learning camera for the validation of highly variable and pseudorandom patterns

NASA Astrophysics Data System (ADS)

Kelley, Michael

2004-05-01

Reliable and productive manufacturing operations have depended on people to quickly detect and solve problems whenever they appear. Over the last 20 years, more and more manufacturing operations have embraced machine vision systems to increase productivity, reliability and cost-effectiveness, including reducing the number of human operators required. Although machine vision technology has long been capable of solving simple problems, it has still not been broadly implemented. The reason is that until now, no machine vision system has been designed to meet the unique demands of complicated pattern recognition. The ZiCAM family was specifically developed to be the first practical hardware to meet these needs. To be able to address non-traditional applications, the machine vision industry must include smart camera technology that meets its users" demands for lower costs, better performance and the ability to address applications of irregular lighting, patterns and color. The next-generation smart cameras will need to evolve as a fundamentally different kind of sensor, with new technology that behaves like a human but performs like a computer. Neural network based systems, coupled with self-taught, n-space, non-linear modeling, promises to be the enabler of the next generation of machine vision equipment. Image processing technology is now available that enables a system to match an operator"s subjectivity. A Zero-Instruction-Set-Computer (ZISC) powered smart camera allows high-speed fuzzy-logic processing, without the need for computer programming. This can address applications of validating highly variable and pseudo-random patterns. A hardware-based implementation of a neural network, Zero-Instruction-Set-Computer, enables a vision system to "think" and "inspect" like a human, with the speed and reliability of a machine.

Knowledge-based machine vision systems for space station automation

NASA Technical Reports Server (NTRS)

Ranganath, Heggere S.; Chipman, Laure J.

1989-01-01

Computer vision techniques which have the potential for use on the space station and related applications are assessed. A knowledge-based vision system (expert vision system) and the development of a demonstration system for it are described. This system implements some of the capabilities that would be necessary in a machine vision system for the robot arm of the laboratory module in the space station. A Perceptics 9200e image processor, on a host VAXstation, was used to develop the demonstration system. In order to use realistic test images, photographs of actual space shuttle simulator panels were used. The system's capabilities of scene identification and scene matching are discussed.

Fingerprint recognition of alien invasive weeds based on the texture character and machine learning

NASA Astrophysics Data System (ADS)

Yu, Jia-Jia; Li, Xiao-Li; He, Yong; Xu, Zheng-Hao

2008-11-01

Multi-spectral imaging technique based on texture analysis and machine learning was proposed to discriminate alien invasive weeds with similar outline but different categories. The objectives of this study were to investigate the feasibility of using Multi-spectral imaging, especially the near-infrared (NIR) channel (800 nm+/-10 nm) to find the weeds' fingerprints, and validate the performance with specific eigenvalues by co-occurrence matrix. Veronica polita Pries, Veronica persica Poir, longtube ground ivy, Laminum amplexicaule Linn. were selected in this study, which perform different effect in field, and are alien invasive species in China. 307 weed leaves' images were randomly selected for the calibration set, while the remaining 207 samples for the prediction set. All images were pretreated by Wallis filter to adjust the noise by uneven lighting. Gray level co-occurrence matrix was applied to extract the texture character, which shows density, randomness correlation, contrast and homogeneity of texture with different algorithms. Three channels (green channel by 550 nm+/-10 nm, red channel by 650 nm+/-10 nm and NIR channel by 800 nm+/-10 nm) were respectively calculated to get the eigenvalues.Least-squares support vector machines (LS-SVM) was applied to discriminate the categories of weeds by the eigenvalues from co-occurrence matrix. Finally, recognition ratio of 83.35% by NIR channel was obtained, better than the results by green channel (76.67%) and red channel (69.46%). The prediction results of 81.35% indicated that the selected eigenvalues reflected the main characteristics of weeds' fingerprint based on multi-spectral (especially by NIR channel) and LS-SVM model.

Operator-coached machine vision for space telerobotics

NASA Technical Reports Server (NTRS)

Bon, Bruce; Wilcox, Brian; Litwin, Todd; Gennery, Donald B.

1991-01-01

A prototype system for interactive object modeling has been developed and tested. The goal of this effort has been to create a system which would demonstrate the feasibility of high interactive operator-coached machine vision in a realistic task environment, and to provide a testbed for experimentation with various modes of operator interaction. The purpose for such a system is to use human perception where machine vision is difficult, i.e., to segment the scene into objects and to designate their features, and to use machine vision to overcome limitations of human perception, i.e., for accurate measurement of object geometry. The system captures and displays video images from a number of cameras, allows the operator to designate a polyhedral object one edge at a time by moving a 3-D cursor within these images, performs a least-squares fit of the designated edges to edge data detected with a modified Sobel operator, and combines the edges thus detected to form a wire-frame object model that matches the Sobel data.

Machine vision and appearance based learning

NASA Astrophysics Data System (ADS)

Bernstein, Alexander

2017-03-01

Smart algorithms are used in Machine vision to organize or extract high-level information from the available data. The resulted high-level understanding the content of images received from certain visual sensing system and belonged to an appearance space can be only a key first step in solving various specific tasks such as mobile robot navigation in uncertain environments, road detection in autonomous driving systems, etc. Appearance-based learning has become very popular in the field of machine vision. In general, the appearance of a scene is a function of the scene content, the lighting conditions, and the camera position. Mobile robots localization problem in machine learning framework via appearance space analysis is considered. This problem is reduced to certain regression on an appearance manifold problem, and newly regression on manifolds methods are used for its solution.

Multispectral image fusion for illumination-invariant palmprint recognition

PubMed Central

Zhang, Xinman; Xu, Xuebin; Shang, Dongpeng

2017-01-01

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied. PMID:28558064

Multispectral image fusion for illumination-invariant palmprint recognition.

PubMed

Lu, Longbin; Zhang, Xinman; Xu, Xuebin; Shang, Dongpeng

2017-01-01

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied.

Investigation into the use of smartphone as a machine vision device for engineering metrology and flaw detection, with focus on drilling

NASA Astrophysics Data System (ADS)

Razdan, Vikram; Bateman, Richard

2015-05-01

This study investigates the use of a Smartphone and its camera vision capabilities in Engineering metrology and flaw detection, with a view to develop a low cost alternative to Machine vision systems which are out of range for small scale manufacturers. A Smartphone has to provide a similar level of accuracy as Machine Vision devices like Smart cameras. The objective set out was to develop an App on an Android Smartphone, incorporating advanced Computer vision algorithms written in java code. The App could then be used for recording measurements of Twist Drill bits and hole geometry, and analysing the results for accuracy. A detailed literature review was carried out for in-depth study of Machine vision systems and their capabilities, including a comparison between the HTC One X Android Smartphone and the Teledyne Dalsa BOA Smart camera. A review of the existing metrology Apps in the market was also undertaken. In addition, the drilling operation was evaluated to establish key measurement parameters of a twist Drill bit, especially flank wear and diameter. The methodology covers software development of the Android App, including the use of image processing algorithms like Gaussian Blur, Sobel and Canny available from OpenCV software library, as well as designing and developing the experimental set-up for carrying out the measurements. The results obtained from the experimental set-up were analysed for geometry of Twist Drill bits and holes, including diametrical measurements and flaw detection. The results show that Smartphones like the HTC One X have the processing power and the camera capability to carry out metrological tasks, although dimensional accuracy achievable from the Smartphone App is below the level provided by Machine vision devices like Smart cameras. A Smartphone with mechanical attachments, capable of image processing and having a reasonable level of accuracy in dimensional measurement, has the potential to become a handy low-cost Machine vision system for small scale manufacturers, especially in field metrology and flaw detection.

The Burn Medical Assistant: Developing Machine Learning Algorithms to Aid in the Estimation of Burn Wound Size

DTIC Science & Technology

2017-10-01

hypothesis that a computer machine learning algorithm can analyze and classify burn injures using multispectral imaging within 5% of an expert clinician...morbidity. In response to these challenges, the USAISR developed and obtained FDA 510(k) clearance of the Burn Navigator™, a computer decision support... computer decision support software (CDSS), can significantly change the CDSS algorithm’s recommendations and thus the total fluid administered to a

SAD-Based Stereo Vision Machine on a System-on-Programmable-Chip (SoPC)

PubMed Central

Zhang, Xiang; Chen, Zhangwei

2013-01-01

This paper, proposes a novel solution for a stereo vision machine based on the System-on-Programmable-Chip (SoPC) architecture. The SOPC technology provides great convenience for accessing many hardware devices such as DDRII, SSRAM, Flash, etc., by IP reuse. The system hardware is implemented in a single FPGA chip involving a 32-bit Nios II microprocessor, which is a configurable soft IP core in charge of managing the image buffer and users' configuration data. The Sum of Absolute Differences (SAD) algorithm is used for dense disparity map computation. The circuits of the algorithmic module are modeled by the Matlab-based DSP Builder. With a set of configuration interfaces, the machine can process many different sizes of stereo pair images. The maximum image size is up to 512 K pixels. This machine is designed to focus on real time stereo vision applications. The stereo vision machine offers good performance and high efficiency in real time. Considering a hardware FPGA clock of 90 MHz, 23 frames of 640 × 480 disparity maps can be obtained in one second with 5 × 5 matching window and maximum 64 disparity pixels. PMID:23459385

75 FR 60478 - In the Matter of Certain Machine Vision Software, Machine Vision Systems, and Products Containing...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-30

... Automation, Inc. (``Amistar'') of San Marcos, California; Techno Soft Systemnics, Inc. (``Techno Soft'') of... the claim terms ``test,'' ``match score surface,'' and ``gradient direction,'' all of his infringement... complainants' proposed construction for the claim terms ``test,'' ``match score surface,'' and ``gradient...

Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields.

PubMed

Artan, Yusuf; Haider, Masoom A; Langer, Deanna L; van der Kwast, Theodorus H; Evans, Andrew J; Yang, Yongyi; Wernick, Miles N; Trachtenberg, John; Yetik, Imam Samil

2010-09-01

Prostate cancer is a leading cause of cancer death for men in the United States. Fortunately, the survival rate for early diagnosed patients is relatively high. Therefore, in vivo imaging plays an important role for the detection and treatment of the disease. Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotherapy, and surgery as well as to monitor disease progression. Magnetic resonance imaging (MRI) performed with an endorectal coil provides higher prostate cancer localization accuracy, when compared to transrectal ultrasound (TRUS). However, in general, a single type of MRI is not sufficient for reliable tumor localization. As an alternative, multispectral MRI, i.e., the use of multiple MRI-derived datasets, has emerged as a promising noninvasive imaging technique for the localization of prostate cancer; however almost all studies are with human readers. There is a significant inter and intraobserver variability for human readers, and it is substantially difficult for humans to analyze the large dataset of multispectral MRI. To solve these problems, this study presents an automated localization method using cost-sensitive support vector machines (SVMs) and shows that this method results in improved localization accuracy than classical SVM. Additionally, we develop a new segmentation method by combining conditional random fields (CRF) with a cost-sensitive framework and show that our method further improves cost-sensitive SVM results by incorporating spatial information. We test SVM, cost-sensitive SVM, and the proposed cost-sensitive CRF on multispectral MRI datasets acquired from 21 biopsy-confirmed cancer patients. Our results show that multispectral MRI helps to increase the accuracy of prostate cancer localization when compared to single MR images; and that using advanced methods such as cost-sensitive SVM as well as the proposed cost-sensitive CRF can boost the performance significantly when compared to SVM.

Corn blight watch experiment results

NASA Technical Reports Server (NTRS)

Johannsen, C. J.; Bauer, M. E.

1972-01-01

Results pertaining to the detection and assessment of the severity and extent of southern corn leaf blight in the Corn Belt area are discussed. Ground observations, interpretation of color infrared photography, and machine analysis of multispectral scanner data were used to identify the blight.

The precision measurement and assembly for miniature parts based on double machine vision systems

NASA Astrophysics Data System (ADS)

Wang, X. D.; Zhang, L. F.; Xin, M. Z.; Qu, Y. Q.; Luo, Y.; Ma, T. M.; Chen, L.

2015-02-01

In the process of miniature parts' assembly, the structural features on the bottom or side of the parts often need to be aligned and positioned. The general assembly equipment integrated with one vertical downward machine vision system cannot satisfy the requirement. A precision automatic assembly equipment was developed with double machine vision systems integrated. In the system, a horizontal vision system is employed to measure the position of the feature structure at the parts' side view, which cannot be seen with the vertical one. The position measured by horizontal camera is converted to the vertical vision system with the calibration information. By careful calibration, the parts' alignment and positioning in the assembly process can be guaranteed. The developed assembly equipment has the characteristics of easy implementation, modularization and high cost performance. The handling of the miniature parts and assembly procedure were briefly introduced. The calibration procedure was given and the assembly error was analyzed for compensation.

Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images.

PubMed

Alexandridis, Thomas K; Tamouridou, Afroditi Alexandra; Pantazi, Xanthoula Eirini; Lagopodi, Anastasia L; Kashefi, Javid; Ovakoglou, Georgios; Polychronos, Vassilios; Moshou, Dimitrios

2017-09-01

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

Snapshot spectral and polarimetric imaging; target identification with multispectral video

NASA Astrophysics Data System (ADS)

Bartlett, Brent D.; Rodriguez, Mikel D.

2013-05-01

As the number of pixels continue to grow in consumer and scientific imaging devices, it has become feasible to collect the incident light field. In this paper, an imaging device developed around light field imaging is used to collect multispectral and polarimetric imagery in a snapshot fashion. The sensor is described and a video data set is shown highlighting the advantage of snapshot spectral imaging. Several novel computer vision approaches are applied to the video cubes to perform scene characterization and target identification. It is shown how the addition of spectral and polarimetric data to the video stream allows for multi-target identification and tracking not possible with traditional RGB video collection.

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

PubMed

Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-08-04

Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds. ©Jose Juan Dominguez Veiga, Martin O'Reilly, Darragh Whelan, Brian Caulfield, Tomas E Ward. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.08.2017.

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation

PubMed Central

O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-01-01

Background Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. Objective The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. Methods We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. Results With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. Conclusions The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds. PMID:28778851

Adaptive illumination source for multispectral vision system applied to material discrimination

NASA Astrophysics Data System (ADS)

Conde, Olga M.; Cobo, Adolfo; Cantero, Paulino; Conde, David; Mirapeix, Jesús; Cubillas, Ana M.; López-Higuera, José M.

2008-04-01

A multispectral system based on a monochrome camera and an adaptive illumination source is presented in this paper. Its preliminary application is focused on material discrimination for food and beverage industries, where monochrome, color and infrared imaging have been successfully applied for this task. This work proposes a different approach, in which the relevant wavelengths for the required discrimination task are selected in advance using a Sequential Forward Floating Selection (SFFS) Algorithm. A light source, based on Light Emitting Diodes (LEDs) at these wavelengths is then used to sequentially illuminate the material under analysis, and the resulting images are captured by a CCD camera with spectral response in the entire range of the selected wavelengths. Finally, the several multispectral planes obtained are processed using a Spectral Angle Mapping (SAM) algorithm, whose output is the desired material classification. Among other advantages, this approach of controlled and specific illumination produces multispectral imaging with a simple monochrome camera, and cold illumination restricted to specific relevant wavelengths, which is desirable for the food and beverage industry. The proposed system has been tested with success for the automatic detection of foreign object in the tobacco processing industry.

Development of a model of machine hand eye coordination and program specifications for a topological machine vision system

NASA Technical Reports Server (NTRS)

1972-01-01

A unified approach to computer vision and manipulation is developed which is called choreographic vision. In the model, objects to be viewed by a projected robot in the Viking missions to Mars are seen as objects to be manipulated within choreographic contexts controlled by a multimoded remote, supervisory control system on Earth. A new theory of context relations is introduced as a basis for choreographic programming languages. A topological vision model is developed for recognizing objects by shape and contour. This model is integrated with a projected vision system consisting of a multiaperture image dissector TV camera and a ranging laser system. System program specifications integrate eye-hand coordination and topological vision functions and an aerospace multiprocessor implementation is described.

Robot path planning using expert systems and machine vision

NASA Astrophysics Data System (ADS)

Malone, Denis E.; Friedrich, Werner E.

1992-02-01

This paper describes a system developed for the robotic processing of naturally variable products. In order to plan the robot motion path it was necessary to use a sensor system, in this case a machine vision system, to observe the variations occurring in workpieces and interpret this with a knowledge based expert system. The knowledge base was acquired by carrying out an in-depth study of the product using examination procedures not available in the robotic workplace and relates the nature of the required path to the information obtainable from the machine vision system. The practical application of this system to the processing of fish fillets is described and used to illustrate the techniques.

Machine processing of S-192 and supporting aircraft data: Studies of atmospheric effects, agricultural classifications, and land resource mapping

NASA Technical Reports Server (NTRS)

Thomson, F.

1975-01-01

Two tasks of machine processing of S-192 multispectral scanner data are reviewed. In the first task, the effects of changing atmospheric and base altitude on the ability to machine-classify agricultural crops were investigated. A classifier and atmospheric effects simulation model was devised and its accuracy verified by comparison of its predicted results with S-192 processed results. In the second task, land resource maps of a mountainous area near Cripple Creek, Colorado were prepared from S-192 data collected on 4 August 1973.

User requirements for project-oriented remote sensing

NASA Technical Reports Server (NTRS)

Hitchcock, H. C.; Baxter, F. P.; Cox, T. L.

1975-01-01

Registration of remotely sensed data to geodetic coordinates provides for overlay analysis of land use data. For aerial photographs of a large area, differences in scales, dates, and film types are reconciled, and multispectral scanner data are machine registered at the time of acquisition.

Comparison of the MPP with other supercomputers for LANDSAT data processing

NASA Technical Reports Server (NTRS)

Ozga, Martin

1987-01-01

The massively parallel processor is compared to the CRAY X-MP and the CYBER-205 for LANDSAT data processing. The maximum likelihood classification algorithm is the basis for comparison since this algorithm is simple to implement and vectorizes very well. The algorithm was implemented on all three machines and tested by classifying the same full scene of LANDSAT multispectral scan data. Timings are compared as well as features of the machines and available software.

Lumber Scanning System for Surface Defect Detection

Treesearch

D. Earl Kline; Y. Jason Hou; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman

1992-01-01

This paper describes research aimed at developing a machine vision technology to drive automated processes in the hardwood forest products manufacturing industry. An industrial-scale machine vision system has been designed to scan variable-size hardwood lumber for detecting important features that influence the grade and value of lumber such as knots, holes, wane,...

Liquid lens: advances in adaptive optics

NASA Astrophysics Data System (ADS)

Casey, Shawn Patrick

2010-12-01

'Liquid lens' technologies promise significant advancements in machine vision and optical communications systems. Adaptations for machine vision, human vision correction, and optical communications are used to exemplify the versatile nature of this technology. Utilization of liquid lens elements allows the cost effective implementation of optical velocity measurement. The project consists of a custom image processor, camera, and interface. The images are passed into customized pattern recognition and optical character recognition algorithms. A single camera would be used for both speed detection and object recognition.

Comparative performance between compressed and uncompressed airborne imagery

NASA Astrophysics Data System (ADS)

Phan, Chung; Rupp, Ronald; Agarwal, Sanjeev; Trang, Anh; Nair, Sumesh

2008-04-01

The US Army's RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD), Countermine Division is evaluating the compressibility of airborne multi-spectral imagery for mine and minefield detection application. Of particular interest is to assess the highest image data compression rate that can be afforded without the loss of image quality for war fighters in the loop and performance of near real time mine detection algorithm. The JPEG-2000 compression standard is used to perform data compression. Both lossless and lossy compressions are considered. A multi-spectral anomaly detector such as RX (Reed & Xiaoli), which is widely used as a core algorithm baseline in airborne mine and minefield detection on different mine types, minefields, and terrains to identify potential individual targets, is used to compare the mine detection performance. This paper presents the compression scheme and compares detection performance results between compressed and uncompressed imagery for various level of compressions. The compression efficiency is evaluated and its dependence upon different backgrounds and other factors are documented and presented using multi-spectral data.

Characteristics of the Arcing Plasma Formation Effect in Spark-Assisted Chemical Engraving of Glass, Based on Machine Vision

PubMed Central

Wu, Dung-Sheng

2018-01-01

Spark-assisted chemical engraving (SACE) is a non-traditional machining technology that is used to machine electrically non-conducting materials including glass, ceramics, and quartz. The processing accuracy, machining efficiency, and reproducibility are the key factors in the SACE process. In the present study, a machine vision method is applied to monitor and estimate the status of a SACE-drilled hole in quartz glass. During the machining of quartz glass, the spring-fed tool electrode was pre-pressured on the quartz glass surface to feed the electrode that was in contact with the machining surface of the quartz glass. In situ image acquisition and analysis of the SACE drilling processes were used to analyze the captured image of the state of the spark discharge at the tip and sidewall of the electrode. The results indicated an association between the accumulative size of the SACE-induced spark area and deepness of the hole. The results indicated that the evaluated depths of the SACE-machined holes were a proportional function of the accumulative spark size with a high degree of correlation. The study proposes an innovative computer vision-based method to estimate the deepness and status of SACE-drilled holes in real time. PMID:29565303

Characteristics of the Arcing Plasma Formation Effect in Spark-Assisted Chemical Engraving of Glass, Based on Machine Vision.

PubMed

Ho, Chao-Ching; Wu, Dung-Sheng

2018-03-22

Spark-assisted chemical engraving (SACE) is a non-traditional machining technology that is used to machine electrically non-conducting materials including glass, ceramics, and quartz. The processing accuracy, machining efficiency, and reproducibility are the key factors in the SACE process. In the present study, a machine vision method is applied to monitor and estimate the status of a SACE-drilled hole in quartz glass. During the machining of quartz glass, the spring-fed tool electrode was pre-pressured on the quartz glass surface to feed the electrode that was in contact with the machining surface of the quartz glass. In situ image acquisition and analysis of the SACE drilling processes were used to analyze the captured image of the state of the spark discharge at the tip and sidewall of the electrode. The results indicated an association between the accumulative size of the SACE-induced spark area and deepness of the hole. The results indicated that the evaluated depths of the SACE-machined holes were a proportional function of the accumulative spark size with a high degree of correlation. The study proposes an innovative computer vision-based method to estimate the deepness and status of SACE-drilled holes in real time.

The use of holographic and diffractive optics for optimized machine vision illumination for critical dimension inspection

NASA Astrophysics Data System (ADS)

Lizotte, Todd E.; Ohar, Orest

2004-02-01

Illuminators used in machine vision applications typically produce non-uniform illumination onto the targeted surface being observed, causing a variety of problems with machine vision alignment or measurement. In most circumstances the light source is broad spectrum, leading to further problems with image quality when viewed through a CCD camera. Configured with a simple light bulb and a mirrored reflector and/or frosted glass plates, these general illuminators are appropriate for only macro applications. Over the last 5 years newer illuminators have hit the market including circular or rectangular arrays of high intensity light emitting diodes. These diode arrays are used to create monochromatic flood illumination of a surface that is to be inspected. The problem with these illumination techniques is that most of the light does not illuminate the desired areas, but broadly spreads across the surface, or when integrated with diffuser elements, tend to create similar shadowing effects to the broad spectrum light sources. In many cases a user will try to increase the performance of these illuminators by adding several of these assemblies together, increasing the intensity or by moving the illumination source closer or farther from the surface being inspected. In this case these non-uniform techniques can lead to machine vision errors, where the computer machine vision may read false information, such as interpreting non-uniform lighting or shadowing effects as defects. This paper will cover a technique involving the use of holographic / diffractive hybrid optical elements that are integrated into standard and customized light sources used in the machine vision industry. The bulk of the paper will describe the function and fabrication of the holographic/diffractive optics and how they can be tailored to improve illuminator design. Further information will be provided a specific design and examples of it in operation will be disclosed.

The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia.

PubMed

Diamond, James; Anderson, Neil H; Bartels, Peter H; Montironi, Rodolfo; Hamilton, Peter W

2004-09-01

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at x 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 x 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.

Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit.

PubMed

Liu, Changhong; Liu, Wei; Lu, Xuzhong; Ma, Fei; Chen, Wei; Yang, Jianbo; Zheng, Lei

2014-01-01

Multispectral imaging with 19 wavelengths in the range of 405-970 nm has been evaluated for nondestructive determination of firmness, total soluble solids (TSS) content and ripeness stage in strawberry fruit. Several analysis approaches, including partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN), were applied to develop theoretical models for predicting the firmness and TSS of intact strawberry fruit. Compared with PLS and SVM, BPNN considerably improved the performance of multispectral imaging for predicting firmness and total soluble solids content with the correlation coefficient (r) of 0.94 and 0.83, SEP of 0.375 and 0.573, and bias of 0.035 and 0.056, respectively. Subsequently, the ability of multispectral imaging technology to classify fruit based on ripeness stage was tested using SVM and principal component analysis-back propagation neural network (PCA-BPNN) models. The higher classification accuracy of 100% was achieved using SVM model. Moreover, the results of all these models demonstrated that the VIS parts of the spectra were the main contributor to the determination of firmness, TSS content estimation and classification of ripeness stage in strawberry fruit. These results suggest that multispectral imaging, together with suitable analysis model, is a promising technology for rapid estimation of quality attributes and classification of ripeness stage in strawberry fruit.

Visible and Extended Near-Infrared Multispectral Imaging for Skin Cancer Diagnosis

PubMed Central

Rey-Barroso, Laura; Burgos-Fernández, Francisco J.; Delpueyo, Xana; Ares, Miguel; Malvehy, Josep; Puig, Susana

2018-01-01

With the goal of diagnosing skin cancer in an early and noninvasive way, an extended near infrared multispectral imaging system based on an InGaAs sensor with sensitivity from 995 nm to 1613 nm was built to evaluate deeper skin layers thanks to the higher penetration of photons at these wavelengths. The outcomes of this device were combined with those of a previously developed multispectral system that works in the visible and near infrared range (414 nm–995 nm). Both provide spectral and spatial information from skin lesions. A classification method to discriminate between melanomas and nevi was developed based on the analysis of first-order statistics descriptors, principal component analysis, and support vector machine tools. The system provided a sensitivity of 78.6% and a specificity of 84.6%, the latter one being improved with respect to that offered by silicon sensors. PMID:29734747

Implementation of Multispectral Image Classification on a Remote Adaptive Computer

NASA Technical Reports Server (NTRS)

Figueiredo, Marco A.; Gloster, Clay S.; Stephens, Mark; Graves, Corey A.; Nakkar, Mouna

1999-01-01

As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms its justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of m a,gnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application, that, can benefit from implementation on an FPGA - based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm implemented on a typical general - purpose computer).

Giving Machines the Vision

NASA Technical Reports Server (NTRS)

1999-01-01

Amherst Systems manufactures foveal machine vision technology and systems commercially available to end-users and system integrators. This technology was initially developed under NASA contracts NAS9-19335 (Johnson Space Center) and NAS1-20841 (Langley Research Center). This technology is currently being delivered to university research facilities and military sites. More information may be found in www.amherst.com.

Machine vision for various manipulation tasks

NASA Astrophysics Data System (ADS)

Domae, Yukiyasu

2017-03-01

Bin-picking, re-grasping, pick-and-place, kitting, etc. There are many manipulation tasks in the fields of automation of factory, warehouse and so on. The main problem of the automation is that the target objects (items/parts) have various shapes, weights and surface materials. In my talk, I will show latest machine vision systems and algorithms against the problem.

A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection

Treesearch

D. Earl Kline; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman; Robert L. Brisbin

1993-01-01

A multiple sensor machine vision prototype is being developed to scan full size hardwood lumber at industrial speeds for automatically detecting features such as knots holes, wane, stain, splits, checks, and color. The prototype integrates a multiple sensor imaging system, a materials handling system, a computer system, and application software. The prototype provides...

Machine Vision Technology for the Forest Products Industry

Treesearch

Richard W. Conners; D.Earl Kline; Philip A. Araman; Thomas T. Drayer

1997-01-01

From forest to finished product, wood is moved from one processing stage to the next, subject to the decisions of individuals along the way. While this process has worked for hundreds of years, the technology exists today to provide more complete information to the decision makers. Virginia Tech has developed this technology, creating a machine vision prototype for...

Receptive fields and the theory of discriminant operators

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Hungenahally, Suresh K.

1991-02-01

Biological basis for machine vision is a notion which is being used extensively for the development of machine vision systems for various applications. In this paper we have made an attempt to emulate the receptive fields that exist in the biological visual channels. In particular we have exploited the notion of receptive fields for developing the mathematical functions named as discriminantfunctions for the extraction of transition information from signals and multi-dimensional signals and images. These functions are found to be useful for the development of artificial receptive fields for neuro-vision systems. 1.

Deep Learning for Computer Vision: A Brief Review

PubMed Central

Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

2018-01-01

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein. PMID:29487619

Progress in computer vision.

NASA Astrophysics Data System (ADS)

Jain, A. K.; Dorai, C.

Computer vision has emerged as a challenging and important area of research, both as an engineering and a scientific discipline. The growing importance of computer vision is evident from the fact that it was identified as one of the "Grand Challenges" and also from its prominent role in the National Information Infrastructure. While the design of a general-purpose vision system continues to be elusive machine vision systems are being used successfully in specific application elusive, machine vision systems are being used successfully in specific application domains. Building a practical vision system requires a careful selection of appropriate sensors, extraction and integration of information from available cues in the sensed data, and evaluation of system robustness and performance. The authors discuss and demonstrate advantages of (1) multi-sensor fusion, (2) combination of features and classifiers, (3) integration of visual modules, and (IV) admissibility and goal-directed evaluation of vision algorithms. The requirements of several prominent real world applications such as biometry, document image analysis, image and video database retrieval, and automatic object model construction offer exciting problems and new opportunities to design and evaluate vision algorithms.

Vision Systems with the Human in the Loop

NASA Astrophysics Data System (ADS)

Bauckhage, Christian; Hanheide, Marc; Wrede, Sebastian; Käster, Thomas; Pfeiffer, Michael; Sagerer, Gerhard

2005-12-01

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed.

Quality Control by Artificial Vision

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

Lam, Edmond Y.; Gleason, Shaun Scott; Niel, Kurt S.

2010-01-01

Computational technology has fundamentally changed many aspects of our lives. One clear evidence is the development of artificial-vision systems, which have effectively automated many manual tasks ranging from quality inspection to quantitative assessment. In many cases, these machine-vision systems are even preferred over manual ones due to their repeatability and high precision. Such advantages come from significant research efforts in advancing sensor technology, illumination, computational hardware, and image-processing algorithms. Similar to the Special Section on Quality Control by Artificial Vision published two years ago in Volume 17, Issue 3 of the Journal of Electronic Imaging, the present one invited papersmore » relevant to fundamental technology improvements to foster quality control by artificial vision, and fine-tuned the technology for specific applications. We aim to balance both theoretical and applied work pertinent to this special section theme. Consequently, we have seven high-quality papers resulting from the stringent peer-reviewing process in place at the Journal of Electronic Imaging. Some of the papers contain extended treatment of the authors work presented at the SPIE Image Processing: Machine Vision Applications conference and the International Conference on Quality Control by Artificial Vision. On the broad application side, Liu et al. propose an unsupervised texture image segmentation scheme. Using a multilayer data condensation spectral clustering algorithm together with wavelet transform, they demonstrate the effectiveness of their approach on both texture and synthetic aperture radar images. A problem related to image segmentation is image extraction. For this, O'Leary et al. investigate the theory of polynomial moments and show how these moments can be compared to classical filters. They also show how to use the discrete polynomial-basis functions for the extraction of 3-D embossed digits, demonstrating superiority over Fourier-basis functions for this task. Image registration is another important task for machine vision. Bingham and Arrowood investigate the implementation and results in applying Fourier phase matching for projection registration, with a particular focus on nondestructive testing using computed tomography. Readers interested in enriching their arsenal of image-processing algorithms for machine-vision tasks should find these papers enriching. Meanwhile, we have four papers dealing with more specific machine-vision tasks. The first one, Yahiaoui et al., is quantitative in nature, using machine vision for real-time passenger counting. Occulsion is a common problem in counting objects and people, and they circumvent this issue with a dense stereovision system, achieving 97 to 99% accuracy in their tests. On the other hand, the second paper by Oswald-Tranta et al. focuses on thermographic crack detection. An infrared camera is used to detect inhomogeneities, which may indicate surface cracks. They describe the various steps in developing fully automated testing equipment aimed at a high throughput. Another paper describing an inspection system is Molleda et al., which handles flatness inspection of rolled products. They employ optical-laser triangulation and 3-D surface reconstruction for this task, showing how these can be achieved in real time. Last but not least, Presles et al. propose a way to monitor the particle-size distribution of batch crystallization processes. This is achieved through a new in situ imaging probe and image-analysis methods. While it is unlikely any reader may be working on these four specific problems at the same time, we are confident that readers will find these papers inspiring and potentially helpful to their own machine-vision system developments.« less

Multi-Image Registration for an Enhanced Vision System

NASA Technical Reports Server (NTRS)

Hines, Glenn; Rahman, Zia-Ur; Jobson, Daniel; Woodell, Glenn

2002-01-01

An Enhanced Vision System (EVS) utilizing multi-sensor image fusion is currently under development at the NASA Langley Research Center. The EVS will provide enhanced images of the flight environment to assist pilots in poor visibility conditions. Multi-spectral images obtained from a short wave infrared (SWIR), a long wave infrared (LWIR), and a color visible band CCD camera, are enhanced and fused using the Retinex algorithm. The images from the different sensors do not have a uniform data structure: the three sensors not only operate at different wavelengths, but they also have different spatial resolutions, optical fields of view (FOV), and bore-sighting inaccuracies. Thus, in order to perform image fusion, the images must first be co-registered. Image registration is the task of aligning images taken at different times, from different sensors, or from different viewpoints, so that all corresponding points in the images match. In this paper, we present two methods for registering multiple multi-spectral images. The first method performs registration using sensor specifications to match the FOVs and resolutions directly through image resampling. In the second method, registration is obtained through geometric correction based on a spatial transformation defined by user selected control points and regression analysis.

Reflections on the Development of a Machine Vision Technology for the Forest Products

Treesearch

Richard W. Conners; D.Earl Kline; Philip A. Araman; Robert L. Brisbon

1992-01-01

The authors have approximately 25 years experience in developing machine vision technology for the forest products industry. Based on this experience this paper will attempt to realistically predict what the future holds for this technology. In particular, this paper will attempt to describe some of the benefits this technology will offer, describe how the technology...

Development of a cost-effective machine vision system for in-field sorting and grading of apples: Fruit orientation and size estimation

USDA-ARS?s Scientific Manuscript database

The objective of this research was to develop an in-field apple presorting and grading system to separate undersized and defective fruit from fresh market-grade apples. To achieve this goal, a cost-effective machine vision inspection prototype was built, which consisted of a low-cost color camera, L...

Color machine vision in industrial process control: case limestone mine

NASA Astrophysics Data System (ADS)

Paernaenen, Pekka H. T.; Lemstrom, Guy F.; Koskinen, Seppo

1994-11-01

An optical sorter technology has been developed to improve profitability of a mine by using color line scan machine vision technology. The new technology adapted longers the expected life time of the limestone mine and improves its efficiency. Also the project has proved that color line scan technology of today can successfully be applied to industrial use in harsh environments.

LED light design method for high contrast and uniform illumination imaging in machine vision.

PubMed

Wu, Xiaojun; Gao, Guangming

2018-03-01

In machine vision, illumination is very critical to determine the complexity of the inspection algorithms. Proper lights can obtain clear and sharp images with the highest contrast and low noise between the interested object and the background, which is conducive to the target being located, measured, or inspected. Contrary to the empirically based trial-and-error convention to select the off-the-shelf LED light in machine vision, an optimization algorithm for LED light design is proposed in this paper. It is composed of the contrast optimization modeling and the uniform illumination technology for non-normal incidence (UINI). The contrast optimization model is built based on the surface reflection characteristics, e.g., the roughness, the reflective index, and light direction, etc., to maximize the contrast between the features of interest and the background. The UINI can keep the uniformity of the optimized lighting by the contrast optimization model. The simulation and experimental results demonstrate that the optimization algorithm is effective and suitable to produce images with the highest contrast and uniformity, which is very inspirational to the design of LED illumination systems in machine vision.

Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture

NASA Astrophysics Data System (ADS)

Elarab, Manal; Ticlavilca, Andres M.; Torres-Rua, Alfonso F.; Maslova, Inga; McKee, Mac

2015-12-01

Precision agriculture requires high-resolution information to enable greater precision in the management of inputs to production. Actionable information about crop and field status must be acquired at high spatial resolution and at a temporal frequency appropriate for timely responses. In this study, high spatial resolution imagery was obtained through the use of a small, unmanned aerial system called AggieAirTM. Simultaneously with the AggieAir flights, intensive ground sampling for plant chlorophyll was conducted at precisely determined locations. This study reports the application of a relevance vector machine coupled with cross validation and backward elimination to a dataset composed of reflectance from high-resolution multi-spectral imagery (VIS-NIR), thermal infrared imagery, and vegetative indices, in conjunction with in situ SPAD measurements from which chlorophyll concentrations were derived, to estimate chlorophyll concentration from remotely sensed data at 15-cm resolution. The results indicate that a relevance vector machine with a thin plate spline kernel type and kernel width of 5.4, having LAI, NDVI, thermal and red bands as the selected set of inputs, can be used to spatially estimate chlorophyll concentration with a root-mean-squared-error of 5.31 μg cm-2, efficiency of 0.76, and 9 relevance vectors.

Neo-Symbiosis: The Next Stage in the Evolution of Human Information Interaction.

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

Griffith, Douglas; Greitzer, Frank L.

In his 1960 paper Man-Machine Symbiosis, Licklider predicted that human brains and computing machines will be coupled in a tight partnership that will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today. Today we are on the threshold of resurrecting the vision of symbiosis. While Licklider’s original vision suggested a co-equal relationship, here we discuss an updated vision, neo-symbiosis, in which the human holds a superordinate position in an intelligent human-computer collaborative environment. This paper was originally published as a journal article and is being publishedmore » as a chapter in an upcoming book series, Advances in Novel Approaches in Cognitive Informatics and Natural Intelligence.« less

Pyramidal neurovision architecture for vision machines

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Knopf, George K.

1993-08-01

The vision system employed by an intelligent robot must be active; active in the sense that it must be capable of selectively acquiring the minimal amount of relevant information for a given task. An efficient active vision system architecture that is based loosely upon the parallel-hierarchical (pyramidal) structure of the biological visual pathway is presented in this paper. Although the computational architecture of the proposed pyramidal neuro-vision system is far less sophisticated than the architecture of the biological visual pathway, it does retain some essential features such as the converging multilayered structure of its biological counterpart. In terms of visual information processing, the neuro-vision system is constructed from a hierarchy of several interactive computational levels, whereupon each level contains one or more nonlinear parallel processors. Computationally efficient vision machines can be developed by utilizing both the parallel and serial information processing techniques within the pyramidal computing architecture. A computer simulation of a pyramidal vision system for active scene surveillance is presented.

Manifold learning in machine vision and robotics

NASA Astrophysics Data System (ADS)

Bernstein, Alexander

2017-02-01

Smart algorithms are used in Machine vision and Robotics to organize or extract high-level information from the available data. Nowadays, Machine learning is an essential and ubiquitous tool to automate extraction patterns or regularities from data (images in Machine vision; camera, laser, and sonar sensors data in Robotics) in order to solve various subject-oriented tasks such as understanding and classification of images content, navigation of mobile autonomous robot in uncertain environments, robot manipulation in medical robotics and computer-assisted surgery, and other. Usually such data have high dimensionality, however, due to various dependencies between their components and constraints caused by physical reasons, all "feasible and usable data" occupy only a very small part in high dimensional "observation space" with smaller intrinsic dimensionality. Generally accepted model of such data is manifold model in accordance with which the data lie on or near an unknown manifold (surface) of lower dimensionality embedded in an ambient high dimensional observation space; real-world high-dimensional data obtained from "natural" sources meet, as a rule, this model. The use of Manifold learning technique in Machine vision and Robotics, which discovers a low-dimensional structure of high dimensional data and results in effective algorithms for solving of a large number of various subject-oriented tasks, is the content of the conference plenary speech some topics of which are in the paper.

Machine Vision Applied to Navigation of Confined Spaces

NASA Technical Reports Server (NTRS)

Briscoe, Jeri M.; Broderick, David J.; Howard, Ricky; Corder, Eric L.

2004-01-01

The reliability of space related assets has been emphasized after the second loss of a Space Shuttle. The intricate nature of the hardware being inspected often requires a complete disassembly to perform a thorough inspection which can be difficult as well as costly. Furthermore, it is imperative that the hardware under inspection not be altered in any other manner than that which is intended. In these cases the use of machine vision can allow for inspection with greater frequency using less intrusive methods. Such systems can provide feedback to guide, not only manually controlled instrumentation, but autonomous robotic platforms as well. This paper serves to detail a method using machine vision to provide such sensing capabilities in a compact package. A single camera is used in conjunction with a projected reference grid to ascertain precise distance measurements. The design of the sensor focuses on the use of conventional components in an unconventional manner with the goal of providing a solution for systems that do not require or cannot accommodate more complex vision systems.

A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems

PubMed Central

Osswald, Marc; Ieng, Sio-Hoi; Benosman, Ryad; Indiveri, Giacomo

2017-01-01

Stereo vision is an important feature that enables machine vision systems to perceive their environment in 3D. While machine vision has spawned a variety of software algorithms to solve the stereo-correspondence problem, their implementation and integration in small, fast, and efficient hardware vision systems remains a difficult challenge. Recent advances made in neuromorphic engineering offer a possible solution to this problem, with the use of a new class of event-based vision sensors and neural processing devices inspired by the organizing principles of the brain. Here we propose a radically novel model that solves the stereo-correspondence problem with a spiking neural network that can be directly implemented with massively parallel, compact, low-latency and low-power neuromorphic engineering devices. We validate the model with experimental results, highlighting features that are in agreement with both computational neuroscience stereo vision theories and experimental findings. We demonstrate its features with a prototype neuromorphic hardware system and provide testable predictions on the role of spike-based representations and temporal dynamics in biological stereo vision processing systems. PMID:28079187

A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems.

PubMed

Osswald, Marc; Ieng, Sio-Hoi; Benosman, Ryad; Indiveri, Giacomo

2017-01-12

Stereo vision is an important feature that enables machine vision systems to perceive their environment in 3D. While machine vision has spawned a variety of software algorithms to solve the stereo-correspondence problem, their implementation and integration in small, fast, and efficient hardware vision systems remains a difficult challenge. Recent advances made in neuromorphic engineering offer a possible solution to this problem, with the use of a new class of event-based vision sensors and neural processing devices inspired by the organizing principles of the brain. Here we propose a radically novel model that solves the stereo-correspondence problem with a spiking neural network that can be directly implemented with massively parallel, compact, low-latency and low-power neuromorphic engineering devices. We validate the model with experimental results, highlighting features that are in agreement with both computational neuroscience stereo vision theories and experimental findings. We demonstrate its features with a prototype neuromorphic hardware system and provide testable predictions on the role of spike-based representations and temporal dynamics in biological stereo vision processing systems.

A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems

NASA Astrophysics Data System (ADS)

Osswald, Marc; Ieng, Sio-Hoi; Benosman, Ryad; Indiveri, Giacomo

2017-01-01

Stereo vision is an important feature that enables machine vision systems to perceive their environment in 3D. While machine vision has spawned a variety of software algorithms to solve the stereo-correspondence problem, their implementation and integration in small, fast, and efficient hardware vision systems remains a difficult challenge. Recent advances made in neuromorphic engineering offer a possible solution to this problem, with the use of a new class of event-based vision sensors and neural processing devices inspired by the organizing principles of the brain. Here we propose a radically novel model that solves the stereo-correspondence problem with a spiking neural network that can be directly implemented with massively parallel, compact, low-latency and low-power neuromorphic engineering devices. We validate the model with experimental results, highlighting features that are in agreement with both computational neuroscience stereo vision theories and experimental findings. We demonstrate its features with a prototype neuromorphic hardware system and provide testable predictions on the role of spike-based representations and temporal dynamics in biological stereo vision processing systems.

Performance of Color Camera Machine Vision in Automated Furniture Rough Mill Systems

Treesearch

D. Earl Kline; Agus Widoyoko; Janice K. Wiedenbeck; Philip A. Araman

1998-01-01

The objective of this study was to evaluate the performance of color camera machine vision for lumber processing in a furniture rough mill. The study used 134 red oak boards to compare the performance of automated gang-rip-first rough mill yield based on a prototype color camera lumber inspection system developed at Virginia Tech with both estimated optimum rough mill...

Overview of machine vision methods in x-ray imaging and microtomography

NASA Astrophysics Data System (ADS)

Buzmakov, Alexey; Zolotov, Denis; Chukalina, Marina; Nikolaev, Dmitry; Gladkov, Andrey; Ingacheva, Anastasia; Yakimchuk, Ivan; Asadchikov, Victor

2018-04-01

Digital X-ray imaging became widely used in science, medicine, non-destructive testing. This allows using modern digital images analysis for automatic information extraction and interpretation. We give short review of scientific applications of machine vision in scientific X-ray imaging and microtomography, including image processing, feature detection and extraction, images compression to increase camera throughput, microtomography reconstruction, visualization and setup adjustment.

Application of Multispectral Imaging to Determine Quality Attributes and Ripeness Stage in Strawberry Fruit

PubMed Central

Liu, Changhong; Liu, Wei; Lu, Xuzhong; Ma, Fei; Chen, Wei; Yang, Jianbo; Zheng, Lei

2014-01-01

Multispectral imaging with 19 wavelengths in the range of 405–970 nm has been evaluated for nondestructive determination of firmness, total soluble solids (TSS) content and ripeness stage in strawberry fruit. Several analysis approaches, including partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN), were applied to develop theoretical models for predicting the firmness and TSS of intact strawberry fruit. Compared with PLS and SVM, BPNN considerably improved the performance of multispectral imaging for predicting firmness and total soluble solids content with the correlation coefficient (r) of 0.94 and 0.83, SEP of 0.375 and 0.573, and bias of 0.035 and 0.056, respectively. Subsequently, the ability of multispectral imaging technology to classify fruit based on ripeness stage was tested using SVM and principal component analysis-back propagation neural network (PCA-BPNN) models. The higher classification accuracy of 100% was achieved using SVM model. Moreover, the results of all these models demonstrated that the VIS parts of the spectra were the main contributor to the determination of firmness, TSS content estimation and classification of ripeness stage in strawberry fruit. These results suggest that multispectral imaging, together with suitable analysis model, is a promising technology for rapid estimation of quality attributes and classification of ripeness stage in strawberry fruit. PMID:24505317

Software model of a machine vision system based on the common house fly.

PubMed

Madsen, Robert; Barrett, Steven; Wilcox, Michael

2005-01-01

The vision system of the common house fly has many properties, such as hyperacuity and parallel structure, which would be advantageous in a machine vision system. A software model has been developed which is ultimately intended to be a tool to guide the design of an analog real time vision system. The model starts by laying out cartridges over an image. The cartridges are analogous to the ommatidium of the fly's eye and contain seven photoreceptors each with a Gaussian profile. The spacing between photoreceptors is variable providing for more or less detail as needed. The cartridges provide information on what type of features they see and neighboring cartridges share information to construct a feature map.

Component Pin Recognition Using Algorithms Based on Machine Learning

NASA Astrophysics Data System (ADS)

Xiao, Yang; Hu, Hong; Liu, Ze; Xu, Jiangchang

2018-04-01

The purpose of machine vision for a plug-in machine is to improve the machine’s stability and accuracy, and recognition of the component pin is an important part of the vision. This paper focuses on component pin recognition using three different techniques. The first technique involves traditional image processing using the core algorithm for binary large object (BLOB) analysis. The second technique uses the histogram of oriented gradients (HOG), to experimentally compare the effect of the support vector machine (SVM) and the adaptive boosting machine (AdaBoost) learning meta-algorithm classifiers. The third technique is the use of an in-depth learning method known as convolution neural network (CNN), which involves identifying the pin by comparing a sample to its training. The main purpose of the research presented in this paper is to increase the knowledge of learning methods used in the plug-in machine industry in order to achieve better results.

Machine vision based quality inspection of flat glass products

NASA Astrophysics Data System (ADS)

Zauner, G.; Schagerl, M.

2014-03-01

This application paper presents a machine vision solution for the quality inspection of flat glass products. A contact image sensor (CIS) is used to generate digital images of the glass surfaces. The presented machine vision based quality inspection at the end of the production line aims to classify five different glass defect types. The defect images are usually characterized by very little `image structure', i.e. homogeneous regions without distinct image texture. Additionally, these defect images usually consist of only a few pixels. At the same time the appearance of certain defect classes can be very diverse (e.g. water drops). We used simple state-of-the-art image features like histogram-based features (std. deviation, curtosis, skewness), geometric features (form factor/elongation, eccentricity, Hu-moments) and texture features (grey level run length matrix, co-occurrence matrix) to extract defect information. The main contribution of this work now lies in the systematic evaluation of various machine learning algorithms to identify appropriate classification approaches for this specific class of images. In this way, the following machine learning algorithms were compared: decision tree (J48), random forest, JRip rules, naive Bayes, Support Vector Machine (multi class), neural network (multilayer perceptron) and k-Nearest Neighbour. We used a representative image database of 2300 defect images and applied cross validation for evaluation purposes.

Research into the Architecture of CAD Based Robot Vision Systems

DTIC Science & Technology

1988-02-09

Vision 󈨚 and "Automatic Generation of Recognition Features for Com- puter Vision," Mudge, Turney and Volz, published in Robotica (1987). All of the...Occluded Parts," (T.N. Mudge, J.L. Turney, and R.A. Volz), Robotica , vol. 5, 1987, pp. 117-127. 5. "Vision Algorithms for Hypercube Machines," (T.N. Mudge

Fuzzy classification for strawberry diseases-infection using machine vision and soft-computing techniques

NASA Astrophysics Data System (ADS)

Altıparmak, Hamit; Al Shahadat, Mohamad; Kiani, Ehsan; Dimililer, Kamil

2018-04-01

Robotic agriculture requires smart and doable techniques to substitute the human intelligence with machine intelligence. Strawberry is one of the important Mediterranean product and its productivity enhancement requires modern and machine-based methods. Whereas a human identifies the disease infected leaves by his eye, the machine should also be capable of vision-based disease identification. The objective of this paper is to practically verify the applicability of a new computer-vision method for discrimination between the healthy and disease infected strawberry leaves which does not require neural network or time consuming trainings. The proposed method was tested under outdoor lighting condition using a regular DLSR camera without any particular lens. Since the type and infection degree of disease is approximated a human brain a fuzzy decision maker classifies the leaves over the images captured on-site having the same properties of human vision. Optimizing the fuzzy parameters for a typical strawberry production area at a summer mid-day in Cyprus produced 96% accuracy for segmented iron deficiency and 93% accuracy for segmented using a typical human instant classification approximation as the benchmark holding higher accuracy than a human eye identifier. The fuzzy-base classifier provides approximate result for decision making on the leaf status as if it is healthy or not.

A discrepancy within primate spatial vision and its bearing on the definition of edge detection processes in machine vision

NASA Technical Reports Server (NTRS)

Jobson, Daniel J.

1990-01-01

The visual perception of form information is considered to be based on the functioning of simple and complex neurons in the primate striate cortex. However, a review of the physiological data on these brain cells cannot be harmonized with either the perceptual spatial frequency performance of primates or the performance which is necessary for form perception in humans. This discrepancy together with recent interest in cortical-like and perceptual-like processing in image coding and machine vision prompted a series of image processing experiments intended to provide some definition of the selection of image operators. The experiments were aimed at determining operators which could be used to detect edges in a computational manner consistent with the visual perception of structure in images. Fundamental issues were the selection of size (peak spatial frequency) and circular versus oriented operators (or some combination). In a previous study, circular difference-of-Gaussian (DOG) operators, with peak spatial frequency responses at about 11 and 33 cyc/deg were found to capture the primary structural information in images. Here larger scale circular DOG operators were explored and led to severe loss of image structure and introduced spatial dislocations (due to blur) in structure which is not consistent with visual perception. Orientation sensitive operators (akin to one class of simple cortical neurons) introduced ambiguities of edge extent regardless of the scale of the operator. For machine vision schemes which are functionally similar to natural vision form perception, two circularly symmetric very high spatial frequency channels appear to be necessary and sufficient for a wide range of natural images. Such a machine vision scheme is most similar to the physiological performance of the primate lateral geniculate nucleus rather than the striate cortex.

49 CFR 214.507 - Required safety equipment for new on-track roadway maintenance machines.

Code of Federal Regulations, 2011 CFR

2011-10-01

... glass, or other material with similar properties, if the machine is designed with a windshield. Each new... wipers or suitable alternatives that provide the machine operator an equivalent level of vision if...

Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest.

PubMed

Blasco, José; Munera, Sandra; Aleixos, Nuria; Cubero, Sergio; Molto, Enrique

Individual items of any agricultural commodity are different from each other in terms of colour, shape or size. Furthermore, as they are living thing, they change their quality attributes over time, thereby making the development of accurate automatic inspection machines a challenging task. Machine vision-based systems and new optical technologies make it feasible to create non-destructive control and monitoring tools for quality assessment to ensure adequate accomplishment of food standards. Such systems are much faster than any manual non-destructive examination of fruit and vegetable quality, thus allowing the whole production to be inspected with objective and repeatable criteria. Moreover, current technology makes it possible to inspect the fruit in spectral ranges beyond the sensibility of the human eye, for instance in the ultraviolet and near-infrared regions. Machine vision-based applications require the use of multiple technologies and knowledge, ranging from those related to image acquisition (illumination, cameras, etc.) to the development of algorithms for spectral image analysis. Machine vision-based systems for inspecting fruit and vegetables are targeted towards different purposes, from in-line sorting into commercial categories to the detection of contaminants or the distribution of specific chemical compounds on the product's surface. This chapter summarises the current state of the art in these techniques, starting with systems based on colour images for the inspection of conventional colour, shape or external defects and then goes on to consider recent developments in spectral image analysis for internal quality assessment or contaminant detection.

Machine vision system for measuring conifer seedling morphology

NASA Astrophysics Data System (ADS)

Rigney, Michael P.; Kranzler, Glenn A.

1995-01-01

A PC-based machine vision system providing rapid measurement of bare-root tree seedling morphological features has been designed. The system uses backlighting and a 2048-pixel line- scan camera to acquire images with transverse resolutions as high as 0.05 mm for precise measurement of stem diameter. Individual seedlings are manually loaded on a conveyor belt and inspected by the vision system in less than 0.25 seconds. Designed for quality control and morphological data acquisition by nursery personnel, the system provides a user-friendly, menu-driven graphical interface. The system automatically locates the seedling root collar and measures stem diameter, shoot height, sturdiness ratio, root mass length, projected shoot and root area, shoot-root area ratio, and percent fine roots. Sample statistics are computed for each measured feature. Measurements for each seedling may be stored for later analysis. Feature measurements may be compared with multi-class quality criteria to determine sample quality or to perform multi-class sorting. Statistical summary and classification reports may be printed to facilitate the communication of quality concerns with grading personnel. Tests were conducted at a commercial forest nursery to evaluate measurement precision. Four quality control personnel measured root collar diameter, stem height, and root mass length on each of 200 conifer seedlings. The same seedlings were inspected four times by the machine vision system. Machine stem diameter measurement precision was four times greater than that of manual measurements. Machine and manual measurements had comparable precision for shoot height and root mass length.

Can Humans Fly Action Understanding with Multiple Classes of Actors

DTIC Science & Technology

2015-06-08

recognition using structure from motion point clouds. In European Conference on Computer Vision, 2008. [5] R. Caruana. Multitask learning. Machine Learning...tonomous driving ? the kitti vision benchmark suite. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. [12] L. Gorelick, M. Blank

Neo-Symbiosis: The Next Stage in the Evolution of Human Information Interaction

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

Griffith, Douglas; Greitzer, Frank L.

We re-address the vision of human-computer symbiosis expressed by J. C. R. Licklider nearly a half-century ago, when he wrote: “The hope is that in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.” (Licklider, 1960). Unfortunately, little progress was made toward this vision over four decades following Licklider’s challenge, despite significant advancements in the fields of human factors and computer science. Licklider’s vision wasmore » largely forgotten. However, recent advances in information science and technology, psychology, and neuroscience have rekindled the potential of making the Licklider’s vision a reality. This paper provides a historical context for and updates the vision, and it argues that such a vision is needed as a unifying framework for advancing IS&T.« less

Reinforcement learning in computer vision

NASA Astrophysics Data System (ADS)

Bernstein, A. V.; Burnaev, E. V.

2018-04-01

Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn state of surrounding environment by solving corresponding computer vision tasks. Solutions of these tasks are used for making decisions about possible future actions. It is not surprising that when solving computer vision tasks we should take into account special aspects of their subsequent application in model-based predictive control. Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. In recent years, Reinforcement learning has been used both for solving such applied tasks as processing and analysis of visual information, and for solving specific computer vision problems such as filtering, extracting image features, localizing objects in scenes, and many others. The paper describes shortly the Reinforcement learning technology and its use for solving computer vision problems.

Nondestructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods.

PubMed

Liu, Changhong; Liu, Wei; Lu, Xuzhong; Chen, Wei; Yang, Jianbo; Zheng, Lei

2014-06-15

Crop-to-crop transgene flow may affect the seed purity of non-transgenic rice varieties, resulting in unwanted biosafety consequences. The feasibility of a rapid and nondestructive determination of transgenic rice seeds from its non-transgenic counterparts was examined by using multispectral imaging system combined with chemometric data analysis. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM), and PCA-back propagation neural network (PCA-BPNN) methods were applied to classify rice seeds according to their genetic origins. The results demonstrated that clear differences between non-transgenic and transgenic rice seeds could be easily visualized with the nondestructive determination method developed through this study and an excellent classification (up to 100% with LS-SVM model) can be achieved. It is concluded that multispectral imaging together with chemometric data analysis is a promising technique to identify transgenic rice seeds with high efficiency, providing bright prospects for future applications. Copyright © 2013 Elsevier Ltd. All rights reserved.

Mapping of Coral Reef Environment in the Arabian Gulf Using Multispectral Remote Sensing

NASA Astrophysics Data System (ADS)

Ben-Romdhane, H.; Marpu, P. R.; Ghedira, H.; Ouarda, T. B. M. J.

2016-06-01

Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.

A Method of High Throughput Monitoring Crop Physiology Using Chlorophyll Fluorescence and Multispectral Imaging.

PubMed

Wang, Heng; Qian, Xiangjie; Zhang, Lan; Xu, Sailong; Li, Haifeng; Xia, Xiaojian; Dai, Liankui; Xu, Liang; Yu, Jingquan; Liu, Xu

2018-01-01

We present a high throughput crop physiology condition monitoring system and corresponding monitoring method. The monitoring system can perform large-area chlorophyll fluorescence imaging and multispectral imaging. The monitoring method can determine the crop current condition continuously and non-destructively. We choose chlorophyll fluorescence parameters and relative reflectance of multispectral as the indicators of crop physiological status. Using tomato as experiment subject, the typical crop physiological stress, such as drought, nutrition deficiency and plant disease can be distinguished by the monitoring method. Furthermore, we have studied the correlation between the physiological indicators and the degree of stress. Besides realizing the continuous monitoring of crop physiology, the monitoring system and method provide the possibility of machine automatic diagnosis of the plant physiology. Highlights: A newly designed high throughput crop physiology monitoring system and the corresponding monitoring method are described in this study. Different types of stress can induce distinct fluorescence and spectral characteristics, which can be used to evaluate the physiological status of plants.

Feasibility in multispectral imaging for predicting the content of bioactive compounds in intact tomato fruit.

PubMed

Liu, Changhong; Liu, Wei; Chen, Wei; Yang, Jianbo; Zheng, Lei

2015-04-15

Tomato is an important health-stimulating fruit because of the antioxidant properties of its main bioactive compounds, dominantly lycopene and phenolic compounds. Nowadays, product differentiation in the fruit market requires an accurate evaluation of these value-added compounds. An experiment was conducted to simultaneously and non-destructively measure lycopene and phenolic compounds content in intact tomatoes using multispectral imaging combined with chemometric methods. Partial least squares (PLS), least squares-support vector machines (LS-SVM) and back propagation neural network (BPNN) were applied to develop quantitative models. Compared with PLS and LS-SVM, BPNN model considerably improved the performance with coefficient of determination in prediction (RP(2))=0.938 and 0.965, residual predictive deviation (RPD)=4.590 and 9.335 for lycopene and total phenolics content prediction, respectively. It is concluded that multispectral imaging is an attractive alternative to the standard methods for determination of bioactive compounds content in intact tomatoes, providing a useful platform for infield fruit sorting/grading. Copyright © 2014 Elsevier Ltd. All rights reserved.

A computer architecture for intelligent machines

NASA Technical Reports Server (NTRS)

Lefebvre, D. R.; Saridis, G. N.

1992-01-01

The theory of intelligent machines proposes a hierarchical organization for the functions of an autonomous robot based on the principle of increasing precision with decreasing intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed. The authors present a computer architecture that implements the lower two levels of the intelligent machine. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Execution-level controllers for motion and vision systems are briefly addressed, as well as the Petri net transducer software used to implement coordination-level functions. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

Bi Sparsity Pursuit: A Paradigm for Robust Subspace Recovery

DTIC Science & Technology

2016-09-27

16. SECURITY CLASSIFICATION OF: The success of sparse models in computer vision and machine learning is due to the fact that, high dimensional data...Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 Signal recovery, Sparse learning , Subspace modeling REPORT DOCUMENTATION PAGE 11...vision and machine learning is due to the fact that, high dimensional data is distributed in a union of low dimensional subspaces in many real-world

Quantifying nonhomogeneous colors in agricultural materials. Part II: comparison of machine vision and sensory panel evaluations.

PubMed

Balaban, M O; Aparicio, J; Zotarelli, M; Sims, C

2008-11-01

The average colors of mangos and apples were measured using machine vision. A method to quantify the perception of nonhomogeneous colors by sensory panelists was developed. Three colors out of several reference colors and their perceived percentage of the total sample area were selected by untrained panelists. Differences between the average colors perceived by panelists and those from the machine vision were reported as DeltaE values (color difference error). Effects of nonhomogeneity of color, and using real samples or their images in the sensory panels on DeltaE were evaluated. In general, samples with more nonuniform colors had higher DeltaE values, suggesting that panelists had more difficulty in evaluating more nonhomogeneous colors. There was no significant difference in DeltaE values between the real fruits and their screen image, therefore images can be used to evaluate color instead of the real samples.

Machine vision methods for use in grain variety discrimination and quality analysis

NASA Astrophysics Data System (ADS)

Winter, Philip W.; Sokhansanj, Shahab; Wood, Hugh C.

1996-12-01

Decreasing cost of computer technology has made it feasible to incorporate machine vision technology into the agriculture industry. The biggest attraction to using a machine vision system is the computer's ability to be completely consistent and objective. One use is in the variety discrimination and quality inspection of grains. Algorithms have been developed using Fourier descriptors and neural networks for use in variety discrimination of barley seeds. RGB and morphology features have been used in the quality analysis of lentils, and probability distribution functions and L,a,b color values for borage dockage testing. These methods have been shown to be very accurate and have a high potential for agriculture. This paper presents the techniques used and results obtained from projects including: a lentil quality discriminator, a barley variety classifier, a borage dockage tester, a popcorn quality analyzer, and a pistachio nut grading system.

Comparison of Artificial Immune System and Particle Swarm Optimization Techniques for Error Optimization of Machine Vision Based Tool Movements

NASA Astrophysics Data System (ADS)

Mahapatra, Prasant Kumar; Sethi, Spardha; Kumar, Amod

2015-10-01

In conventional tool positioning technique, sensors embedded in the motion stages provide the accurate tool position information. In this paper, a machine vision based system and image processing technique for motion measurement of lathe tool from two-dimensional sequential images captured using charge coupled device camera having a resolution of 250 microns has been described. An algorithm was developed to calculate the observed distance travelled by the tool from the captured images. As expected, error was observed in the value of the distance traversed by the tool calculated from these images. Optimization of errors due to machine vision system, calibration, environmental factors, etc. in lathe tool movement was carried out using two soft computing techniques, namely, artificial immune system (AIS) and particle swarm optimization (PSO). The results show better capability of AIS over PSO.

Generic decoding of seen and imagined objects using hierarchical visual features.

PubMed

Horikawa, Tomoyasu; Kamitani, Yukiyasu

2017-05-22

Object recognition is a key function in both human and machine vision. While brain decoding of seen and imagined objects has been achieved, the prediction is limited to training examples. We present a decoding approach for arbitrary objects using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features, including those derived from a deep convolutional neural network, can be predicted from fMRI patterns, and that greater accuracy is achieved for low-/high-level features with lower-/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, decoding of imagined objects reveals progressive recruitment of higher-to-lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval.

Performance evaluation of various classifiers for color prediction of rice paddy plant leaf

NASA Astrophysics Data System (ADS)

Singh, Amandeep; Singh, Maninder Lal

2016-11-01

The food industry is one of the industries that uses machine vision for a nondestructive quality evaluation of the produce. These quality measuring systems and softwares are precalculated on the basis of various image-processing algorithms which generally use a particular type of classifier. These classifiers play a vital role in making the algorithms so intelligent that it can contribute its best while performing the said quality evaluations by translating the human perception into machine vision and hence machine learning. The crop of interest is rice, and the color of this crop indicates the health status of the plant. An enormous number of classifiers are available to solve the purpose of color prediction, but choosing the best among them is the focus of this paper. Performance of a total of 60 classifiers has been analyzed from the application point of view, and the results have been discussed. The motivation comes from the idea of providing a set of classifiers with excellent performance and implementing them on a single algorithm for the improvement of machine vision learning and, hence, associated applications.

Multispectral tissue characterization for intestinal anastomosis optimization.

PubMed

Cha, Jaepyeong; Shademan, Azad; Le, Hanh N D; Decker, Ryan; Kim, Peter C W; Kang, Jin U; Krieger, Axel

2015-10-01

Intestinal anastomosis is a surgical procedure that restores bowel continuity after surgical resection to treat intestinal malignancy, inflammation, or obstruction. Despite the routine nature of intestinal anastomosis procedures, the rate of complications is high. Standard visual inspection cannot distinguish the tissue subsurface and small changes in spectral characteristics of the tissue, so existing tissue anastomosis techniques that rely on human vision to guide suturing could lead to problems such as bleeding and leakage from suturing sites. We present a proof-of-concept study using a portable multispectral imaging (MSI) platform for tissue characterization and preoperative surgical planning in intestinal anastomosis. The platform is composed of a fiber ring light-guided MSI system coupled with polarizers and image analysis software. The system is tested on ex vivo porcine intestine tissue, and we demonstrate the feasibility of identifying optimal regions for suture placement.

Multispectral tissue characterization for intestinal anastomosis optimization

PubMed Central

Cha, Jaepyeong; Shademan, Azad; Le, Hanh N. D.; Decker, Ryan; Kim, Peter C. W.; Kang, Jin U.; Krieger, Axel

2015-01-01

Abstract. Intestinal anastomosis is a surgical procedure that restores bowel continuity after surgical resection to treat intestinal malignancy, inflammation, or obstruction. Despite the routine nature of intestinal anastomosis procedures, the rate of complications is high. Standard visual inspection cannot distinguish the tissue subsurface and small changes in spectral characteristics of the tissue, so existing tissue anastomosis techniques that rely on human vision to guide suturing could lead to problems such as bleeding and leakage from suturing sites. We present a proof-of-concept study using a portable multispectral imaging (MSI) platform for tissue characterization and preoperative surgical planning in intestinal anastomosis. The platform is composed of a fiber ring light-guided MSI system coupled with polarizers and image analysis software. The system is tested on ex vivo porcine intestine tissue, and we demonstrate the feasibility of identifying optimal regions for suture placement. PMID:26440616

The development of machine technology processing for earth resource survey

NASA Technical Reports Server (NTRS)

Landgrebe, D. A.

1970-01-01

The following technologies are considered for automatic processing of earth resources data: (1) registration of multispectral and multitemporal images, (2) digital image display systems, (3) data system parameter effects on satellite remote sensing systems, and (4) data compression techniques based on spectral redundancy. The importance of proper spectral band and compression algorithm selections is pointed out.

Iterative normalization method for improved prostate cancer localization with multispectral magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Liu, Xin; Samil Yetik, Imam

2012-04-01

Use of multispectral magnetic resonance imaging has received a great interest for prostate cancer localization in research and clinical studies. Manual extraction of prostate tumors from multispectral magnetic resonance imaging is inefficient and subjective, while automated segmentation is objective and reproducible. For supervised, automated segmentation approaches, learning is essential to obtain the information from training dataset. However, in this procedure, all patients are assumed to have similar properties for the tumor and normal tissues, and the segmentation performance suffers since the variations across patients are ignored. To conquer this difficulty, we propose a new iterative normalization method based on relative intensity values of tumor and normal tissues to normalize multispectral magnetic resonance images and improve segmentation performance. The idea of relative intensity mimics the manual segmentation performed by human readers, who compare the contrast between regions without knowing the actual intensity values. We compare the segmentation performance of the proposed method with that of z-score normalization followed by support vector machine, local active contours, and fuzzy Markov random field. Our experimental results demonstrate that our method outperforms the three other state-of-the-art algorithms, and was found to have specificity of 0.73, sensitivity of 0.69, and accuracy of 0.79, significantly better than alternative methods.

Software architecture for time-constrained machine vision applications

NASA Astrophysics Data System (ADS)

Usamentiaga, Rubén; Molleda, Julio; García, Daniel F.; Bulnes, Francisco G.

2013-01-01

Real-time image and video processing applications require skilled architects, and recent trends in the hardware platform make the design and implementation of these applications increasingly complex. Many frameworks and libraries have been proposed or commercialized to simplify the design and tuning of real-time image processing applications. However, they tend to lack flexibility, because they are normally oriented toward particular types of applications, or they impose specific data processing models such as the pipeline. Other issues include large memory footprints, difficulty for reuse, and inefficient execution on multicore processors. We present a novel software architecture for time-constrained machine vision applications that addresses these issues. The architecture is divided into three layers. The platform abstraction layer provides a high-level application programming interface for the rest of the architecture. The messaging layer provides a message-passing interface based on a dynamic publish/subscribe pattern. A topic-based filtering in which messages are published to topics is used to route the messages from the publishers to the subscribers interested in a particular type of message. The application layer provides a repository for reusable application modules designed for machine vision applications. These modules, which include acquisition, visualization, communication, user interface, and data processing, take advantage of the power of well-known libraries such as OpenCV, Intel IPP, or CUDA. Finally, the proposed architecture is applied to a real machine vision application: a jam detector for steel pickling lines.

Database Integrity Monitoring for Synthetic Vision Systems Using Machine Vision and SHADE

NASA Technical Reports Server (NTRS)

Cooper, Eric G.; Young, Steven D.

2005-01-01

In an effort to increase situational awareness, the aviation industry is investigating technologies that allow pilots to visualize what is outside of the aircraft during periods of low-visibility. One of these technologies, referred to as Synthetic Vision Systems (SVS), provides the pilot with real-time computer-generated images of obstacles, terrain features, runways, and other aircraft regardless of weather conditions. To help ensure the integrity of such systems, methods of verifying the accuracy of synthetically-derived display elements using onboard remote sensing technologies are under investigation. One such method is based on a shadow detection and extraction (SHADE) algorithm that transforms computer-generated digital elevation data into a reference domain that enables direct comparison with radar measurements. This paper describes machine vision techniques for making this comparison and discusses preliminary results from application to actual flight data.

Creative Technology and Rap

ERIC Educational Resources Information Center

Ch'ien, Evelyn

2011-01-01

This paper describes how a linguistic form, rap, can evolve in tandem with technological advances and manifest human-machine creativity. Rather than assuming that the interplay between machines and technology makes humans robotic or machine-like, the paper explores how the pressure of executing artistic visions using technology can drive…

Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review.

PubMed

Pérez, Luis; Rodríguez, Íñigo; Rodríguez, Nuria; Usamentiaga, Rubén; García, Daniel F

2016-03-05

In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works.

Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review

PubMed Central

Pérez, Luis; Rodríguez, Íñigo; Rodríguez, Nuria; Usamentiaga, Rubén; García, Daniel F.

2016-01-01

In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works. PMID:26959030

A neurite quality index and machine vision software for improved quantification of neurodegeneration.

PubMed

Romero, Peggy; Miller, Ted; Garakani, Arman

2009-12-01

Current methods to assess neurodegradation in dorsal root ganglion cultures as a model for neurodegenerative diseases are imprecise and time-consuming. Here we describe two new methods to quantify neuroprotection in these cultures. The neurite quality index (NQI) builds upon earlier manual methods, incorporating additional morphological events to increase detection sensitivity for the detection of early degeneration events. Neurosight is a machine vision-based method that recapitulates many of the strengths of NQI while enabling high-throughput screening applications with decreased costs.

Low-latency situational awareness for UxV platforms

NASA Astrophysics Data System (ADS)

Berends, David C.

2012-06-01

Providing high quality, low latency video from unmanned vehicles through bandwidth-limited communications channels remains a formidable challenge for modern vision system designers. SRI has developed a number of enabling technologies to address this, including the use of SWaP-optimized Systems-on-a-Chip which provide Multispectral Fusion and Contrast Enhancement as well as H.264 video compression. Further, the use of salience-based image prefiltering prior to image compression greatly reduces output video bandwidth by selectively blurring non-important scene regions. Combined with our customization of the VLC open source video viewer for low latency video decoding, SRI developed a prototype high performance, high quality vision system for UxV application in support of very demanding system latency requirements and user CONOPS.

Artificial Intelligence/Robotics Applications to Navy Aircraft Maintenance.

DTIC Science & Technology

1984-06-01

other automatic machinery such as presses, molding machines , and numerically-controlled machine tools, just as people do. A-36...Robotics Technologies 3 B. Relevant AI Technologies 4 1. Expert Systems 4 2. Automatic Planning 4 3. Natural Language 5 4. Machine Vision...building machines that imitate human behavior. Artificial intelligence is concerned with the functions of the brain, whereas robotics include, in

Development of machine-vision system for gap inspection of muskmelon grafted seedlings.

PubMed

Liu, Siyao; Xing, Zuochang; Wang, Zifan; Tian, Subo; Jahun, Falalu Rabiu

2017-01-01

Grafting robots have been developed in the world, but some auxiliary works such as gap-inspecting for grafted seedlings still need to be done by human. An machine-vision system of gap inspection for grafted muskmelon seedlings was developed in this study. The image acquiring system consists of a CCD camera, a lens and a front white lighting source. The image of inspected gap was processed and analyzed by software of HALCON 12.0. The recognition algorithm for the system is based on principle of deformable template matching. A template should be created from an image of qualified grafted seedling gap. Then the gap image of the grafted seedling will be compared with the created template to determine their matching degree. Based on the similarity between the gap image of grafted seedling and the template, the matching degree will be 0 to 1. The less similar for the grafted seedling gap with the template the smaller of matching degree. Thirdly, the gap will be output as qualified or unqualified. If the matching degree of grafted seedling gap and the template is less than 0.58, or there is no match is found, the gap will be judged as unqualified; otherwise the gap will be qualified. Finally, 100 muskmelon seedlings were grafted and inspected to test the gap inspection system. Results showed that the gap inspection machine-vision system could recognize the gap qualification correctly as 98% of human vision. And the inspection speed of this system can reach 15 seedlings·min-1. The gap inspection process in grafting can be fully automated with this developed machine-vision system, and the gap inspection system will be a key step of a fully-automatic grafting robots.

Biologically based machine vision: signal analysis of monopolar cells in the visual system of Musca domestica.

PubMed

Newton, Jenny; Barrett, Steven F; Wilcox, Michael J; Popp, Stephanie

2002-01-01

Machine vision for navigational purposes is a rapidly growing field. Many abilities such as object recognition and target tracking rely on vision. Autonomous vehicles must be able to navigate in dynamic enviroments and simultaneously locate a target position. Traditional machine vision often fails to react in real time because of large computational requirements whereas the fly achieves complex orientation and navigation with a relatively small and simple brain. Understanding how the fly extracts visual information and how neurons encode and process information could lead us to a new approach for machine vision applications. Photoreceptors in the Musca domestica eye that share the same spatial information converge into a structure called the cartridge. The cartridge consists of the photoreceptor axon terminals and monopolar cells L1, L2, and L4. It is thought that L1 and L2 cells encode edge related information relative to a single cartridge. These cells are thought to be equivalent to vertebrate bipolar cells, producing contrast enhancement and reduction of information sent to L4. Monopolar cell L4 is thought to perform image segmentation on the information input from L1 and L2 and also enhance edge detection. A mesh of interconnected L4's would correlate the output from L1 and L2 cells of adjacent cartridges and provide a parallel network for segmenting an object's edges. The focus of this research is to excite photoreceptors of the common housefly, Musca domestica, with different visual patterns. The electrical response of monopolar cells L1, L2, and L4 will be recorded using intracellular recording techniques. Signal analysis will determine the neurocircuitry to detect and segment images.

Automatic Quality Inspection of Percussion Cap Mass Production by Means of 3D Machine Vision and Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Tellaeche, A.; Arana, R.; Ibarguren, A.; Martínez-Otzeta, J. M.

The exhaustive quality control is becoming very important in the world's globalized market. One of these examples where quality control becomes critical is the percussion cap mass production. These elements must achieve a minimum tolerance deviation in their fabrication. This paper outlines a machine vision development using a 3D camera for the inspection of the whole production of percussion caps. This system presents multiple problems, such as metallic reflections in the percussion caps, high speed movement of the system and mechanical errors and irregularities in percussion cap placement. Due to these problems, it is impossible to solve the problem by traditional image processing methods, and hence, machine learning algorithms have been tested to provide a feasible classification of the possible errors present in the percussion caps.

Machine vision inspection of lace using a neural network

NASA Astrophysics Data System (ADS)

Sanby, Christopher; Norton-Wayne, Leonard

1995-03-01

Lace is particularly difficult to inspect using machine vision since it comprises a fine and complex pattern of threads which must be verified, on line and in real time. Small distortions in the pattern are unavoidable. This paper describes instrumentation for inspecting lace actually on the knitting machine. A CCD linescan camera synchronized to machine motions grabs an image of the lace. Differences between this lace image and a perfect prototype image are detected by comparison methods, thresholding techniques, and finally, a neural network (to distinguish real defects from false alarms). Though produced originally in a laboratory on SUN Sparc work-stations, the processing has subsequently been implemented on a 50 Mhz 486 PC-look-alike. Successful operation has been demonstrated in a factory, but over a restricted width. Full width coverage awaits provision of faster processing.

A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

PubMed Central

Wang, Guizhou; Liu, Jianbo; He, Guojin

2013-01-01

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy. PMID:24453808

Experiences Using an Open Source Software Library to Teach Computer Vision Subjects

ERIC Educational Resources Information Center

Cazorla, Miguel; Viejo, Diego

2015-01-01

Machine vision is an important subject in computer science and engineering degrees. For laboratory experimentation, it is desirable to have a complete and easy-to-use tool. In this work we present a Java library, oriented to teaching computer vision. We have designed and built the library from the scratch with emphasis on readability and…

Feature recognition and detection for ancient architecture based on machine vision

NASA Astrophysics Data System (ADS)

Zou, Zheng; Wang, Niannian; Zhao, Peng; Zhao, Xuefeng

2018-03-01

Ancient architecture has a very high historical and artistic value. The ancient buildings have a wide variety of textures and decorative paintings, which contain a lot of historical meaning. Therefore, the research and statistics work of these different compositional and decorative features play an important role in the subsequent research. However, until recently, the statistics of those components are mainly by artificial method, which consumes a lot of labor and time, inefficiently. At present, as the strong support of big data and GPU accelerated training, machine vision with deep learning as the core has been rapidly developed and widely used in many fields. This paper proposes an idea to recognize and detect the textures, decorations and other features of ancient building based on machine vision. First, classify a large number of surface textures images of ancient building components manually as a set of samples. Then, using the convolution neural network to train the samples in order to get a classification detector. Finally verify its precision.

A low-cost machine vision system for the recognition and sorting of small parts

NASA Astrophysics Data System (ADS)

Barea, Gustavo; Surgenor, Brian W.; Chauhan, Vedang; Joshi, Keyur D.

2018-04-01

An automated machine vision-based system for the recognition and sorting of small parts was designed, assembled and tested. The system was developed to address a need to expose engineering students to the issues of machine vision and assembly automation technology, with readily available and relatively low-cost hardware and software. This paper outlines the design of the system and presents experimental performance results. Three different styles of plastic gears, together with three different styles of defective gears, were used to test the system. A pattern matching tool was used for part classification. Nine experiments were conducted to demonstrate the effects of changing various hardware and software parameters, including: conveyor speed, gear feed rate, classification, and identification score thresholds. It was found that the system could achieve a maximum system accuracy of 95% at a feed rate of 60 parts/min, for a given set of parameter settings. Future work will be looking at the effect of lighting.

An Automated Classification Technique for Detecting Defects in Battery Cells

NASA Technical Reports Server (NTRS)

McDowell, Mark; Gray, Elizabeth

2006-01-01

Battery cell defect classification is primarily done manually by a human conducting a visual inspection to determine if the battery cell is acceptable for a particular use or device. Human visual inspection is a time consuming task when compared to an inspection process conducted by a machine vision system. Human inspection is also subject to human error and fatigue over time. We present a machine vision technique that can be used to automatically identify defective sections of battery cells via a morphological feature-based classifier using an adaptive two-dimensional fast Fourier transformation technique. The initial area of interest is automatically classified as either an anode or cathode cell view as well as classified as an acceptable or a defective battery cell. Each battery cell is labeled and cataloged for comparison and analysis. The result is the implementation of an automated machine vision technique that provides a highly repeatable and reproducible method of identifying and quantifying defects in battery cells.

An active role for machine learning in drug development

PubMed Central

Murphy, Robert F.

2014-01-01

Due to the complexity of biological systems, cutting-edge machine-learning methods will be critical for future drug development. In particular, machine-vision methods to extract detailed information from imaging assays and active-learning methods to guide experimentation will be required to overcome the dimensionality problem in drug development. PMID:21587249

Machine Learning Techniques in Clinical Vision Sciences.

PubMed

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.

A computer architecture for intelligent machines

NASA Technical Reports Server (NTRS)

Lefebvre, D. R.; Saridis, G. N.

1991-01-01

The Theory of Intelligent Machines proposes a hierarchical organization for the functions of an autonomous robot based on the Principle of Increasing Precision With Decreasing Intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed in recent years. A computer architecture that implements the lower two levels of the intelligent machine is presented. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Details of Execution Level controllers for motion and vision systems are addressed, as well as the Petri net transducer software used to implement Coordination Level functions. Extensions to UNIX and VxWorks operating systems which enable the development of a heterogeneous, distributed application are described. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

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.

Qualitative evaluations and comparisons of six night-vision colorization methods

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Reese, Kristopher; Blasch, Erik; McManamon, Paul

2013-05-01

Current multispectral night vision (NV) colorization techniques can manipulate images to produce colorized images that closely resemble natural scenes. The colorized NV images can enhance human perception by improving observer object classification and reaction times especially for low light conditions. This paper focuses on the qualitative (subjective) evaluations and comparisons of six NV colorization methods. The multispectral images include visible (Red-Green- Blue), near infrared (NIR), and long wave infrared (LWIR) images. The six colorization methods are channel-based color fusion (CBCF), statistic matching (SM), histogram matching (HM), joint-histogram matching (JHM), statistic matching then joint-histogram matching (SM-JHM), and the lookup table (LUT). Four categries of quality measurements are used for the qualitative evaluations, which are contrast, detail, colorfulness, and overall quality. The score of each measurement is rated from 1 to 3 scale to represent low, average, and high quality, respectively. Specifically, high contrast (of rated score 3) means an adequate level of brightness and contrast. The high detail represents high clarity of detailed contents while maintaining low artifacts. The high colorfulness preserves more natural colors (i.e., closely resembles the daylight image). Overall quality is determined from the NV image compared to the reference image. Nine sets of multispectral NV images were used in our experiments. For each set, the six colorized NV images (produced from NIR and LWIR images) are concurrently presented to users along with the reference color (RGB) image (taken at daytime). A total of 67 subjects passed a screening test ("Ishihara Color Blindness Test") and were asked to evaluate the 9-set colorized images. The experimental results showed the quality order of colorization methods from the best to the worst: CBCF < SM < SM-JHM < LUT < JHM < HM. It is anticipated that this work will provide a benchmark for NV colorization and for quantitative evaluation using an objective metric such as objective evaluation index (OEI).

The Intangible Assets Advantages in the Machine Vision Inspection of Thermoplastic Materials

NASA Astrophysics Data System (ADS)

Muntean, Diana; Răulea, Andreea Simina

2017-12-01

Innovation is not a simple concept but is the main source of success. It is more important to have the right people and mindsets in place than to have a perfectly crafted plan in order to make the most out of an idea or business. The aim of this paper is to emphasize the importance of intangible assets when it comes to machine vision inspection of thermoplastic materials pointing out some aspects related to knowledge based assets and their need for a success idea to be developed in a successful product.

Design study for multi-channel tape recorder system, volume 1

NASA Technical Reports Server (NTRS)

1972-01-01

The means of storing multispectral, high resolution sensor data on an Earth observing satellite are studied. It is concluded that this is best done digitally on a multi-track, longitudinal, magnetic tape recorder. The machine proposed will store 8 X 10 to the 10th power bits of data on 1040 m of 51 mm-wide magnetic tape mounted on two co-planar reels.

High-fidelity video and still-image communication based on spectral information: natural vision system and its applications

NASA Astrophysics Data System (ADS)

Yamaguchi, Masahiro; Haneishi, Hideaki; Fukuda, Hiroyuki; Kishimoto, Junko; Kanazawa, Hiroshi; Tsuchida, Masaru; Iwama, Ryo; Ohyama, Nagaaki

2006-01-01

In addition to the great advancement of high-resolution and large-screen imaging technology, the issue of color is now receiving considerable attention as another aspect than the image resolution. It is difficult to reproduce the original color of subject in conventional imaging systems, and that obstructs the applications of visual communication systems in telemedicine, electronic commerce, and digital museum. To breakthrough the limitation of conventional RGB 3-primary systems, "Natural Vision" project aims at an innovative video and still-image communication technology with high-fidelity color reproduction capability, based on spectral information. This paper summarizes the results of NV project including the development of multispectral and multiprimary imaging technologies and the experimental investigations on the applications to medicine, digital archives, electronic commerce, and computer graphics.

Pseudoisochromatic test plate colour representation dependence on printing technology

NASA Astrophysics Data System (ADS)

Luse, K.; Fomins, S.; Ozolinsh, M.

2012-08-01

The aim of the study is to determine best printing technology for creation of colour vision deficiency tests. Valid tests for protanopia and deuteranopia were created from perceived colour matching experiments from printed colour samples by colour deficient individuals. Calibrated EpsonStylus Pro 7800 printer for ink prints and Noritsu HD 3701 digital printer for photographic prints were used. Multispectral imagery (by tunable liquid crystal filters system CRI Nuance Vis 07) data analysis show that in case of ink prints, the measured pixel colour coordinate dispersion (in the CIExy colour diagram) of similar colour arrays is smaller than in case of photographic printing. The print quality in terms of colour coordinate dispersion for printing methods used is much higher than in case of commercially available colour vision deficiency tests.

Evaluation of Chilling Injury in Mangoes Using Multispectral Imaging.

PubMed

Hashim, Norhashila; Onwude, Daniel I; Osman, Muhamad Syafiq

2018-05-01

Commodities originating from tropical and subtropical climes are prone to chilling injury (CI). This injury could affect the quality and marketing potential of mango after harvest. This will later affect the quality of the produce and subsequent consumer acceptance. In this study, the appearance of CI symptoms in mango was evaluated non-destructively using multispectral imaging. The fruit were stored at 4 °C to induce CI and 12 °C to preserve the quality of the control samples for 4 days before they were taken out and stored at ambient temperature for 24 hr. Measurements using multispectral imaging and standard reference methods were conducted before and after storage. The performance of multispectral imaging was compared using standard reference properties including moisture content (MC), total soluble solids (TSS) content, firmness, pH, and color. Least square support vector machine (LS-SVM) combined with principal component analysis (PCA) were used to discriminate CI samples with those of control and before storage, respectively. The statistical results demonstrated significant changes in the reference quality properties of samples before and after storage. The results also revealed that multispectral parameters have a strong correlation with the reference parameters of L * , a * , TSS, and MC. The MC and L * were found to be the best reference parameters in identifying the severity of CI in mangoes. PCA and LS-SVM analysis indicated that the fruit were successfully classified into their categories, that is, before storage, control, and CI. This indicated that the multispectral imaging technique is feasible for detecting CI in mangoes during postharvest storage and processing. This paper demonstrates a fast, easy, and accurate method of identifying the effect of cold storage on mango, nondestructively. The method presented in this paper can be used industrially to efficiently differentiate different fruits from each other after low temperature storage. © 2018 Institute of Food Technologists®.

Machine vision system: a tool for quality inspection of food and agricultural products.

PubMed

Patel, Krishna Kumar; Kar, A; Jha, S N; Khan, M A

2012-04-01

Quality inspection of food and agricultural produce are difficult and labor intensive. Simultaneously, with increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in food products continues to grow. However, these operations generally in India are manual which is costly as well as unreliable because human decision in identifying quality factors such as appearance, flavor, nutrient, texture, etc., is inconsistent, subjective and slow. Machine vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has found a variety of different applications in the food industry. Considerable research has highlighted its potential for the inspection and grading of fruits and vegetables, grain quality and characteristic examination and quality evaluation of other food products like bakery products, pizza, cheese, and noodles etc. The objective of this paper is to provide in depth introduction of machine vision system, its components and recent work reported on food and agricultural produce.

Design of apochromatic lens with large field and high definition for machine vision.

PubMed

Yang, Ao; Gao, Xingyu; Li, Mingfeng

2016-08-01

Precise machine vision detection for a large object at a finite working distance (WD) requires that the lens has a high resolution for a large field of view (FOV). In this case, the effect of a secondary spectrum on image quality is not negligible. According to the detection requirements, a high resolution apochromatic objective is designed and analyzed. The initial optical structure (IOS) is combined with three segments. Next, the secondary spectrum of the IOS is corrected by replacing glasses using the dispersion vector analysis method based on the Buchdahl dispersion equation. Other aberrations are optimized by the commercial optical design software ZEMAX by properly choosing the optimization function operands. The optimized optical structure (OOS) has an f-number (F/#) of 3.08, a FOV of φ60  mm, a WD of 240 mm, and a modulated transfer function (MTF) of all fields of more than 0.1 at 320  cycles/mm. The design requirements for a nonfluorite material apochromatic objective lens with a large field and high definition for machine vision detection have been achieved.

Broiler weight estimation based on machine vision and artificial neural network.

PubMed

Amraei, S; Abdanan Mehdizadeh, S; Salari, S

2017-04-01

1. Machine vision and artificial neural network (ANN) procedures were used to estimate live body weight of broiler chickens in 30 1-d-old broiler chickens reared for 42 d. 2. Imaging was performed two times daily. To localise chickens within the pen, an ellipse fitting algorithm was used and the chickens' head and tail removed using the Chan-Vese method. 3. The correlations between the body weight and 6 physical extracted features indicated that there were strong correlations between body weight and the 5 features including area, perimeter, convex area, major and minor axis length. 5. According to statistical analysis there was no significant difference between morning and afternoon data over 42 d. 6. In an attempt to improve the accuracy of live weight approximation different ANN techniques, including Bayesian regulation, Levenberg-Marquardt, Scaled conjugate gradient and gradient descent were used. Bayesian regulation with R 2 value of 0.98 was the best network for prediction of broiler weight. 7. The accuracy of the machine vision technique was examined and most errors were less than 50 g.

Principles of computer processing of Landsat data for geologic applications

USGS Publications Warehouse

Taranik, James V.

1978-01-01

The main objectives of computer processing of Landsat data for geologic applications are to improve display of image data to the analyst or to facilitate evaluation of the multispectral characteristics of the data. Interpretations of the data are made from enhanced and classified data by an analyst trained in geology. Image enhancements involve adjustments of brightness values for individual picture elements. Image classification involves determination of the brightness values of picture elements for a particular cover type. Histograms are used to display the range and frequency of occurrence of brightness values. Landsat-1 and -2 data are preprocessed at Goddard Space Flight Center (GSFC) to adjust for the detector response of the multispectral scanner (MSS). Adjustments are applied to minimize the effects of striping, adjust for bad-data lines and line segments and lost individual pixel data. Because illumination conditions and landscape characteristics vary considerably and detector response changes with time, the radiometric adjustments applied at GSFC are seldom perfect and some detector striping remain in Landsat data. Rotation of the Earth under the satellite and movements of the satellite platform introduce geometric distortions in the data that must also be compensated for if image data are to be correctly displayed to the data analyst. Adjustments to Landsat data are made to compensate for variable solar illumination and for atmospheric effects. GeoMetric registration of Landsat data involves determination of the spatial location of a pixel in. the output image and the determination of a new value for the pixel. The general objective of image enhancement is to optimize display of the data to the analyst. Contrast enhancements are employed to expand the range of brightness values in Landsat data so that the data can be efficiently recorded in a manner desired by the analyst. Spatial frequency enhancements are designed to enhance boundaries between features which have subtle differences in brightness values. Ratioing tends to reduce the effects due to topography and it tends to emphasize changes in brightness values between two Landsat bands. Simulated natural color is produced for geologists so that the colors of materials on images appear similar to colors of actual materials in the field. Image classification of Landsat data involves both machine assisted delineation of multispectral patterns in four-dimensional spectral space and identification of machine delineated multispectral patterns that represent particular cover conditions. The geological information derived from an analysis of a multispectral classification is usually related to lithology.

Industry's tireless eyes

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

Not Available

1987-08-01

This article reports that there are literally hundreds of machine vision systems from which to choose. They range in cost from $10,000 to $1,000,000. Most have been designed for specific applications; the same systems if used for a different application may fail dismally. How can you avoid wasting money on inferior, useless, or nonexpandable systems. A good reference is the Automated Vision Association in Ann Arbor, Mich., a trade group comprised of North American machine vision manufacturers. Reputable suppliers caution users to do their homework before making an investment. Important considerations include comprehensive details on the objects to be viewed-thatmore » is, quantity, shape, dimension, size, and configuration details; lighting characteristics and variations; component orientation details. Then, what do you expect the system to do-inspect, locate components, aid in robotic vision. Other criteria include system speed and related accuracy and reliability. What are the projected benefits and system paybacks.. Examine primarily paybacks associated with scrap and rework reduction as well as reduced warranty costs.« less

Testing meta tagger

DTIC Science & Technology

2017-12-21

rank , and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on...Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.[1] Arthur Samuel...an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning " in 1959 while at IBM[2]. Evolved

Discussion of CoSA: Clustering of Sparse Approximations

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

Armstrong, Derek Elswick

2017-03-07

The purpose of this talk is to discuss the possible applications of CoSA (Clustering of Sparse Approximations) to the exploitation of HSI (HyperSpectral Imagery) data. CoSA is presented by Moody et al. in the Journal of Applied Remote Sensing (“Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries”, Vol. 8, 2014) and is based on machine learning techniques.

On-line welding quality inspection system for steel pipe based on machine vision

NASA Astrophysics Data System (ADS)

Yang, Yang

2017-05-01

In recent years, high frequency welding has been widely used in production because of its advantages of simplicity, reliability and high quality. In the production process, how to effectively control the weld penetration welding, ensure full penetration, weld uniform, so as to ensure the welding quality is to solve the problem of the present stage, it is an important research field in the field of welding technology. In this paper, based on the study of some methods of welding inspection, a set of on-line welding quality inspection system based on machine vision is designed.

Rubber hose surface defect detection system based on machine vision

NASA Astrophysics Data System (ADS)

Meng, Fanwu; Ren, Jingrui; Wang, Qi; Zhang, Teng

2018-01-01

As an important part of connecting engine, air filter, engine, cooling system and automobile air-conditioning system, automotive hose is widely used in automobile. Therefore, the determination of the surface quality of the hose is particularly important. This research is based on machine vision technology, using HALCON algorithm for the processing of the hose image, and identifying the surface defects of the hose. In order to improve the detection accuracy of visual system, this paper proposes a method to classify the defects to reduce misjudegment. The experimental results show that the method can detect surface defects accurately.

Machine vision system for automated detection of stained pistachio nuts

NASA Astrophysics Data System (ADS)

Pearson, Tom C.

1995-01-01

A machine vision system was developed to separate stained pistachio nuts, which comprise of about 5% of the California crop, from unstained nuts. The system may be used to reduce labor involved with manual grading or to remove aflatoxin contaminated product from low grade process streams. The system was tested on two different pistachio process streams: the bi- chromatic color sorter reject stream and the small nut shelling stock stream. The system had a minimum overall error rate of 14% for the bi-chromatic sorter reject stream and 15% for the small shelling stock stream.

Artificial Intelligence.

ERIC Educational Resources Information Center

Wash, Darrel Patrick

1989-01-01

Making a machine seem intelligent is not easy. As a consequence, demand has been rising for computer professionals skilled in artificial intelligence and is likely to continue to go up. These workers develop expert systems and solve the mysteries of machine vision, natural language processing, and neural networks. (Editor)

Recognition of surface lithologic and topographic patterns in southwest Colorado with ADP techniques

NASA Technical Reports Server (NTRS)

Melhorn, W. N.; Sinnock, S.

1973-01-01

Analysis of ERTS-1 multispectral data by automatic pattern recognition procedures is applicable toward grappling with current and future resource stresses by providing a means for refining existing geologic maps. The procedures used in the current analysis already yield encouraging results toward the eventual machine recognition of extensive surface lithologic and topographic patterns. Automatic mapping of a series of hogbacks, strike valleys, and alluvial surfaces along the northwest flank of the San Juan Basin in Colorado can be obtained by minimal man-machine interaction. The determination of causes for separable spectral signatures is dependent upon extensive correlation of micro- and macro field based ground truth observations and aircraft underflight data with the satellite data.

The Earth Resources Laboratory Applications Software (ELAS) in university research and education: An operator oriented geobased information system

NASA Technical Reports Server (NTRS)

Coker, B. L.; Kind, T. C.; Smith, W. F., Jr.; Weber, N. V.

1981-01-01

Created for analyzing and processing digital data such as that collected by multispectral scanners or digitized from maps, ELAS is designed for ease of user operation and includes its own FORTRAN operating monitor and an expandable set of application modules which are FORTRAN overlays. On those machines that do not support FORTRAN overlaying, the modules exist as subprograms. The subsystem can be implemented on most 16-bit or 32-bit machines and is capable of, but not limited to, operating on low-cost minicomputer systems. The recommended hardware configuration for ELAS and a representative listing of some operating and application modules are presented.

Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

NASA Astrophysics Data System (ADS)

DeCost, Brian L.; Jain, Harshvardhan; Rollett, Anthony D.; Holm, Elizabeth A.

2017-03-01

By applying computer vision and machine learning methods, we develop a system to characterize powder feedstock materials for metal additive manufacturing (AM). Feature detection and description algorithms are applied to create a microstructural scale image representation that can be used to cluster, compare, and analyze powder micrographs. When applied to eight commercial feedstock powders, the system classifies powder images into the correct material systems with greater than 95% accuracy. The system also identifies both representative and atypical powder images. These results suggest the possibility of measuring variations in powders as a function of processing history, relating microstructural features of powders to properties relevant to their performance in AM processes, and defining objective material standards based on visual images. A significant advantage of the computer vision approach is that it is autonomous, objective, and repeatable.

The effect of multispectral image fusion enhancement on human efficiency.

PubMed

Bittner, Jennifer L; Schill, M Trent; Mohd-Zaid, Fairul; Blaha, Leslie M

2017-01-01

The visual system can be highly influenced by changes to visual presentation. Thus, numerous techniques have been developed to augment imagery in an attempt to improve human perception. The current paper examines the potential impact of one such enhancement, multispectral image fusion, where imagery captured in varying spectral bands (e.g., visible, thermal, night vision) is algorithmically combined to produce an output to strengthen visual perception. We employ ideal observer analysis over a series of experimental conditions to (1) establish a framework for testing the impact of image fusion over the varying aspects surrounding its implementation (e.g., stimulus content, task) and (2) examine the effectiveness of fusion on human information processing efficiency in a basic application. We used a set of rotated Landolt C images captured with a number of individual sensor cameras and combined across seven traditional fusion algorithms (e.g., Laplacian pyramid, principal component analysis, averaging) in a 1-of-8 orientation task. We found that, contrary to the idea of fused imagery always producing a greater impact on perception, single-band imagery can be just as influential. Additionally, efficiency data were shown to fluctuate based on sensor combination instead of fusion algorithm, suggesting the need for examining multiple factors to determine the success of image fusion. Our use of ideal observer analysis, a popular technique from the vision sciences, provides not only a standard for testing fusion in direct relation to the visual system but also allows for comparable examination of fusion across its associated problem space of application.

Optical character recognition reading aid for the visually impaired.

PubMed

Grandin, Juan Carlos; Cremaschi, Fabian; Lombardo, Elva; Vitu, Ed; Dujovny, Manuel

2008-06-01

An optical character recognition (OCR) reading machine is a significant help for visually impaired patients. An OCR reading machine is used. This instrument can provide a significant help in order to improve the quality of life of patients with low vision or blindness.

Integrity Determination for Image Rendering Vision Navigation

DTIC Science & Technology

2016-03-01

identifying an object within a scene, tracking a SIFT feature between frames or matching images and/or features for stereo vision applications. This... object level, either in 2-D or 3-D, versus individual features. There is a breadth of information, largely from the machine vision community...matching or image rendering image correspondence approach is based upon using either 2-D or 3-D object models or templates to perform object detection or

Sensor fusion of phase measuring profilometry and stereo vision for three-dimensional inspection of electronic components assembled on printed circuit boards.

PubMed

Hong, Deokhwa; Lee, Hyunki; Kim, Min Young; Cho, Hyungsuck; Moon, Jeon Il

2009-07-20

Automatic optical inspection (AOI) for printed circuit board (PCB) assembly plays a very important role in modern electronics manufacturing industries. Well-developed inspection machines in each assembly process are required to ensure the manufacturing quality of the electronics products. However, generally almost all AOI machines are based on 2D image-analysis technology. In this paper, a 3D-measurement-method-based AOI system is proposed consisting of a phase shifting profilometer and a stereo vision system for assembled electronic components on a PCB after component mounting and the reflow process. In this system information from two visual systems is fused to extend the shape measurement range limited by 2pi phase ambiguity of the phase shifting profilometer, and finally to maintain fine measurement resolution and high accuracy of the phase shifting profilometer with the measurement range extended by the stereo vision. The main purpose is to overcome the low inspection reliability problem of 2D-based inspection machines by using 3D information of components. The 3D shape measurement results on PCB-mounted electronic components are shown and compared with results from contact and noncontact 3D measuring machines. Based on a series of experiments, the usefulness of the proposed sensor system and its fusion technique are discussed and analyzed in detail.

Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island

NASA Astrophysics Data System (ADS)

Heleno, S.; Matias, M.; Pina, P.; Sousa, A. J.

2015-09-01

A method for semi-automatic landslide detection, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a Support Vector Machine classifier on a GeoEye-1 multispectral image, sensed 3 days after the major damaging landslide event that occurred in Madeira island (20 February 2010), with a pre-event LIDAR Digital Elevation Model. The testing is developed in a 15 km2-wide study area, where 95 % of the landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier east facing-slopes.

Lithological mapping using Landsat 8 OLI and Terra ASTER multispectral data in the Bas Drâa inlier, Moroccan Anti Atlas

NASA Astrophysics Data System (ADS)

Adiri, Zakaria; Harti, Abderrazak El; Jellouli, Amine; Maacha, Lhou; Bachaoui, El Mostafa

2016-01-01

Lithological mapping is a fundamental step in various mineral prospecting studies because it forms the basis of the interpretation and validation of retrieved results. Therefore, this study exploited the multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat 8 Operational Land Imager (OLI) data in order to map lithological units in the Bas Drâa inlier, at the Moroccan Anti Atlas. This task was completed by using principal component analysis (PCA), band ratios (BR), and support vector machine (SVM) classification. Overall accuracy and the kappa coefficient of SVM based on ground truth in addition to the results of PCA and BR show an excellent correlation with the existing geological map of the study area. Consequently, the methodology proposed demonstrates a high potential of ASTER and Landsat 8 OLI data in lithological units discrimination.

The diagnostic performance of expert dermoscopists vs a computer-vision system on small-diameter melanomas.

PubMed

Friedman, Robert J; Gutkowicz-Krusin, Dina; Farber, Michele J; Warycha, Melanie; Schneider-Kels, Lori; Papastathis, Nicole; Mihm, Martin C; Googe, Paul; King, Roy; Prieto, Victor G; Kopf, Alfred W; Polsky, David; Rabinovitz, Harold; Oliviero, Margaret; Cognetta, Armand; Rigel, Darrell S; Marghoob, Ashfaq; Rivers, Jason; Johr, Robert; Grant-Kels, Jane M; Tsao, Hensin

2008-04-01

To evaluate the performance of dermoscopists in diagnosing small pigmented skin lesions (diameter

Vision-Based People Detection System for Heavy Machine Applications

PubMed Central

Fremont, Vincent; Bui, Manh Tuan; Boukerroui, Djamal; Letort, Pierrick

2016-01-01

This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance. PMID:26805838

Vision-Based People Detection System for Heavy Machine Applications.

PubMed

Fremont, Vincent; Bui, Manh Tuan; Boukerroui, Djamal; Letort, Pierrick

2016-01-20

This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance.

Developing a machine vision system for simultaneous prediction of freshness indicators based on tilapia (Oreochromis niloticus) pupil and gill color during storage at 4°C.

PubMed

Shi, Ce; Qian, Jianping; Han, Shuai; Fan, Beilei; Yang, Xinting; Wu, Xiaoming

2018-03-15

The study assessed the feasibility of developing a machine vision system based on pupil and gill color changes in tilapia for simultaneous prediction of total volatile basic nitrogen (TVB-N), thiobarbituric acid (TBA) and total viable counts (TVC) during storage at 4°C. The pupils and gills were chosen and color space conversion among RGB, HSI and L ∗ a ∗ b ∗ color spaces was performed automatically by an image processing algorithm. Multiple regression models were established by correlating pupil and gill color parameters with TVB-N, TVC and TBA (R 2 =0.989-0.999). However, assessment of freshness based on gill color is destructive and time-consuming because gill cover must be removed before images are captured. Finally, visualization maps of spoilage based on pupil color were achieved using image algorithms. The results show that assessment of tilapia pupil color parameters using machine vision can be used as a low-cost, on-line method for predicting freshness during 4°C storage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automatic decoding of facial movements reveals deceptive pain expressions

PubMed Central

Bartlett, Marian Stewart; Littlewort, Gwen C.; Frank, Mark G.; Lee, Kang

2014-01-01

Summary In highly social species such as humans, faces have evolved to convey rich information for social interaction, including expressions of emotions and pain [1–3]. Two motor pathways control facial movement [4–7]. A subcortical extrapyramidal motor system drives spontaneous facial expressions of felt emotions. A cortical pyramidal motor system controls voluntary facial expressions. The pyramidal system enables humans to simulate facial expressions of emotions not actually experienced. Their simulation is so successful that they can deceive most observers [8–11]. Machine vision may, however, be able to distinguish deceptive from genuine facial signals by identifying the subtle differences between pyramidally and extrapyramidally driven movements. Here we show that human observers could not discriminate real from faked expressions of pain better than chance, and after training, improved accuracy to a modest 55%. However a computer vision system that automatically measures facial movements and performs pattern recognition on those movements attained 85% accuracy. The machine system’s superiority is attributable to its ability to differentiate the dynamics of genuine from faked expressions. Thus by revealing the dynamics of facial action through machine vision systems, our approach has the potential to elucidate behavioral fingerprints of neural control systems involved in emotional signaling. PMID:24656830

Gradual Reduction in Sodium Content in Cooked Ham, with Corresponding Change in Sensorial Properties Measured by Sensory Evaluation and a Multimodal Machine Vision System

PubMed Central

Greiff, Kirsti; Mathiassen, John Reidar; Misimi, Ekrem; Hersleth, Margrethe; Aursand, Ida G.

2015-01-01

The European diet today generally contains too much sodium (Na+). A partial substitution of NaCl by KCl has shown to be a promising method for reducing sodium content. The aim of this work was to investigate the sensorial changes of cooked ham with reduced sodium content. Traditional sensorial evaluation and objective multimodal machine vision were used. The salt content in the hams was decreased from 3.4% to 1.4%, and 25% of the Na+ was replaced by K+. The salt reduction had highest influence on the sensory attributes salty taste, after taste, tenderness, hardness and color hue. The multimodal machine vision system showed changes in lightness, as a function of reduced salt content. Compared to the reference ham (3.4% salt), a replacement of Na+-ions by K+-ions of 25% gave no significant changes in WHC, moisture, pH, expressed moisture, the sensory profile attributes or the surface lightness and shininess. A further reduction of salt down to 1.7–1.4% salt, led to a decrease in WHC and an increase in expressible moisture. PMID:26422367

Gradual Reduction in Sodium Content in Cooked Ham, with Corresponding Change in Sensorial Properties Measured by Sensory Evaluation and a Multimodal Machine Vision System.

PubMed

Greiff, Kirsti; Mathiassen, John Reidar; Misimi, Ekrem; Hersleth, Margrethe; Aursand, Ida G

2015-01-01

The European diet today generally contains too much sodium (Na(+)). A partial substitution of NaCl by KCl has shown to be a promising method for reducing sodium content. The aim of this work was to investigate the sensorial changes of cooked ham with reduced sodium content. Traditional sensorial evaluation and objective multimodal machine vision were used. The salt content in the hams was decreased from 3.4% to 1.4%, and 25% of the Na(+) was replaced by K(+). The salt reduction had highest influence on the sensory attributes salty taste, after taste, tenderness, hardness and color hue. The multimodal machine vision system showed changes in lightness, as a function of reduced salt content. Compared to the reference ham (3.4% salt), a replacement of Na(+)-ions by K(+)-ions of 25% gave no significant changes in WHC, moisture, pH, expressed moisture, the sensory profile attributes or the surface lightness and shininess. A further reduction of salt down to 1.7-1.4% salt, led to a decrease in WHC and an increase in expressible moisture.

Triadic split-merge sampler

NASA Astrophysics Data System (ADS)

van Rossum, Anne C.; Lin, Hai Xiang; Dubbeldam, Johan; van der Herik, H. Jaap

2018-04-01

In machine vision typical heuristic methods to extract parameterized objects out of raw data points are the Hough transform and RANSAC. Bayesian models carry the promise to optimally extract such parameterized objects given a correct definition of the model and the type of noise at hand. A category of solvers for Bayesian models are Markov chain Monte Carlo methods. Naive implementations of MCMC methods suffer from slow convergence in machine vision due to the complexity of the parameter space. Towards this blocked Gibbs and split-merge samplers have been developed that assign multiple data points to clusters at once. In this paper we introduce a new split-merge sampler, the triadic split-merge sampler, that perform steps between two and three randomly chosen clusters. This has two advantages. First, it reduces the asymmetry between the split and merge steps. Second, it is able to propose a new cluster that is composed out of data points from two different clusters. Both advantages speed up convergence which we demonstrate on a line extraction problem. We show that the triadic split-merge sampler outperforms the conventional split-merge sampler. Although this new MCMC sampler is demonstrated in this machine vision context, its application extend to the very general domain of statistical inference.

Machine Vision Within The Framework Of Collective Neural Assemblies

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Knopf, George K.

1990-03-01

The proposed mechanism for designing a robust machine vision system is based on the dynamic activity generated by the various neural populations embedded in nervous tissue. It is postulated that a hierarchy of anatomically distinct tissue regions are involved in visual sensory information processing. Each region may be represented as a planar sheet of densely interconnected neural circuits. Spatially localized aggregates of these circuits represent collective neural assemblies. Four dynamically coupled neural populations are assumed to exist within each assembly. In this paper we present a state-variable model for a tissue sheet derived from empirical studies of population dynamics. Each population is modelled as a nonlinear second-order system. It is possible to emulate certain observed physiological and psychophysiological phenomena of biological vision by properly programming the interconnective gains . Important early visual phenomena such as temporal and spatial noise insensitivity, contrast sensitivity and edge enhancement will be discussed for a one-dimensional tissue model.

A robust embedded vision system feasible white balance algorithm

NASA Astrophysics Data System (ADS)

Wang, Yuan; Yu, Feihong

2018-01-01

White balance is a very important part of the color image processing pipeline. In order to meet the need of efficiency and accuracy in embedded machine vision processing system, an efficient and robust white balance algorithm combining several classical ones is proposed. The proposed algorithm mainly has three parts. Firstly, in order to guarantee higher efficiency, an initial parameter calculated from the statistics of R, G and B components from raw data is used to initialize the following iterative method. After that, the bilinear interpolation algorithm is utilized to implement demosaicing procedure. Finally, an adaptive step adjustable scheme is introduced to ensure the controllability and robustness of the algorithm. In order to verify the proposed algorithm's performance on embedded vision system, a smart camera based on IMX6 DualLite, IMX291 and XC6130 is designed. Extensive experiments on a large amount of images under different color temperatures and exposure conditions illustrate that the proposed white balance algorithm avoids color deviation problem effectively, achieves a good balance between efficiency and quality, and is suitable for embedded machine vision processing system.

Feature-based three-dimensional registration for repetitive geometry in machine vision

PubMed Central

Gong, Yuanzheng; Seibel, Eric J.

2016-01-01

As an important step in three-dimensional (3D) machine vision, 3D registration is a process of aligning two or multiple 3D point clouds that are collected from different perspectives together into a complete one. The most popular approach to register point clouds is to minimize the difference between these point clouds iteratively by Iterative Closest Point (ICP) algorithm. However, ICP does not work well for repetitive geometries. To solve this problem, a feature-based 3D registration algorithm is proposed to align the point clouds that are generated by vision-based 3D reconstruction. By utilizing texture information of the object and the robustness of image features, 3D correspondences can be retrieved so that the 3D registration of two point clouds is to solve a rigid transformation. The comparison of our method and different ICP algorithms demonstrates that our proposed algorithm is more accurate, efficient and robust for repetitive geometry registration. Moreover, this method can also be used to solve high depth uncertainty problem caused by little camera baseline in vision-based 3D reconstruction. PMID:28286703

Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images

PubMed Central

Ortega-Terol, Damian; Ballesteros, Rocio

2017-01-01

Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology. PMID:29036930

Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images.

PubMed

Ortega-Terol, Damian; Hernandez-Lopez, David; Ballesteros, Rocio; Gonzalez-Aguilera, Diego

2017-10-15

Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology.

Landmark navigation and autonomous landing approach with obstacle detection for aircraft

NASA Astrophysics Data System (ADS)

Fuerst, Simon; Werner, Stefan; Dickmanns, Dirk; Dickmanns, Ernst D.

1997-06-01

A machine perception system for aircraft and helicopters using multiple sensor data for state estimation is presented. By combining conventional aircraft sensor like gyros, accelerometers, artificial horizon, aerodynamic measuring devices and GPS with vision data taken by conventional CCD-cameras mounted on a pan and tilt platform, the position of the craft can be determined as well as the relative position to runways and natural landmarks. The vision data of natural landmarks are used to improve position estimates during autonomous missions. A built-in landmark management module decides which landmark should be focused on by the vision system, depending on the distance to the landmark and the aspect conditions. More complex landmarks like runways are modeled with different levels of detail that are activated dependent on range. A supervisor process compares vision data and GPS data to detect mistracking of the vision system e.g. due to poor visibility and tries to reinitialize the vision system or to set focus on another landmark available. During landing approach obstacles like trucks and airplanes can be detected on the runway. The system has been tested in real-time within a hardware-in-the-loop simulation. Simulated aircraft measurements corrupted by noise and other characteristic sensor errors have been fed into the machine perception system; the image processing module for relative state estimation was driven by computer generated imagery. Results from real-time simulation runs are given.

Improving the Accuracy of Cloud Detection Using Machine Learning

NASA Astrophysics Data System (ADS)

Craddock, M. E.; Alliss, R. J.; Mason, M.

2017-12-01

Cloud detection from geostationary satellite imagery has long been accomplished through multi-spectral channel differencing in comparison to the Earth's surface. The distinction of clear/cloud is then determined by comparing these differences to empirical thresholds. Using this methodology, the probability of detecting clouds exceeds 90% but performance varies seasonally, regionally and temporally. The Cloud Mask Generator (CMG) database developed under this effort, consists of 20 years of 4 km, 15minute clear/cloud images based on GOES data over CONUS and Hawaii. The algorithms to determine cloudy pixels in the imagery are based on well-known multi-spectral techniques and defined thresholds. These thresholds were produced by manually studying thousands of images and thousands of man-hours to determine the success and failure of the algorithms to fine tune the thresholds. This study aims to investigate the potential of improving cloud detection by using Random Forest (RF) ensemble classification. RF is the ideal methodology to employ for cloud detection as it runs efficiently on large datasets, is robust to outliers and noise and is able to deal with highly correlated predictors, such as multi-spectral satellite imagery. The RF code was developed using Python in about 4 weeks. The region of focus selected was Hawaii and includes the use of visible and infrared imagery, topography and multi-spectral image products as predictors. The development of the cloud detection technique is realized in three steps. First, tuning of the RF models is completed to identify the optimal values of the number of trees and number of predictors to employ for both day and night scenes. Second, the RF models are trained using the optimal number of trees and a select number of random predictors identified during the tuning phase. Lastly, the model is used to predict clouds for an independent time period than used during training and compared to truth, the CMG cloud mask. Initial results show 97% accuracy during the daytime, 94% accuracy at night, and 95% accuracy for all times. The total time to train, tune and test was approximately one week. The improved performance and reduced time to produce results is testament to improved computer technology and the use of machine learning as a more efficient and accurate methodology of cloud detection.

Some examples of image warping for low vision prosthesis

NASA Technical Reports Server (NTRS)

Juday, Richard D.; Loshin, David S.

1988-01-01

NASA has developed an image processor, the Programmable Remapper, for certain functions in machine vision. The Remapper performs a highly arbitrary geometric warping of an image at video rate. It might ultimately be shrunk to a size and cost that could allow its use in a low-vision prosthesis. Coordinate warpings have been developed for retinitis pigmentosa (tunnel vision) and for maculapathy (loss of central field) that are intended to make best use of the patient's remaining viable retina. The rationales and mathematics are presented for some warpings that we will try in clinical studies using the Remapper's prototype.

Application of earth resources technology satellite data to urban and regional planning: Test site, County of Los Angeles

NASA Technical Reports Server (NTRS)

Raje, S.; Mcknight, J.; Willoughby, G.; Economy, R. (Principal Investigator)

1974-01-01

The author has identified the following significant results. The County of Los Angeles photointerpreted ERTS film products to define problems of interest, coordinated ground truth over the complex test site including interfaces with secondary users as well as participated in on-line analyses of the GE multispectral information extraction systems. Interactive machine analyses were carried out, developing techniques and procedures as well as evaluating the outputs for community and regional planning. Extensive aircraft underflight coverage was provided that was valuable both in inputs preparation and outputs evaluation of the machine-aided analyses. One of the nonstandard ERTS images led to the discovery of a major new fault lineament on the northern slope of the Santa Monica Mountains.

PROCEDURES FOR ACCURATE PRODUCTION OF COLOR IMAGES FROM SATELLITE OR AIRCRAFT MULTISPECTRAL DIGITAL DATA.

USGS Publications Warehouse

Duval, Joseph S.

1985-01-01

Because the display and interpretation of satellite and aircraft remote-sensing data make extensive use of color film products, accurate reproduction of the color images is important. To achieve accurate color reproduction, the exposure and chemical processing of the film must be monitored and controlled. By using a combination of sensitometry, densitometry, and transfer functions that control film response curves, all of the different steps in the making of film images can be monitored and controlled. Because a sensitometer produces a calibrated exposure, the resulting step wedge can be used to monitor the chemical processing of the film. Step wedges put on film by image recording machines provide a means of monitoring the film exposure and color balance of the machines.

Multispectral image-fused head-tracked vision system (HTVS) for driving applications

NASA Astrophysics Data System (ADS)

Reese, Colin E.; Bender, Edward J.

2001-08-01

Current military thermal driver vision systems consist of a single Long Wave Infrared (LWIR) sensor mounted on a manually operated gimbal, which is normally locked forward during driving. The sensor video imagery is presented on a large area flat panel display for direct view. The Night Vision and Electronics Sensors Directorate and Kaiser Electronics are cooperatively working to develop a driver's Head Tracked Vision System (HTVS) which directs dual waveband sensors in a more natural head-slewed imaging mode. The HTVS consists of LWIR and image intensified sensors, a high-speed gimbal, a head mounted display, and a head tracker. The first prototype systems have been delivered and have undergone preliminary field trials to characterize the operational benefits of a head tracked sensor system for tactical military ground applications. This investigation will address the advantages of head tracked vs. fixed sensor systems regarding peripheral sightings of threats, road hazards, and nearby vehicles. An additional thrust will investigate the degree to which additive (A+B) fusion of LWIR and image intensified sensors enhances overall driving performance. Typically, LWIR sensors are better for detecting threats, while image intensified sensors provide more natural scene cues, such as shadows and texture. This investigation will examine the degree to which the fusion of these two sensors enhances the driver's overall situational awareness.

Hyperspectral imaging for presumptive identification of bacterial colonies on solid chromogenic culture media

NASA Astrophysics Data System (ADS)

Guillemot, Mathilde; Midahuen, Rony; Archeny, Delpine; Fulchiron, Corine; Montvernay, Regis; Perrin, Guillaume; Leroux, Denis F.

2016-04-01

BioMérieux is automating the microbiology laboratory in order to reduce cost (less manpower and consumables), to improve performance (increased sensitivity, machine algorithms) and to gain traceability through optimization of the clinical laboratory workflow. In this study, we evaluate the potential of Hyperspectral imaging (HSI) as a substitute to human visual observation when performing the task of microbiological culture interpretation. Microbial colonies from 19 strains subcategorized in 6 chromogenic classes were analyzed after a 24h-growth on a chromogenic culture medium (chromID® CPS Elite, bioMérieux, France). The HSI analysis was performed in the VNIR region (400-900 nm) using a linescan configuration. Using algorithms relying on Linear Spectral Unmixing, and using exclusively Diffuse Reflectance Spectra (DRS) as input data, we report interclass classification accuracies of 100% using a fully automatable approach and no use of morphological information. In order to eventually simplify the instrument, the performance of degraded DRS was also evaluated using only the most discriminant 14 spectral channels (a model for a multispectral approach) or 3 channels (model of a RGB image). The overall classification performance remains unchanged for our multispectral model but is degraded for the predicted RGB model, hints that a multispectral solution might bring the answer for an improved colony recognition.

Rapid detection of frozen-then-thawed minced beef using multispectral imaging and Fourier transform infrared spectroscopy.

PubMed

Ropodi, Athina I; Panagou, Efstathios Z; Nychas, George-John E

2018-01-01

In recent years, fraud detection has become a major priority for food authorities, as fraudulent practices can have various economic and safety consequences. This work explores ways of identifying frozen-then-thawed minced beef labeled as fresh in a rapid, large-scale and cost-effective way. For this reason, freshly-ground beef was purchased from seven separate shops at different times, divided in fifteen portions and placed in Petri dishes. Multi-spectral images and FTIR spectra of the first five were immediately acquired while the remaining were frozen (-20°C) and stored for 7 and 32days (5 samples for each time interval). Samples were thawed and subsequently subjected to similar data acquisition. In total, 105 multispectral images and FTIR spectra were collected which were further analyzed using partial least-squares discriminant analysis and support vector machines. Two meat batches (30 samples) were reserved for independent validation and the remaining five batches were divided in training and test set (75 samples). Results showed 100% overall correct classification for test and external validation MSI data, while FTIR data yielded 93.3 and 96.7% overall correct classification for FTIR test set and external validation set respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

A low-cost color vision system for automatic estimation of apple fruit orientation and maximum equatorial diameter

USDA-ARS?s Scientific Manuscript database

The overall objective of this research was to develop an in-field presorting and grading system to separate undersized and defective fruit from fresh market-grade apples. To achieve this goal, a cost-effective machine vision inspection prototype was built, which consisted of a low-cost color camera,...

Scanning System -- Technology Worth a Look

Treesearch

Philip A. Araman; Daniel L. Schmoldt; Richard W. Conners; D. Earl Kline

1995-01-01

In an effort to help automate the inspection for lumber defects, optical scanning systems are emerging as an alternative to the human eye. Although still in its infancy, scanning technology is being explored by machine companies and universities. This article was excerpted from "Machine Vision Systems for Grading and Processing Hardwood Lumber," by Philip...

Current Technologies and its Trends of Machine Vision in the Field of Security and Disaster Prevention

NASA Astrophysics Data System (ADS)

Hashimoto, Manabu; Fujino, Yozo

Image sensing technologies are expected as useful and effective way to suppress damages by criminals and disasters in highly safe and relieved society. In this paper, we describe current important subjects, required functions, technical trends, and a couple of real examples of developed system. As for the video surveillance, recognition of human trajectory and human behavior using image processing techniques are introduced with real examples about the violence detection for elevators. In the field of facility monitoring technologies as civil engineering, useful machine vision applications such as automatic detection of concrete cracks on walls of a building or recognition of crowded people on bridge for effective guidance in emergency are shown.

In-process fault detection for textile fabric production: onloom imaging

NASA Astrophysics Data System (ADS)

Neumann, Florian; Holtermann, Timm; Schneider, Dorian; Kulczycki, Ashley; Gries, Thomas; Aach, Til

2011-05-01

Constant and traceable high fabric quality is of high importance both for technical and for high-quality conventional fabrics. Usually, quality inspection is carried out by trained personal, whose detection rate and maximum period of concentration are limited. Low resolution automated fabric inspection machines using texture analysis were developed. Since 2003, systems for the in-process inspection on weaving machines ("onloom") are commercially available. With these defects can be detected, but not measured quantitative precisely. Most systems are also prone to inevitable machine vibrations. Feedback loops for fault prevention are not established. Technology has evolved since 2003: Camera and computer prices dropped, resolutions were enhanced, recording speeds increased. These are the preconditions for real-time processing of high-resolution images. So far, these new technological achievements are not used in textile fabric production. For efficient use, a measurement system must be integrated into the weaving process; new algorithms for defect detection and measurement must be developed. The goal of the joint project is the development of a modern machine vision system for nondestructive onloom fabric inspection. The system consists of a vibration-resistant machine integration, a high-resolution machine vision system, and new, reliable, and robust algorithms with quality database for defect documentation. The system is meant to detect, measure, and classify at least 80 % of economically relevant defects. Concepts for feedback loops into the weaving process will be pointed out.

Precise positioning method for multi-process connecting based on binocular vision

NASA Astrophysics Data System (ADS)

Liu, Wei; Ding, Lichao; Zhao, Kai; Li, Xiao; Wang, Ling; Jia, Zhenyuan

2016-01-01

With the rapid development of aviation and aerospace, the demand for metal coating parts such as antenna reflector, eddy-current sensor and signal transmitter, etc. is more and more urgent. Such parts with varied feature dimensions, complex three-dimensional structures, and high geometric accuracy are generally fabricated by the combination of different manufacturing technology. However, it is difficult to ensure the machining precision because of the connection error between different processing methods. Therefore, a precise positioning method is proposed based on binocular micro stereo vision in this paper. Firstly, a novel and efficient camera calibration method for stereoscopic microscope is presented to solve the problems of narrow view field, small depth of focus and too many nonlinear distortions. Secondly, the extraction algorithms for law curve and free curve are given, and the spatial position relationship between the micro vision system and the machining system is determined accurately. Thirdly, a precise positioning system based on micro stereovision is set up and then embedded in a CNC machining experiment platform. Finally, the verification experiment of the positioning accuracy is conducted and the experimental results indicated that the average errors of the proposed method in the X and Y directions are 2.250 μm and 1.777 μm, respectively.

A Vision-Aided 3D Path Teaching Method before Narrow Butt Joint Welding

PubMed Central

Zeng, Jinle; Chang, Baohua; Du, Dong; Peng, Guodong; Chang, Shuhe; Hong, Yuxiang; Wang, Li; Shan, Jiguo

2017-01-01

For better welding quality, accurate path teaching for actuators must be achieved before welding. Due to machining errors, assembly errors, deformations, etc., the actual groove position may be different from the predetermined path. Therefore, it is significant to recognize the actual groove position using machine vision methods and perform an accurate path teaching process. However, during the teaching process of a narrow butt joint, the existing machine vision methods may fail because of poor adaptability, low resolution, and lack of 3D information. This paper proposes a 3D path teaching method for narrow butt joint welding. This method obtains two kinds of visual information nearly at the same time, namely 2D pixel coordinates of the groove in uniform lighting condition and 3D point cloud data of the workpiece surface in cross-line laser lighting condition. The 3D position and pose between the welding torch and groove can be calculated after information fusion. The image resolution can reach 12.5 μm. Experiments are carried out at an actuator speed of 2300 mm/min and groove width of less than 0.1 mm. The results show that this method is suitable for groove recognition before narrow butt joint welding and can be applied in path teaching fields of 3D complex components. PMID:28492481

Applications of color machine vision in the agricultural and food industries

NASA Astrophysics Data System (ADS)

Zhang, Min; Ludas, Laszlo I.; Morgan, Mark T.; Krutz, Gary W.; Precetti, Cyrille J.

1999-01-01

Color is an important factor in Agricultural and the Food Industry. Agricultural or prepared food products are often grade by producers and consumers using color parameters. Color is used to estimate maturity, sort produce for defects, but also perform genetic screenings or make an aesthetic judgement. The task of sorting produce following a color scale is very complex, requires special illumination and training. Also, this task cannot be performed for long durations without fatigue and loss of accuracy. This paper describes a machine vision system designed to perform color classification in real-time. Applications for sorting a variety of agricultural products are included: e.g. seeds, meat, baked goods, plant and wood.FIrst the theory of color classification of agricultural and biological materials is introduced. Then, some tools for classifier development are presented. Finally, the implementation of the algorithm on real-time image processing hardware and example applications for industry is described. This paper also presented an image analysis algorithm and a prototype machine vision system which was developed for industry. This system will automatically locate the surface of some plants using digital camera and predict information such as size, potential value and type of this plant. The algorithm developed will be feasible for real-time identification in an industrial environment.

Real-time machine vision system using FPGA and soft-core processor

NASA Astrophysics Data System (ADS)

Malik, Abdul Waheed; Thörnberg, Benny; Meng, Xiaozhou; Imran, Muhammad

2012-06-01

This paper presents a machine vision system for real-time computation of distance and angle of a camera from reference points in the environment. Image pre-processing, component labeling and feature extraction modules were modeled at Register Transfer (RT) level and synthesized for implementation on field programmable gate arrays (FPGA). The extracted image component features were sent from the hardware modules to a soft-core processor, MicroBlaze, for computation of distance and angle. A CMOS imaging sensor operating at a clock frequency of 27MHz was used in our experiments to produce a video stream at the rate of 75 frames per second. Image component labeling and feature extraction modules were running in parallel having a total latency of 13ms. The MicroBlaze was interfaced with the component labeling and feature extraction modules through Fast Simplex Link (FSL). The latency for computing distance and angle of camera from the reference points was measured to be 2ms on the MicroBlaze, running at 100 MHz clock frequency. In this paper, we present the performance analysis, device utilization and power consumption for the designed system. The FPGA based machine vision system that we propose has high frame speed, low latency and a power consumption that is much lower compared to commercially available smart camera solutions.

A Vision-Aided 3D Path Teaching Method before Narrow Butt Joint Welding.

PubMed

Zeng, Jinle; Chang, Baohua; Du, Dong; Peng, Guodong; Chang, Shuhe; Hong, Yuxiang; Wang, Li; Shan, Jiguo

2017-05-11

For better welding quality, accurate path teaching for actuators must be achieved before welding. Due to machining errors, assembly errors, deformations, etc., the actual groove position may be different from the predetermined path. Therefore, it is significant to recognize the actual groove position using machine vision methods and perform an accurate path teaching process. However, during the teaching process of a narrow butt joint, the existing machine vision methods may fail because of poor adaptability, low resolution, and lack of 3D information. This paper proposes a 3D path teaching method for narrow butt joint welding. This method obtains two kinds of visual information nearly at the same time, namely 2D pixel coordinates of the groove in uniform lighting condition and 3D point cloud data of the workpiece surface in cross-line laser lighting condition. The 3D position and pose between the welding torch and groove can be calculated after information fusion. The image resolution can reach 12.5 μm. Experiments are carried out at an actuator speed of 2300 mm/min and groove width of less than 0.1 mm. The results show that this method is suitable for groove recognition before narrow butt joint welding and can be applied in path teaching fields of 3D complex components.

Toward open set recognition.

PubMed

Scheirer, Walter J; de Rezende Rocha, Anderson; Sapkota, Archana; Boult, Terrance E

2013-07-01

To date, almost all experimental evaluations of machine learning-based recognition algorithms in computer vision have taken the form of "closed set" recognition, whereby all testing classes are known at training time. A more realistic scenario for vision applications is "open set" recognition, where incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. This paper explores the nature of open set recognition and formalizes its definition as a constrained minimization problem. The open set recognition problem is not well addressed by existing algorithms because it requires strong generalization. As a step toward a solution, we introduce a novel "1-vs-set machine," which sculpts a decision space from the marginal distances of a 1-class or binary SVM with a linear kernel. This methodology applies to several different applications in computer vision where open set recognition is a challenging problem, including object recognition and face verification. We consider both in this work, with large scale cross-dataset experiments performed over the Caltech 256 and ImageNet sets, as well as face matching experiments performed over the Labeled Faces in the Wild set. The experiments highlight the effectiveness of machines adapted for open set evaluation compared to existing 1-class and binary SVMs for the same tasks.

Machine vision method for online surface inspection of easy open can ends

NASA Astrophysics Data System (ADS)

Mariño, Perfecto; Pastoriza, Vicente; Santamaría, Miguel

2006-10-01

Easy open can end manufacturing process in the food canning sector currently makes use of a manual, non-destructive testing procedure to guarantee can end repair coating quality. This surface inspection is based on a visual inspection made by human inspectors. Due to the high production rate (100 to 500 ends per minute) only a small part of each lot is verified (statistical sampling), then an automatic, online, inspection system, based on machine vision, has been developed to improve this quality control. The inspection system uses a fuzzy model to make the acceptance/rejection decision for each can end from the information obtained by the vision sensor. In this work, the inspection method is presented. This surface inspection system checks the total production, classifies the ends in agreement with an expert human inspector, supplies interpretability to the operators in order to find out the failure causes and reduce mean time to repair during failures, and allows to modify the minimum can end repair coating quality.

Predicting pork loin intramuscular fat using computer vision system.

PubMed

Liu, J-H; Sun, X; Young, J M; Bachmeier, L A; Newman, D J

2018-09-01

The objective of this study was to investigate the ability of computer vision system to predict pork intramuscular fat percentage (IMF%). Center-cut loin samples (n = 85) were trimmed of subcutaneous fat and connective tissue. Images were acquired and pixels were segregated to estimate image IMF% and 18 image color features for each image. Subjective IMF% was determined by a trained grader. Ether extract IMF% was calculated using ether extract method. Image color features and image IMF% were used as predictors for stepwise regression and support vector machine models. Results showed that subjective IMF% had a correlation of 0.81 with ether extract IMF% while the image IMF% had a 0.66 correlation with ether extract IMF%. Accuracy rates for regression models were 0.63 for stepwise and 0.75 for support vector machine. Although subjective IMF% has shown to have better prediction, results from computer vision system demonstrates the potential of being used as a tool in predicting pork IMF% in the future. Copyright © 2018 Elsevier Ltd. All rights reserved.

Experimental results in autonomous landing approaches by dynamic machine vision

NASA Astrophysics Data System (ADS)

Dickmanns, Ernst D.; Werner, Stefan; Kraus, S.; Schell, R.

1994-07-01

The 4-D approach to dynamic machine vision, exploiting full spatio-temporal models of the process to be controlled, has been applied to on board autonomous landing approaches of aircraft. Aside from image sequence processing, for which it was developed initially, it is also used for data fusion from a range of sensors. By prediction error feedback an internal representation of the aircraft state relative to the runway in 3-D space and time is servo- maintained in the interpretation process, from which the control applications required are being derived. The validity and efficiency of the approach have been proven both in hardware- in-the-loop simulations and in flight experiments with a twin turboprop aircraft Do128 under perturbations from cross winds and wind gusts. The software package has been ported to `C' and onto a new transputer image processing platform; the system has been expanded for bifocal vision with two cameras of different focal length mounted fixed relative to each other on a two-axes platform for viewing direction control.

Autonomous proximity operations using machine vision for trajectory control and pose estimation

NASA Technical Reports Server (NTRS)

Cleghorn, Timothy F.; Sternberg, Stanley R.

1991-01-01

A machine vision algorithm was developed which permits guidance control to be maintained during autonomous proximity operations. At present this algorithm exists as a simulation, running upon an 80386 based personal computer, using a ModelMATE CAD package to render the target vehicle. However, the algorithm is sufficiently simple, so that following off-line training on a known target vehicle, it should run in real time with existing vision hardware. The basis of the algorithm is a sequence of single camera images of the target vehicle, upon which radial transforms were performed. Selected points of the resulting radial signatures are fed through a decision tree, to determine whether the signature matches that of the known reference signatures for a particular view of the target. Based upon recognized scenes, the position of the maneuvering vehicle with respect to the target vehicles can be calculated, and adjustments made in the former's trajectory. In addition, the pose and spin rates of the target satellite can be estimated using this method.

Proceedings of the 1986 IEEE international conference on systems, man and cybernetics

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

Not Available

1986-01-01

This book presents the papers given at a conference on man-machine systems. Topics considered at the conference included neural model-based cognitive theory and engineering, user interfaces, adaptive and learning systems, human interaction with robotics, decision making, the testing and evaluation of expert systems, software development, international conflict resolution, intelligent interfaces, automation in man-machine system design aiding, knowledge acquisition in expert systems, advanced architectures for artificial intelligence, pattern recognition, knowledge bases, and machine vision.

Development of a Machine-Vision System for Recording of Force Calibration Data

NASA Astrophysics Data System (ADS)

Heamawatanachai, Sumet; Chaemthet, Kittipong; Changpan, Tawat

This paper presents the development of a new system for recording of force calibration data using machine vision technology. Real time camera and computer system were used to capture images of the reading from the instruments during calibration. Then, the measurement images were transformed and translated to numerical data using optical character recognition (OCR) technique. These numerical data along with raw images were automatically saved to memories as the calibration database files. With this new system, the human error of recording would be eliminated. The verification experiments were done by using this system for recording the measurement results from an amplifier (DMP 40) with load cell (HBM-Z30-10kN). The NIMT's 100-kN deadweight force standard machine (DWM-100kN) was used to generate test forces. The experiments setup were done in 3 categories; 1) dynamics condition (record during load changing), 2) statics condition (record during fix load), and 3) full calibration experiments in accordance with ISO 376:2011. The captured images from dynamics condition experiment gave >94% without overlapping of number. The results from statics condition experiment were >98% images without overlapping. All measurement images without overlapping were translated to number by the developed program with 100% accuracy. The full calibration experiments also gave 100% accurate results. Moreover, in case of incorrect translation of any result, it is also possible to trace back to the raw calibration image to check and correct it. Therefore, this machine-vision-based system and program should be appropriate for recording of force calibration data.

Thoughts turned into high-level commands: Proof-of-concept study of a vision-guided robot arm driven by functional MRI (fMRI) signals.

PubMed

Minati, Ludovico; Nigri, Anna; Rosazza, Cristina; Bruzzone, Maria Grazia

2012-06-01

Previous studies have demonstrated the possibility of using functional MRI to control a robot arm through a brain-machine interface by directly coupling haemodynamic activity in the sensory-motor cortex to the position of two axes. Here, we extend this work by implementing interaction at a more abstract level, whereby imagined actions deliver structured commands to a robot arm guided by a machine vision system. Rather than extracting signals from a small number of pre-selected regions, the proposed system adaptively determines at individual level how to map representative brain areas to the input nodes of a classifier network. In this initial study, a median action recognition accuracy of 90% was attained on five volunteers performing a game consisting of collecting randomly positioned coloured pawns and placing them into cups. The "pawn" and "cup" instructions were imparted through four mental imaginery tasks, linked to robot arm actions by a state machine. With the current implementation in MatLab language the median action recognition time was 24.3s and the robot execution time was 17.7s. We demonstrate the notion of combining haemodynamic brain-machine interfacing with computer vision to implement interaction at the level of high-level commands rather than individual movements, which may find application in future fMRI approaches relevant to brain-lesioned patients, and provide source code supporting further work on larger command sets and real-time processing. Copyright © 2012 IPEM. Published by Elsevier Ltd. All rights reserved.

Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island

NASA Astrophysics Data System (ADS)

Heleno, Sandra; Matias, Magda; Pina, Pedro; Sousa, António Jorge

2016-04-01

A method for semiautomated landslide detection and mapping, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a support vector machine classifier and is tested using a GeoEye-1 multispectral image, sensed 3 days after a major damaging landslide event that occurred on Madeira Island (20 February 2010), and a pre-event lidar digital terrain model. The testing is developed in a 15 km2 wide study area, where 95 % of the number of landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area, with commission errors below 26 % and omission errors below 24 %. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier, east-facing slopes.

Using WorldView-2 Imagery to Track Flooding in Thailand in a Multi-Asset Sensorweb

NASA Technical Reports Server (NTRS)

McLaren, David; Doubleday, Joshua; Chien, Steve

2012-01-01

For the flooding seasons of 2011-2012 multiple space assets were used in a "sensorweb" to track major flooding in Thailand. Worldview-2 multispectral data was used in this effort and provided extremely high spatial resolution (2m / pixel) multispectral (8 bands at 0.45-1.05 micrometer spectra) data from which mostly automated workflows derived surface water extent and volumetric water information for use by a range of NGO and national authorities. We first describe how Worldview-2 and its data was integrated into the overall flood tracking sensorweb. We next describe the use of Support Vector Machine learning techniques that were used to derive surface water extent classifiers. Then we describe the fusion of surface water extent and digital elevation map (DEM) data to derive volumetric water calculations. Finally we discuss key future work such as speeding up the workflows and automating the data registration process (the only portion of the workflow requiring human input).

Potential application of machine vision technology to saffron (Crocus sativus L.) quality characterization.

PubMed

Kiani, Sajad; Minaei, Saeid

2016-12-01

Saffron quality characterization is an important issue in the food industry and of interest to the consumers. This paper proposes an expert system based on the application of machine vision technology for characterization of saffron and shows how it can be employed in practical usage. There is a correlation between saffron color and its geographic location of production and some chemical attributes which could be properly used for characterization of saffron quality and freshness. This may be accomplished by employing image processing techniques coupled with multivariate data analysis for quantification of saffron properties. Expert algorithms can be made available for prediction of saffron characteristics such as color as well as for product classification. Copyright © 2016. Published by Elsevier Ltd.

Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress

PubMed Central

Fu, Longwen; Liu, Zuoyi

2018-01-01

Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented. PMID:29849612

A Developmental Approach to Machine Learning?

PubMed Central

Smith, Linda B.; Slone, Lauren K.

2017-01-01

Visual learning depends on both the algorithms and the training material. This essay considers the natural statistics of infant- and toddler-egocentric vision. These natural training sets for human visual object recognition are very different from the training data fed into machine vision systems. Rather than equal experiences with all kinds of things, toddlers experience extremely skewed distributions with many repeated occurrences of a very few things. And though highly variable when considered as a whole, individual views of things are experienced in a specific order – with slow, smooth visual changes moment-to-moment, and developmentally ordered transitions in scene content. We propose that the skewed, ordered, biased visual experiences of infants and toddlers are the training data that allow human learners to develop a way to recognize everything, both the pervasively present entities and the rarely encountered ones. The joint consideration of real-world statistics for learning by researchers of human and machine learning seems likely to bring advances in both disciplines. PMID:29259573

A machine vision system for micro-EDM based on linux

NASA Astrophysics Data System (ADS)

Guo, Rui; Zhao, Wansheng; Li, Gang; Li, Zhiyong; Zhang, Yong

2006-11-01

Due to the high precision and good surface quality that it can give, Electrical Discharge Machining (EDM) is potentially an important process for the fabrication of micro-tools and micro-components. However, a number of issues remain unsolved before micro-EDM becomes a reliable process with repeatable results. To deal with the difficulties in micro electrodes on-line fabrication and tool wear compensation, a micro-EDM machine vision system is developed with a Charge Coupled Device (CCD) camera, with an optical resolution of 1.61μm and an overall magnification of 113~729. Based on the Linux operating system, an image capturing program is developed with the V4L2 API, and an image processing program is exploited by using OpenCV. The contour of micro electrodes can be extracted by means of the Canny edge detector. Through the system calibration, the micro electrodes diameter can be measured on-line. Experiments have been carried out to prove its performance, and the reasons of measurement error are also analyzed.

Development of a Computer Vision Technology for the Forest Products Manufacturing Industry

Treesearch

D. Earl Kline; Richard Conners; Philip A. Araman

1992-01-01

The goal of this research is to create an automated processing/grading system for hardwood lumber that will be of use to the forest products industry. The objective of creating a full scale machine vision prototype for inspecting hardwood lumber will become a reality in calendar year 1992. Space for the full scale prototype has been created at the Brooks Forest...

High-Level Vision and Planning Workshop Proceedings

DTIC Science & Technology

1989-08-01

Correspondence in Line Drawings of Multiple View-. In Proc. of 8th Intern. Joint Conf. on Artificial intellignece . 1983. [63] Tomiyasu, K. Tutorial...joint U.S.-Israeli workshop on artificial intelligence are provided in this Institute for Defense Analyses document. This document is based on a broad...participants is provided along with applicable references for individual papers. 14. SUBJECT TERMS 15. NUMBER OF PAGES Artificial Intelligence; Machine Vision

Teaching Translation and Interpreting 2: Insights, Aims, Visions. [Selection of] Papers from the Second Language International Conference (Elsinore, Denmark, June 4-6, 1993).

ERIC Educational Resources Information Center

Dollerup, Cay, Ed.; Lindegaard, Annette, Ed.

This selection of papers starts with insights into multi- and plurilingual settings, then proceeds to discussions of aims for practical work with students, and ends with visions of future developments within translation for the mass media and the impact of machine translation. Papers are: "Interpreting at the European Commission";…

A multimodal 3D framework for fire characteristics estimation

NASA Astrophysics Data System (ADS)

Toulouse, T.; Rossi, L.; Akhloufi, M. A.; Pieri, A.; Maldague, X.

2018-02-01

In the last decade we have witnessed an increasing interest in using computer vision and image processing in forest fire research. Image processing techniques have been successfully used in different fire analysis areas such as early detection, monitoring, modeling and fire front characteristics estimation. While the majority of the work deals with the use of 2D visible spectrum images, recent work has introduced the use of 3D vision in this field. This work proposes a new multimodal vision framework permitting the extraction of the three-dimensional geometrical characteristics of fires captured by multiple 3D vision systems. The 3D system is a multispectral stereo system operating in both the visible and near-infrared (NIR) spectral bands. The framework supports the use of multiple stereo pairs positioned so as to capture complementary views of the fire front during its propagation. Multimodal registration is conducted using the captured views in order to build a complete 3D model of the fire front. The registration process is achieved using multisensory fusion based on visual data (2D and NIR images), GPS positions and IMU inertial data. Experiments were conducted outdoors in order to show the performance of the proposed framework. The obtained results are promising and show the potential of using the proposed framework in operational scenarios for wildland fire research and as a decision management system in fighting.

Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants.

PubMed

Navarro, Pedro J; Pérez, Fernando; Weiss, Julia; Egea-Cortines, Marcos

2016-05-05

Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML) algorithms and computer vision intended to solve the automatic phenotype data analysis in plant material. We developed a growth-chamber able to accommodate species of various sizes. Night image acquisition requires near infrared lightning. For the ML process, we tested three different algorithms: k-nearest neighbour (kNN), Naive Bayes Classifier (NBC), and Support Vector Machine. Each ML algorithm was executed with different kernel functions and they were trained with raw data and two types of data normalisation. Different metrics were computed to determine the optimal configuration of the machine learning algorithms. We obtained a performance of 99.31% in kNN for RGB images and a 99.34% in SVM for NIR. Our results show that ML techniques can speed up phenomic data analysis. Furthermore, both RGB and NIR images can be segmented successfully but may require different ML algorithms for segmentation.

Evolving land cover classification algorithms for multispectral and multitemporal imagery

NASA Astrophysics Data System (ADS)

Brumby, Steven P.; Theiler, James P.; Bloch, Jeffrey J.; Harvey, Neal R.; Perkins, Simon J.; Szymanski, John J.; Young, Aaron C.

2002-01-01

The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multi-spectral and multi-temporal imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from before, during, and after the wildfire. Using an existing land cover classification based on a 1992 Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, and an algorithm to mask out clouds and cloud shadows. We report preliminary results of combining individual classification results using a K-means clustering approach. The details of our evolved classification are compared to the manually produced land-cover classification.

Some Examples Of Image Warping For Low Vision Prosthesis

NASA Astrophysics Data System (ADS)

Juday, Richard D.; Loshin, David S.

1988-08-01

NASA and Texas Instruments have developed an image processor, the Programmable Remapper 1, for certain functions in machine vision. The Remapper performs a highly arbitrary geometric warping of an image at video rate. It might ultimately be shrunk to a size and cost that could allow its use in a low-vision prosthesis. We have developed coordinate warpings for retinitis pigmentosa (tunnel vision) and for maculapathy (loss of central field) that are intended to make best use of the patient's remaining viable retina. The rationales and mathematics are presented for some warpings that we will try in clinical studies using the Remapper's prototype. (Recorded video imagery was shown at the conference for the maculapathy remapping.

Methodology for creating dedicated machine and algorithm on sunflower counting

NASA Astrophysics Data System (ADS)

Muracciole, Vincent; Plainchault, Patrick; Mannino, Maria-Rosaria; Bertrand, Dominique; Vigouroux, Bertrand

2007-09-01

In order to sell grain lots in European countries, seed industries need a government certification. This certification requests purity testing, seed counting in order to quantify specified seed species and other impurities in lots, and germination testing. These analyses are carried out within the framework of international trade according to the methods of the International Seed Testing Association. Presently these different analyses are still achieved manually by skilled operators. Previous works have already shown that seeds can be characterized by around 110 visual features (morphology, colour, texture), and thus have presented several identification algorithms. Until now, most of the works in this domain are computer based. The approach presented in this article is based on the design of dedicated electronic vision machine aimed to identify and sort seeds. This machine is composed of a FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor) and a PC bearing the GUI (Human Machine Interface) of the system. Its operation relies on the stroboscopic image acquisition of a seed falling in front of a camera. A first machine was designed according to this approach, in order to simulate all the vision chain (image acquisition, feature extraction, identification) under the Matlab environment. In order to perform this task into dedicated hardware, all these algorithms were developed without the use of the Matlab toolbox. The objective of this article is to present a design methodology for a special purpose identification algorithm based on distance between groups into dedicated hardware machine for seed counting.

Integrated Imaging and Vision Techniques for Industrial Inspection: A Special Issue on Machine Vision and Applications

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

Liu, Zheng; Ukida, H.; Ramuhalli, Pradeep

2010-06-05

Imaging- and vision-based techniques play an important role in industrial inspection. The sophistication of the techniques assures high- quality performance of the manufacturing process through precise positioning, online monitoring, and real-time classification. Advanced systems incorporating multiple imaging and/or vision modalities provide robust solutions to complex situations and problems in industrial applications. A diverse range of industries, including aerospace, automotive, electronics, pharmaceutical, biomedical, semiconductor, and food/beverage, etc., have benefited from recent advances in multi-modal imaging, data fusion, and computer vision technologies. Many of the open problems in this context are in the general area of image analysis methodologies (preferably in anmore » automated fashion). This editorial article introduces a special issue of this journal highlighting recent advances and demonstrating the successful applications of integrated imaging and vision technologies in industrial inspection.« less

Nondestructive and rapid detection of potato black heart based on machine vision technology

NASA Astrophysics Data System (ADS)

Tian, Fang; Peng, Yankun; Wei, Wensong

2016-05-01

Potatoes are one of the major food crops in the world. Potato black heart is a kind of defect that the surface is intact while the tissues in skin become black. This kind of potato has lost the edibleness, but it's difficult to be detected with conventional methods. A nondestructive detection system based on the machine vision technology was proposed in this study to distinguish the normal and black heart of potatoes according to the different transmittance of them. The detection system was equipped with a monochrome CCD camera, LED light sources for transmitted illumination and a computer. Firstly, the transmission images of normal and black heart potatoes were taken by the detection system. Then the images were processed by algorithm written with VC++. As the transmitted light intensity was influenced by the radial dimension of the potato samples, the relationship between the grayscale value and the potato radial dimension was acquired by analyzing the grayscale value changing rule of the transmission image. Then proper judging condition was confirmed to distinguish the normal and black heart of potatoes after image preprocessing. The results showed that the nondestructive system built coupled with the processing methods was accessible for the detection of potato black heart at a considerable accuracy rate. The transmission detection technique based on machine vision is nondestructive and feasible to realize the detection of potato black heart.

USAF Summer Faculty Research Program. 1981 Research Reports. Volume I.

DTIC Science & Technology

1981-10-01

Kent, OH 44242 (216) 672-2816 Dr. Martin D. Altschuler Degree: PhD, Physics and Astronomy, 1964 Associate Professor Specialty: Robot Vision, Surface...line inspection and control, computer- aided manufacturing, robot vision, mapping of machine parts and castings, etc. The technique we developed...posture, reduced healing time and bacteria level, and improved capacity for work endurance and efficiency. 1 ,2𔃽 Federal agencies, such as the FDA and

Insect vision as model for machine vision

NASA Astrophysics Data System (ADS)

Osorio, D.; Sobey, Peter J.

1992-11-01

The neural architecture, neurophysiology and behavioral abilities of insect vision are described, and compared with that of mammals. Insects have a hardwired neural architecture of highly differentiated neurons, quite different from the cerebral cortex, yet their behavioral abilities are in important respects similar to those of mammals. These observations challenge the view that the key to the power of biological neural computation is distributed processing by a plastic, highly interconnected, network of individually undifferentiated and unreliable neurons that has been a dominant picture of biological computation since Pitts and McCulloch's seminal work in the 1940's.

University NanoSat Program: AggieSat3

DTIC Science & Technology

2009-06-01

commercially available product for stereo machine vision developed by Point Grey Research. The current binocular BumbleBee2® system incorporates two...and Fellow of the American Society of Mechanical Engineers (ASME) in 1997. She was awarded the 2007 J. Leland "Lee" Atwood Award from the ASEE...AggieSat2 satellite programs. Additional experience gained in the area of drawing standards, machining capabilities, solid modeling, safety

Identification Of Cells With A Compact Microscope Imaging System With Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2006-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking mic?oscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

Man-machine interactive imaging and data processing using high-speed digital mass storage

NASA Technical Reports Server (NTRS)

Alsberg, H.; Nathan, R.

1975-01-01

The role of vision in teleoperation has been recognized as an important element in the man-machine control loop. In most applications of remote manipulation, direct vision cannot be used. To overcome this handicap, the human operator's control capabilities are augmented by a television system. This medium provides a practical and useful link between workspace and the control station from which the operator perform his tasks. Human performance deteriorates when the images are degraded as a result of instrumental and transmission limitations. Image enhancement is used to bring out selected qualities in a picture to increase the perception of the observer. A general purpose digital computer, an extensive special purpose software system is used to perform an almost unlimited repertoire of processing operations.

Tracking of Cells with a Compact Microscope Imaging System with Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2007-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously

Tracking of cells with a compact microscope imaging system with intelligent controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2007-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to auto-focus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

Operation of a Cartesian Robotic System in a Compact Microscope with Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2006-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

Machine vision extracted plant movement for early detection of plant water stress.

PubMed

Kacira, M; Ling, P P; Short, T H

2002-01-01

A methodology was established for early, non-contact, and quantitative detection of plant water stress with machine vision extracted plant features. Top-projected canopy area (TPCA) of the plants was extracted from plant images using image-processing techniques. Water stress induced plant movement was decoupled from plant diurnal movement and plant growth using coefficient of relative variation of TPCA (CRV[TPCA)] and was found to be an effective marker for water stress detection. Threshold value of CRV(TPCA) as an indicator of water stress was determined by a parametric approach. The effectiveness of the sensing technique was evaluated against the timing of stress detection by an operator. Results of this study suggested that plant water stress detection using projected canopy area based features of the plants was feasible.

Local intensity adaptive image coding

NASA Technical Reports Server (NTRS)

Huck, Friedrich O.

1989-01-01

The objective of preprocessing for machine vision is to extract intrinsic target properties. The most important properties ordinarily are structure and reflectance. Illumination in space, however, is a significant problem as the extreme range of light intensity, stretching from deep shadow to highly reflective surfaces in direct sunlight, impairs the effectiveness of standard approaches to machine vision. To overcome this critical constraint, an image coding scheme is being investigated which combines local intensity adaptivity, image enhancement, and data compression. It is very effective under the highly variant illumination that can exist within a single frame or field of view, and it is very robust to noise at low illuminations. Some of the theory and salient features of the coding scheme are reviewed. Its performance is characterized in a simulated space application, the research and development activities are described.

Integration of USB and firewire cameras in machine vision applications

NASA Astrophysics Data System (ADS)

Smith, Timothy E.; Britton, Douglas F.; Daley, Wayne D.; Carey, Richard

1999-08-01

Digital cameras have been around for many years, but a new breed of consumer market cameras is hitting the main stream. By using these devices, system designers and integrators will be well posited to take advantage of technological advances developed to support multimedia and imaging applications on the PC platform. Having these new cameras on the consumer market means lower cost, but it does not necessarily guarantee ease of integration. There are many issues that need to be accounted for like image quality, maintainable frame rates, image size and resolution, supported operating system, and ease of software integration. This paper will describe briefly a couple of the consumer digital standards, and then discuss some of the advantages and pitfalls of integrating both USB and Firewire cameras into computer/machine vision applications.

Recovery of a spectrum based on a compressive-sensing algorithm with weighted principal component analysis

NASA Astrophysics Data System (ADS)

Dafu, Shen; Leihong, Zhang; Dong, Liang; Bei, Li; Yi, Kang

2017-07-01

The purpose of this study is to improve the reconstruction precision and better copy the color of spectral image surfaces. A new spectral reflectance reconstruction algorithm based on an iterative threshold combined with weighted principal component space is presented in this paper, and the principal component with weighted visual features is the sparse basis. Different numbers of color cards are selected as the training samples, a multispectral image is the testing sample, and the color differences in the reconstructions are compared. The channel response value is obtained by a Mega Vision high-accuracy, multi-channel imaging system. The results show that spectral reconstruction based on weighted principal component space is superior in performance to that based on traditional principal component space. Therefore, the color difference obtained using the compressive-sensing algorithm with weighted principal component analysis is less than that obtained using the algorithm with traditional principal component analysis, and better reconstructed color consistency with human eye vision is achieved.

Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery

NASA Astrophysics Data System (ADS)

Castillo, Jose Alan A.; Apan, Armando A.; Maraseni, Tek N.; Salmo, Severino G.

2017-12-01

The recent launch of the Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity for land-based biomass mapping and monitoring especially in the tropics where deforestation is highest. Yet, unlike in agriculture and inland land uses, the use of Sentinel imagery has not been evaluated for biomass retrieval in mangrove forest and the non-forest land uses that replaced mangroves. In this study, we evaluated the ability of Sentinel imagery for the retrieval and predictive mapping of above-ground biomass of mangroves and their replacement land uses. We used Sentinel SAR and multispectral imagery to develop biomass prediction models through the conventional linear regression and novel Machine Learning algorithms. We developed models each from SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass. In contrast, the model which utilised optical bands had the lowest accuracy. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient to low vegetation cover non-forest replacement land uses such as abandoned aquaculture pond, cleared mangrove and abandoned salt pond. These models had 0.82-0.83 correlation/agreement of observed and predicted value, and root mean square error of 27.8-28.5 Mg ha-1. Among the Sentinel-2 multispectral bands, the red and red edge bands (bands 4, 5 and 7), combined with elevation data, were the best variable set combination for biomass prediction. The red edge-based Inverted Red-Edge Chlorophyll Index had the highest prediction accuracy among the vegetation indices. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data. The study demonstrates encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery.

From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information?

NASA Astrophysics Data System (ADS)

Meyer, Hanna; Lehnert, Lukas W.; Wang, Yun; Reudenbach, Christoph; Nauss, Thomas; Bendix, Jörg

2017-03-01

Though the relevance of pasture degradation on the Qinghai-Tibet Plateau (QTP) is widely postulated, its extent is still unknown. Due to the enormous spatial extent, remote sensing provides the only possibility to investigate pasture degradation via frequently used proxies such as vegetation cover and aboveground biomass (AGB). However, unified remote sensing approaches are still lacking. This study tests the applicability of hyper- and multispectral in situ measurements to map vegetation cover and AGB on regional scales. Using machine learning techniques, it is tested whether the full hyperspectral information is needed or if multispectral information is sufficient to accurately estimate pasture degradation proxies. To regionalize pasture degradation proxies, the transferability of the locally derived ML-models to high resolution multispectral satellite data is assessed. 1183 hyperspectral measurements and vegetation records were performed at 18 locations on the QTP. Random Forests models with recursive feature selection were trained to estimate vegetation cover and AGB using narrow-band indices (NBI) as predictors. Separate models were calculated using NBI from hyperspectral data as well as from the same data resampled to WorldView-2, QuickBird and RapidEye channels. The hyperspectral results were compared to the multispectral results. Finally, the models were applied to satellite data to map vegetation cover and AGB on a regional scale. Vegetation cover was accurately predicted by Random Forest if hyperspectral measurements were used (cross validated R2 = 0.89). In contrast, errors in AGB estimations were considerably higher (cross validated R2 = 0.32). Only small differences in accuracy were observed between the models based on hyperspectral compared to multispectral data. The application of the models to satellite images generally resulted in an increase of the estimation error. Though this reflects the challenge of applying in situ measurements to satellite data, the results still show a high potential to map pasture degradation proxies on the QTP. Thus, this study presents robust methodology to remotely detect and monitor pasture degradation at high spatial resolutions.

Intelligent image processing for machine safety

NASA Astrophysics Data System (ADS)

Harvey, Dennis N.

1994-10-01

This paper describes the use of intelligent image processing as a machine guarding technology. One or more color, linear array cameras are positioned to view the critical region(s) around a machine tool or other piece of manufacturing equipment. The image data is processed to provide indicators of conditions dangerous to the equipment via color content, shape content, and motion content. The data from these analyses is then sent to a threat evaluator. The purpose of the evaluator is to determine if a potentially machine-damaging condition exists based on the analyses of color, shape, and motion, and on `knowledge' of the specific environment of the machine. The threat evaluator employs fuzzy logic as a means of dealing with uncertainty in the vision data.

Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

NASA Astrophysics Data System (ADS)

Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

2016-03-01

The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

Application of machine vision to pup loaf bread evaluation

NASA Astrophysics Data System (ADS)

Zayas, Inna Y.; Chung, O. K.

1996-12-01

Intrinsic end-use quality of hard winter wheat breeding lines is routinely evaluated at the USDA, ARS, USGMRL, Hard Winter Wheat Quality Laboratory. Experimental baking test of pup loaves is the ultimate test for evaluating hard wheat quality. Computer vision was applied to developing an objective methodology for bread quality evaluation for the 1994 and 1995 crop wheat breeding line samples. Computer extracted features for bread crumb grain were studied, using subimages (32 by 32 pixel) and features computed for the slices with different threshold settings. A subsampling grid was located with respect to the axis of symmetry of a slice to provide identical topological subimage information. Different ranking techniques were applied to the databases. Statistical analysis was run on the database with digital image and breadmaking features. Several ranking algorithms and data visualization techniques were employed to create a sensitive scale for porosity patterns of bread crumb. There were significant linear correlations between machine vision extracted features and breadmaking parameters. Crumb grain scores by human experts were correlated more highly with some image features than with breadmaking parameters.

Three-Dimensional Images For Robot Vision

NASA Astrophysics Data System (ADS)

McFarland, William D.

1983-12-01

Robots are attracting increased attention in the industrial productivity crisis. As one significant approach for this nation to maintain technological leadership, the need for robot vision has become critical. The "blind" robot, while occupying an economical niche at present is severely limited and job specific, being only one step up from the numerical controlled machines. To successfully satisfy robot vision requirements a three dimensional representation of a real scene must be provided. Several image acquistion techniques are discussed with more emphasis on the laser radar type instruments. The autonomous vehicle is also discussed as a robot form, and the requirements for these applications are considered. The total computer vision system requirement is reviewed with some discussion of the major techniques in the literature for three dimensional scene analysis.

Protyping machine vision software on the World Wide Web

NASA Astrophysics Data System (ADS)

Karantalis, George; Batchelor, Bruce G.

1998-10-01

Interactive image processing is a proven technique for analyzing industrial vision applications and building prototype systems. Several of the previous implementations have used dedicated hardware to perform the image processing, with a top layer of software providing a convenient user interface. More recently, self-contained software packages have been devised and these run on a standard computer. The advent of the Java programming language has made it possible to write platform-independent software, operating over the Internet, or a company-wide Intranet. Thus, there arises the possibility of designing at least some shop-floor inspection/control systems, without the vision engineer ever entering the factories where they will be used. It successful, this project will have a major impact on the productivity of vision systems designers.

An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN).

PubMed

Keenan, S J; Diamond, J; McCluggage, W G; Bharucha, H; Thompson, D; Bartels, P H; Hamilton, P W

2000-11-01

The histological grading of cervical intraepithelial neoplasia (CIN) remains subjective, resulting in inter- and intra-observer variation and poor reproducibility in the grading of cervical lesions. This study has attempted to develop an objective grading system using automated machine vision. The architectural features of cervical squamous epithelium are quantitatively analysed using a combination of computerized digital image processing and Delaunay triangulation analysis; 230 images digitally captured from cases previously classified by a gynaecological pathologist included normal cervical squamous epithelium (n=30), koilocytosis (n=46), CIN 1 (n=52), CIN 2 (n=56), and CIN 3 (n=46). Intra- and inter-observer variation had kappa values of 0.502 and 0.415, respectively. A machine vision system was developed in KS400 macro programming language to segment and mark the centres of all nuclei within the epithelium. By object-oriented analysis of image components, the positional information of nuclei was used to construct a Delaunay triangulation mesh. Each mesh was analysed to compute triangle dimensions including the mean triangle area, the mean triangle edge length, and the number of triangles per unit area, giving an individual quantitative profile of measurements for each case. Discriminant analysis of the geometric data revealed the significant discriminatory variables from which a classification score was derived. The scoring system distinguished between normal and CIN 3 in 98.7% of cases and between koilocytosis and CIN 1 in 76.5% of cases, but only 62.3% of the CIN cases were classified into the correct group, with the CIN 2 group showing the highest rate of misclassification. Graphical plots of triangulation data demonstrated the continuum of morphological change from normal squamous epithelium to the highest grade of CIN, with overlapping of the groups originally defined by the pathologists. This study shows that automated location of nuclei in cervical biopsies using computerized image analysis is possible. Analysis of positional information enables quantitative evaluation of architectural features in CIN using Delaunay triangulation meshes, which is effective in the objective classification of CIN. This demonstrates the future potential of automated machine vision systems in diagnostic histopathology. Copyright 2000 John Wiley & Sons, Ltd.

Fusion of Multiple Sensing Modalities for Machine Vision

DTIC Science & Technology

1994-05-31

Modeling of Non-Homogeneous 3-D Objects for Thermal and Visual Image Synthesis," Pattern Recognition, in press. U [11] Nair, Dinesh , and J. K. Aggarwal...20th AIPR Workshop: Computer Vision--Meeting the Challenges, McLean, Virginia, October 1991. Nair, Dinesh , and J. K. Aggarwal, "An Object Recognition...Computer Engineering August 1992 Sunil Gupta Ph.D. Student Mohan Kumar M.S. Student Sandeep Kumar M.S. Student Xavier Lebegue Ph.D., Computer

Haptic Exploration in Humans and Machines: Attribute Integration and Machine Recognition/Implementation.

DTIC Science & Technology

1988-04-30

side it necessary and Identify’ by’ block n~nmbot) haptic hand, touch , vision, robot, object recognition, categorization 20. AGSTRPACT (Continue an...established that the haptic system has remarkable capabilities for object recognition. We define haptics as purposive touch . The basic tactual system...gathered ratings of the importance of dimensions for categorizing common objects by touch . Texture and hardness ratings strongly co-vary, which is

Change detection in Arctic satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

NASA Astrophysics Data System (ADS)

Moody, Daniela I.; Wilson, Cathy J.; Rowland, Joel C.; Altmann, Garrett L.

2015-06-01

Advanced pattern recognition and computer vision algorithms are of great interest for landscape characterization, change detection, and change monitoring in satellite imagery, in support of global climate change science and modeling. We present results from an ongoing effort to extend neuroscience-inspired models for feature extraction to the environmental sciences, and we demonstrate our work using Worldview-2 multispectral satellite imagery. We use a Hebbian learning rule to derive multispectral, multiresolution dictionaries directly from regional satellite normalized band difference index data. These feature dictionaries are used to build sparse scene representations, from which we automatically generate land cover labels via our CoSA algorithm: Clustering of Sparse Approximations. These data adaptive feature dictionaries use joint spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologic features. Land cover labels are estimated in example Worldview-2 satellite images of Barrow, Alaska, taken at two different times, and are used to detect and discuss seasonal surface changes. Our results suggest that an approach that learns from both spectral and spatial features is promising for practical pattern recognition problems in high resolution satellite imagery.

Cutting tool form compensation system and method

DOEpatents

Barkman, W.E.; Babelay, E.F. Jr.; Klages, E.J.

1993-10-19

A compensation system for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along a preprogrammed path during a machining operation utilizes a camera and a vision computer for gathering information at a preselected stage of a machining operation relating to the actual shape and size of the cutting edge of the cutting tool and for altering the preprogrammed path in accordance with detected variations between the actual size and shape of the cutting edge and an assumed size and shape of the cutting edge. The camera obtains an image of the cutting tool against a background so that the cutting tool and background possess contrasting light intensities, and the vision computer utilizes the contrasting light intensities of the image to locate points therein which correspond to points along the actual cutting edge. Following a series of computations involving the determining of a tool center from the points identified along the tool edge, the results of the computations are fed to the controller where the preprogrammed path is altered as aforedescribed. 9 figures.

Cutting tool form compensaton system and method

DOEpatents

Barkman, William E.; Babelay, Jr., Edwin F.; Klages, Edward J.

1993-01-01

A compensation system for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along a preprogrammed path during a machining operation utilizes a camera and a vision computer for gathering information at a preselected stage of a machining operation relating to the actual shape and size of the cutting edge of the cutting tool and for altering the preprogrammed path in accordance with detected variations between the actual size and shape of the cutting edge and an assumed size and shape of the cutting edge. The camera obtains an image of the cutting tool against a background so that the cutting tool and background possess contrasting light intensities, and the vision computer utilizes the contrasting light intensities of the image to locate points therein which correspond to points along the actual cutting edge. Following a series of computations involving the determining of a tool center from the points identified along the tool edge, the results of the computations are fed to the controller where the preprogrammed path is altered as aforedescribed.

Galaxy morphology - An unsupervised machine learning approach

NASA Astrophysics Data System (ADS)

Schutter, A.; Shamir, L.

2015-09-01

Structural properties poses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a network of similarities between galaxy morphological types, and automatically deduce a morphological sequence of galaxies. Application of the method to the EFIGI catalog show that the morphological scheme produced by the algorithm is largely in agreement with the De Vaucouleurs system, demonstrating the ability of computer vision and machine learning methods to automatically profile galaxy morphological sequences. The unsupervised analysis method is based on comprehensive computer vision techniques that compute the visual similarities between the different morphological types. Rather than relying on human cognition, the proposed system deduces the similarities between sets of galaxy images in an automatic manner, and is therefore not limited by the number of galaxies being analyzed. The source code of the method is publicly available, and the protocol of the experiment is included in the paper so that the experiment can be replicated, and the method can be used to analyze user-defined datasets of galaxy images.

Quad-polarized synthetic aperture radar and multispectral data classification using classification and regression tree and support vector machine-based data fusion system

NASA Astrophysics Data System (ADS)

Bigdeli, Behnaz; Pahlavani, Parham

2017-01-01

Interpretation of synthetic aperture radar (SAR) data processing is difficult because the geometry and spectral range of SAR are different from optical imagery. Consequently, SAR imaging can be a complementary data to multispectral (MS) optical remote sensing techniques because it does not depend on solar illumination and weather conditions. This study presents a multisensor fusion of SAR and MS data based on the use of classification and regression tree (CART) and support vector machine (SVM) through a decision fusion system. First, different feature extraction strategies were applied on SAR and MS data to produce more spectral and textural information. To overcome the redundancy and correlation between features, an intrinsic dimension estimation method based on noise-whitened Harsanyi, Farrand, and Chang determines the proper dimension of the features. Then, principal component analysis and independent component analysis were utilized on stacked feature space of two data. Afterward, SVM and CART classified each reduced feature space. Finally, a fusion strategy was utilized to fuse the classification results. To show the effectiveness of the proposed methodology, single classification on each data was compared to the obtained results. A coregistered Radarsat-2 and WorldView-2 data set from San Francisco, USA, was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with optical sensor based on the proposed methodology improve the classification results for most of the classes. The proposed fusion method provided approximately 93.24% and 95.44% for two different areas of the data.

Machine vision inspection of railroad track

DOT National Transportation Integrated Search

2011-01-10

North American Railways and the United States Department of Transportation : (US DOT) Federal Railroad Administration (FRA) require periodic inspection of railway : infrastructure to ensure the safety of railway operation. This inspection is a critic...

Technology for robotic surface inspection in space

NASA Technical Reports Server (NTRS)

Volpe, Richard; Balaram, J.

1994-01-01

This paper presents on-going research in robotic inspection of space platforms. Three main areas of investigation are discussed: machine vision inspection techniques, an integrated sensor end-effector, and an orbital environment laboratory simulation. Machine vision inspection utilizes automatic comparison of new and reference images to detect on-orbit induced damage such as micrometeorite impacts. The cameras and lighting used for this inspection are housed in a multisensor end-effector, which also contains a suite of sensors for detection of temperature, gas leaks, proximity, and forces. To fully test all of these sensors, a realistic space platform mock-up has been created, complete with visual, temperature, and gas anomalies. Further, changing orbital lighting conditions are effectively mimicked by a robotic solar simulator. In the paper, each of these technology components will be discussed, and experimental results are provided.

INFIBRA: machine vision inspection of acrylic fiber production

NASA Astrophysics Data System (ADS)

Davies, Roger; Correia, Bento A. B.; Contreiras, Jose; Carvalho, Fernando D.

1998-10-01

This paper describes the implementation of INFIBRA, a machine vision system for the inspection of acrylic fiber production lines. The system was developed by INETI under a contract from Fisipe, Fibras Sinteticas de Portugal, S.A. At Fisipe there are ten production lines in continuous operation, each approximately 40 m in length. A team of operators used to perform periodic manual visual inspection of each line in conditions of high ambient temperature and humidity. It is not surprising that failures in the manual inspection process occurred with some frequency, with consequences that ranged from reduced fiber quality to production stoppages. The INFIBRA system architecture is a specialization of a generic, modular machine vision architecture based on a network of Personal Computers (PCs), each equipped with a low cost frame grabber. Each production line has a dedicated PC that performs automatic inspection, using specially designed metrology algorithms, via four video cameras located at key positions on the line. The cameras are mounted inside custom-built, hermetically sealed water-cooled housings to protect them from the unfriendly environment. The ten PCs, one for each production line, communicate with a central PC via a standard Ethernet connection. The operator controls all aspects of the inspection process, from configuration through to handling alarms, via a simple graphical interface on the central PC. At any time the operator can also view on the central PC's screen the live image from any one of the 40 cameras employed by the system.

Feature extraction algorithm for space targets based on fractal theory

NASA Astrophysics Data System (ADS)

Tian, Balin; Yuan, Jianping; Yue, Xiaokui; Ning, Xin

2007-11-01

In order to offer a potential for extending the life of satellites and reducing the launch and operating costs, satellite servicing including conducting repairs, upgrading and refueling spacecraft on-orbit become much more frequently. Future space operations can be more economically and reliably executed using machine vision systems, which can meet real time and tracking reliability requirements for image tracking of space surveillance system. Machine vision was applied to the research of relative pose for spacecrafts, the feature extraction algorithm was the basis of relative pose. In this paper fractal geometry based edge extraction algorithm which can be used in determining and tracking the relative pose of an observed satellite during proximity operations in machine vision system was presented. The method gets the gray-level image distributed by fractal dimension used the Differential Box-Counting (DBC) approach of the fractal theory to restrain the noise. After this, we detect the consecutive edge using Mathematical Morphology. The validity of the proposed method is examined by processing and analyzing images of space targets. The edge extraction method not only extracts the outline of the target, but also keeps the inner details. Meanwhile, edge extraction is only processed in moving area to reduce computation greatly. Simulation results compared edge detection using the method which presented by us with other detection methods. The results indicate that the presented algorithm is a valid method to solve the problems of relative pose for spacecrafts.

Self-powered vision electronic-skin basing on piezo-photodetecting Ppy/PVDF pixel-patterned matrix for mimicking vision.

PubMed

Han, Wuxiao; Zhang, Linlin; He, Haoxuan; Liu, Hongmin; Xing, Lili; Xue, Xinyu

2018-06-22

The development of multifunctional electronic-skin that establishes human-machine interfaces, enhances perception abilities or has other distinct biomedical applications is the key to the realization of artificial intelligence. In this paper, a new self-powered (battery-free) flexible vision electronic-skin has been realized from pixel-patterned matrix of piezo-photodetecting PVDF/Ppy film. The electronic-skin under applied deformation can actively output piezoelectric voltage, and the outputting signal can be significantly influenced by UV illumination. The piezoelectric output can act as both the photodetecting signal and electricity power. The reliability is demonstrated over 200 light on-off cycles. The sensing unit matrix of 6 × 6 pixels on the electronic-skin can realize image recognition through mapping multi-point UV stimuli. This self-powered vision electronic-skin that simply mimics human retina may have potential application in vision substitution.

Self-powered vision electronic-skin basing on piezo-photodetecting Ppy/PVDF pixel-patterned matrix for mimicking vision

NASA Astrophysics Data System (ADS)

Han, Wuxiao; Zhang, Linlin; He, Haoxuan; Liu, Hongmin; Xing, Lili; Xue, Xinyu

2018-06-01

The development of multifunctional electronic-skin that establishes human-machine interfaces, enhances perception abilities or has other distinct biomedical applications is the key to the realization of artificial intelligence. In this paper, a new self-powered (battery-free) flexible vision electronic-skin has been realized from pixel-patterned matrix of piezo-photodetecting PVDF/Ppy film. The electronic-skin under applied deformation can actively output piezoelectric voltage, and the outputting signal can be significantly influenced by UV illumination. The piezoelectric output can act as both the photodetecting signal and electricity power. The reliability is demonstrated over 200 light on–off cycles. The sensing unit matrix of 6 × 6 pixels on the electronic-skin can realize image recognition through mapping multi-point UV stimuli. This self-powered vision electronic-skin that simply mimics human retina may have potential application in vision substitution.

Evolving forest fire burn severity classification algorithms for multispectral imagery

NASA Astrophysics Data System (ADS)

Brumby, Steven P.; Harvey, Neal R.; Bloch, Jeffrey J.; Theiler, James P.; Perkins, Simon J.; Young, Aaron C.; Szymanski, John J.

2001-08-01

Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100% tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial color/infrared photography.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

NASA Astrophysics Data System (ADS)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Machine processing of remotely sensed data; Proceedings of the Fifth Annual Symposium, Purdue University, West Lafayette, Ind., June 27-29, 1979

NASA Technical Reports Server (NTRS)

Tendam, I. M. (Editor); Morrison, D. B.

1979-01-01

Papers are presented on techniques and applications for the machine processing of remotely sensed data. Specific topics include the Landsat-D mission and thematic mapper, data preprocessing to account for atmospheric and solar illumination effects, sampling in crop area estimation, the LACIE program, the assessment of revegetation on surface mine land using color infrared aerial photography, the identification of surface-disturbed features through a nonparametric analysis of Landsat MSS data, the extraction of soil data in vegetated areas, and the transfer of remote sensing computer technology to developing nations. Attention is also given to the classification of multispectral remote sensing data using context, the use of guided clustering techniques for Landsat data analysis in forest land cover mapping, crop classification using an interactive color display, and future trends in image processing software and hardware.

Humans and machines in space: The vision, the challenge, the payoff; Proceedings of the 29th Goddard Memorial Symposium, Washington, Mar. 14, 15, 1991

NASA Astrophysics Data System (ADS)

Johnson, Bradley; May, Gayle L.; Korn, Paula

The present conference discusses the currently envisioned goals of human-machine systems in spacecraft environments, prospects for human exploration of the solar system, and plausible methods for meeting human needs in space. Also discussed are the problems of human-machine interaction in long-duration space flights, remote medical systems for space exploration, the use of virtual reality for planetary exploration, the alliance between U.S. Antarctic and space programs, and the economic and educational impacts of the U.S. space program.

Fast and robust generation of feature maps for region-based visual attention.

PubMed

Aziz, Muhammad Zaheer; Mertsching, Bärbel

2008-05-01

Visual attention is one of the important phenomena in biological vision which can be followed to achieve more efficiency, intelligence, and robustness in artificial vision systems. This paper investigates a region-based approach that performs pixel clustering prior to the processes of attention in contrast to late clustering as done by contemporary methods. The foundation steps of feature map construction for the region-based attention model are proposed here. The color contrast map is generated based upon the extended findings from the color theory, the symmetry map is constructed using a novel scanning-based method, and a new algorithm is proposed to compute a size contrast map as a formal feature channel. Eccentricity and orientation are computed using the moments of obtained regions and then saliency is evaluated using the rarity criteria. The efficient design of the proposed algorithms allows incorporating five feature channels while maintaining a processing rate of multiple frames per second. Another salient advantage over the existing techniques is the reusability of the salient regions in the high-level machine vision procedures due to preservation of their shapes and precise locations. The results indicate that the proposed model has the potential to efficiently integrate the phenomenon of attention into the main stream of machine vision and systems with restricted computing resources such as mobile robots can benefit from its advantages.

Development of a machine vision system for automated structural assembly

NASA Technical Reports Server (NTRS)

Sydow, P. Daniel; Cooper, Eric G.

1992-01-01

Research is being conducted at the LaRC to develop a telerobotic assembly system designed to construct large space truss structures. This research program was initiated within the past several years, and a ground-based test-bed was developed to evaluate and expand the state of the art. Test-bed operations currently use predetermined ('taught') points for truss structural assembly. Total dependence on the use of taught points for joint receptacle capture and strut installation is neither robust nor reliable enough for space operations. Therefore, a machine vision sensor guidance system is being developed to locate and guide the robot to a passive target mounted on the truss joint receptacle. The vision system hardware includes a miniature video camera, passive targets mounted on the joint receptacles, target illumination hardware, and an image processing system. Discrimination of the target from background clutter is accomplished through standard digital processing techniques. Once the target is identified, a pose estimation algorithm is invoked to determine the location, in three-dimensional space, of the target relative to the robots end-effector. Preliminary test results of the vision system in the Automated Structural Assembly Laboratory with a range of lighting and background conditions indicate that it is fully capable of successfully identifying joint receptacle targets throughout the required operational range. Controlled optical bench test results indicate that the system can also provide the pose estimation accuracy to define the target position.

Humans and machines in space: The vision, the challenge, the payoff; AAS Goddard Memorial Symposium, 29th, Washington, DC, March 14-15, 1991

NASA Astrophysics Data System (ADS)

Johnson, Bradley; May, Gayle L.; Korn, Paula

A recent symposium produced papers in the areas of solar system exploration, man machine interfaces, cybernetics, virtual reality, telerobotics, life support systems and the scientific and technology spinoff from the NASA space program. A number of papers also addressed the social and economic impacts of the space program. For individual titles, see A95-87468 through A95-87479.

A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data.

PubMed

Sharma, Ram C; Hara, Keitarou; Hirayama, Hidetake

2017-01-01

This paper presents the performance and evaluation of a number of machine learning classifiers for the discrimination between the vegetation physiognomic classes using the satellite based time-series of the surface reflectance data. Discrimination of six vegetation physiognomic classes, Evergreen Coniferous Forest, Evergreen Broadleaf Forest, Deciduous Coniferous Forest, Deciduous Broadleaf Forest, Shrubs, and Herbs, was dealt with in the research. Rich-feature data were prepared from time-series of the satellite data for the discrimination and cross-validation of the vegetation physiognomic types using machine learning approach. A set of machine learning experiments comprised of a number of supervised classifiers with different model parameters was conducted to assess how the discrimination of vegetation physiognomic classes varies with classifiers, input features, and ground truth data size. The performance of each experiment was evaluated by using the 10-fold cross-validation method. Experiment using the Random Forests classifier provided highest overall accuracy (0.81) and kappa coefficient (0.78). However, accuracy metrics did not vary much with experiments. Accuracy metrics were found to be very sensitive to input features and size of ground truth data. The results obtained in the research are expected to be useful for improving the vegetation physiognomic mapping in Japan.

FOCIS: A forest classification and inventory system using LANDSAT and digital terrain data

NASA Technical Reports Server (NTRS)

Strahler, A. H.; Franklin, J.; Woodcook, C. E.; Logan, T. L.

1981-01-01

Accurate, cost-effective stratification of forest vegetation and timber inventory is the primary goal of a Forest Classification and Inventory System (FOCIS). Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine processing techniques to extract and process tonal, textural, and terrain information from registered LANDSAT multispectral and digital terrain data. Comparison of samples from timber strata identified by conventional procedures showed that both have about the same potential to reduce the variance of timber volume estimates over simple random sampling.

Application of principal component analysis to multispectral-multimodal optical image analysis for malaria diagnostics.

PubMed

Omucheni, Dickson L; Kaduki, Kenneth A; Bulimo, Wallace D; Angeyo, Hudson K

2014-12-11

Multispectral imaging microscopy is a novel microscopic technique that integrates spectroscopy with optical imaging to record both spectral and spatial information of a specimen. This enables acquisition of a large and more informative dataset than is achievable in conventional optical microscopy. However, such data are characterized by high signal correlation and are difficult to interpret using univariate data analysis techniques. In this work, the development and application of a novel method which uses principal component analysis (PCA) in the processing of spectral images obtained from a simple multispectral-multimodal imaging microscope to detect Plasmodium parasites in unstained thin blood smear for malaria diagnostics is reported. The optical microscope used in this work has been modified by replacing the broadband light source (tungsten halogen lamp) with a set of light emitting diodes (LEDs) emitting thirteen different wavelengths of monochromatic light in the UV-vis-NIR range. The LEDs are activated sequentially to illuminate same spot of the unstained thin blood smears on glass slides, and grey level images are recorded at each wavelength. PCA was used to perform data dimensionality reduction and to enhance score images for visualization as well as for feature extraction through clusters in score space. Using this approach, haemozoin was uniquely distinguished from haemoglobin in unstained thin blood smears on glass slides and the 590-700 spectral range identified as an important band for optical imaging of haemozoin as a biomarker for malaria diagnosis. This work is of great significance in reducing the time spent on staining malaria specimens and thus drastically reducing diagnosis time duration. The approach has the potential of replacing a trained human eye with a trained computerized vision system for malaria parasite blood screening.

High Dynamic Range Spectral Imaging Pipeline For Multispectral Filter Array Cameras.

PubMed

Lapray, Pierre-Jean; Thomas, Jean-Baptiste; Gouton, Pierre

2017-06-03

Spectral filter arrays imaging exhibits a strong similarity with color filter arrays. This permits us to embed this technology in practical vision systems with little adaptation of the existing solutions. In this communication, we define an imaging pipeline that permits high dynamic range (HDR)-spectral imaging, which is extended from color filter arrays. We propose an implementation of this pipeline on a prototype sensor and evaluate the quality of our implementation results on real data with objective metrics and visual examples. We demonstrate that we reduce noise, and, in particular we solve the problem of noise generated by the lack of energy balance. Data are provided to the community in an image database for further research.

Visions of the Cosmos

NASA Astrophysics Data System (ADS)

Collins Petersen, Carolyn; Brandt, John C.

2003-11-01

Introduction; 1. Eyes in the sky; 2. Telescopes: multi-frequency time machines; 3. Planets on a pixel; 4. The lives of stars; 5. Galaxies - tales of stellar cities; 6. The once and future universe; 7. Stargazing - the next generation; Glossary.

A real-time surface inspection system for precision steel balls based on machine vision

NASA Astrophysics Data System (ADS)

Chen, Yi-Ji; Tsai, Jhy-Cherng; Hsu, Ya-Chen

2016-07-01

Precision steel balls are one of the most fundament components for motion and power transmission parts and they are widely used in industrial machinery and the automotive industry. As precision balls are crucial for the quality of these products, there is an urgent need to develop a fast and robust system for inspecting defects of precision steel balls. In this paper, a real-time system for inspecting surface defects of precision steel balls is developed based on machine vision. The developed system integrates a dual-lighting system, an unfolding mechanism and inspection algorithms for real-time signal processing and defect detection. The developed system is tested under feeding speeds of 4 pcs s-1 with a detection rate of 99.94% and an error rate of 0.10%. The minimum detectable surface flaw area is 0.01 mm2, which meets the requirement for inspecting ISO grade 100 precision steel balls.

A new approach based on Machine Learning for predicting corneal curvature (K1) and astigmatism in patients with keratoconus after intracorneal ring implantation.

PubMed

Valdés-Mas, M A; Martín-Guerrero, J D; Rupérez, M J; Pastor, F; Dualde, C; Monserrat, C; Peris-Martínez, C

2014-08-01

Keratoconus (KC) is the most common type of corneal ectasia. A corneal transplantation was the treatment of choice until the last decade. However, intra-corneal ring implantation has become more and more common, and it is commonly used to treat KC thus avoiding a corneal transplantation. This work proposes a new approach based on Machine Learning to predict the vision gain of KC patients after ring implantation. That vision gain is assessed by means of the corneal curvature and the astigmatism. Different models were proposed; the best results were achieved by an artificial neural network based on the Multilayer Perceptron. The error provided by the best model was 0.97D of corneal curvature and 0.93D of astigmatism. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A survey of camera error sources in machine vision systems

NASA Astrophysics Data System (ADS)

Jatko, W. B.

In machine vision applications, such as an automated inspection line, television cameras are commonly used to record scene intensity in a computer memory or frame buffer. Scene data from the image sensor can then be analyzed with a wide variety of feature-detection techniques. Many algorithms found in textbooks on image processing make the implicit simplifying assumption of an ideal input image with clearly defined edges and uniform illumination. The ideal image model is helpful to aid the student in understanding the principles of operation, but when these algorithms are blindly applied to real-world images the results can be unsatisfactory. This paper examines some common measurement errors found in camera sensors and their underlying causes, and possible methods of error compensation. The role of the camera in a typical image-processing system is discussed, with emphasis on the origination of signal distortions. The effects of such things as lighting, optics, and sensor characteristics are considered.

Fragrant pear sexuality recognition with machine vision

NASA Astrophysics Data System (ADS)

Ma, Benxue; Ying, Yibin

2006-10-01

In this research, a method to identify Kuler fragrant pear's sexuality with machine vision was developed. Kuler fragrant pear has male pear and female pear. They have an obvious difference in favor. To detect the sexuality of Kuler fragrant pear, images of fragrant pear were acquired by CCD color camera. Before feature extraction, some preprocessing is conducted on the acquired images to remove noise and unnecessary contents. Color feature, perimeter feature and area feature of fragrant pear bottom image were extracted by digital image processing technique. And the fragrant pear sexuality was determined by complexity obtained from perimeter and area. In this research, using 128 Kurle fragrant pears as samples, good recognition rate between the male pear and the female pear was obtained for Kurle pear's sexuality detection (82.8%). Result shows this method could detect male pear and female pear with a good accuracy.

Nondestructive Detection of the Internalquality of Apple Using X-Ray and Machine Vision

NASA Astrophysics Data System (ADS)

Yang, Fuzeng; Yang, Liangliang; Yang, Qing; Kang, Likui

The internal quality of apple is impossible to be detected by eyes in the procedure of sorting, which could reduce the apple’s quality reaching market. This paper illustrates an instrument using X-ray and machine vision. The following steps were introduced to process the X-ray image in order to determine the mould core apple. Firstly, lifting wavelet transform was used to get a low frequency image and three high frequency images. Secondly, we enhanced the low frequency image through image’s histogram equalization. Then, the edge of each apple's image was detected using canny operator. Finally, a threshold was set to clarify mould core and normal apple according to the different length of the apple core’s diameter. The experimental results show that this method could on-line detect the mould core apple with less time consuming, less than 0.03 seconds per apple, and the accuracy could reach 92%.

Computational Analysis of Behavior.

PubMed

Egnor, S E Roian; Branson, Kristin

2016-07-08

In this review, we discuss the emerging field of computational behavioral analysis-the use of modern methods from computer science and engineering to quantitatively measure animal behavior. We discuss aspects of experiment design important to both obtaining biologically relevant behavioral data and enabling the use of machine vision and learning techniques for automation. These two goals are often in conflict. Restraining or restricting the environment of the animal can simplify automatic behavior quantification, but it can also degrade the quality or alter important aspects of behavior. To enable biologists to design experiments to obtain better behavioral measurements, and computer scientists to pinpoint fruitful directions for algorithm improvement, we review known effects of artificial manipulation of the animal on behavior. We also review machine vision and learning techniques for tracking, feature extraction, automated behavior classification, and automated behavior discovery, the assumptions they make, and the types of data they work best with.

Machine vision application in animal trajectory tracking.

PubMed

Koniar, Dušan; Hargaš, Libor; Loncová, Zuzana; Duchoň, František; Beňo, Peter

2016-04-01

This article was motivated by the doctors' demand to make a technical support in pathologies of gastrointestinal tract research [10], which would be based on machine vision tools. Proposed solution should be less expensive alternative to already existing RF (radio frequency) methods. The objective of whole experiment was to evaluate the amount of animal motion dependent on degree of pathology (gastric ulcer). In the theoretical part of the article, several methods of animal trajectory tracking are presented: two differential methods based on background subtraction, the thresholding methods based on global and local threshold and the last method used for animal tracking was the color matching with a chosen template containing a searched spectrum of colors. The methods were tested offline on five video samples. Each sample contained situation with moving guinea pig locked in a cage under various lighting conditions. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

KENNEDY SPACE CENTER, FLA. - Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla., is the site where Center Director Jim Kennedy and astronaut Kay Hire shared the agency’s new vision for space exploration with the next generation of explorers. Kennedy talked with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

NASA Image and Video Library

2004-02-20

KENNEDY SPACE CENTER, FLA. - Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla., is the site where Center Director Jim Kennedy and astronaut Kay Hire shared the agency’s new vision for space exploration with the next generation of explorers. Kennedy talked with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

Sensor fusion II: Human and machine strategies; Proceedings of the Meeting, Philadelphia, PA, Nov. 6-9, 1989

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1990-01-01

Various papers on human and machine strategies in sensor fusion are presented. The general topics addressed include: active vision, measurement and analysis of visual motion, decision models for sensor fusion, implementation of sensor fusion algorithms, applying sensor fusion to image analysis, perceptual modules and their fusion, perceptual organization and object recognition, planning and the integration of high-level knowledge with perception, using prior knowledge and context in sensor fusion.

Convolutional neural network guided blue crab knuckle detection for autonomous crab meat picking machine

NASA Astrophysics Data System (ADS)

Wang, Dongyi; Vinson, Robert; Holmes, Maxwell; Seibel, Gary; Tao, Yang

2018-04-01

The Atlantic blue crab is among the highest-valued seafood found in the American Eastern Seaboard. Currently, the crab processing industry is highly dependent on manual labor. However, there is great potential for vision-guided intelligent machines to automate the meat picking process. Studies show that the back-fin knuckles are robust features containing information about a crab's size, orientation, and the position of the crab's meat compartments. Our studies also make it clear that detecting the knuckles reliably in images is challenging due to the knuckle's small size, anomalous shape, and similarity to joints in the legs and claws. An accurate and reliable computer vision algorithm was proposed to detect the crab's back-fin knuckles in digital images. Convolutional neural networks (CNNs) can localize rough knuckle positions with 97.67% accuracy, transforming a global detection problem into a local detection problem. Compared to the rough localization based on human experience or other machine learning classification methods, the CNN shows the best localization results. In the rough knuckle position, a k-means clustering method is able to further extract the exact knuckle positions based on the back-fin knuckle color features. The exact knuckle position can help us to generate a crab cutline in XY plane using a template matching method. This is a pioneering research project in crab image analysis and offers advanced machine intelligence for automated crab processing.

Wire connector classification with machine vision and a novel hybrid SVM

NASA Astrophysics Data System (ADS)

Chauhan, Vedang; Joshi, Keyur D.; Surgenor, Brian W.

2018-04-01

A machine vision-based system has been developed and tested that uses a novel hybrid Support Vector Machine (SVM) in a part inspection application with clear plastic wire connectors. The application required the system to differentiate between 4 different known styles of connectors plus one unknown style, for a total of 5 classes. The requirement to handle an unknown class is what necessitated the hybrid approach. The system was trained with the 4 known classes and tested with 5 classes (the 4 known plus the 1 unknown). The hybrid classification approach used two layers of SVMs: one layer was semi-supervised and the other layer was supervised. The semi-supervised SVM was a special case of unsupervised machine learning that classified test images as one of the 4 known classes (to accept) or as the unknown class (to reject). The supervised SVM classified test images as one of the 4 known classes and consequently would give false positives (FPs). Two methods were tested. The difference between the methods was that the order of the layers was switched. The method with the semi-supervised layer first gave an accuracy of 80% with 20% FPs. The method with the supervised layer first gave an accuracy of 98% with 0% FPs. Further work is being conducted to see if the hybrid approach works with other applications that have an unknown class requirement.

Design and Development of a High Speed Sorting System Based on Machine Vision Guiding

NASA Astrophysics Data System (ADS)

Zhang, Wenchang; Mei, Jiangping; Ding, Yabin

In this paper, a vision-based control strategy to perform high speed pick-and-place tasks on automation product line is proposed, and relevant control software is develop. Using Delta robot to control a sucker to grasp disordered objects from one moving conveyer and then place them on the other in order. CCD camera gets one picture every time the conveyer moves a distance of ds. Objects position and shape are got after image processing. Target tracking method based on "Servo motor + synchronous conveyer" is used to fulfill the high speed porting operation real time. Experiments conducted on Delta robot sorting system demonstrate the efficiency and validity of the proposed vision-control strategy.

Vision Based Autonomous Robotic Control for Advanced Inspection and Repair

NASA Technical Reports Server (NTRS)

Wehner, Walter S.

2014-01-01

The advanced inspection system is an autonomous control and analysis system that improves the inspection and remediation operations for ground and surface systems. It uses optical imaging technology with intelligent computer vision algorithms to analyze physical features of the real-world environment to make decisions and learn from experience. The advanced inspection system plans to control a robotic manipulator arm, an unmanned ground vehicle and cameras remotely, automatically and autonomously. There are many computer vision, image processing and machine learning techniques available as open source for using vision as a sensory feedback in decision-making and autonomous robotic movement. My responsibilities for the advanced inspection system are to create a software architecture that integrates and provides a framework for all the different subsystem components; identify open-source algorithms and techniques; and integrate robot hardware.

An Autonomous Gps-Denied Unmanned Vehicle Platform Based on Binocular Vision for Planetary Exploration

NASA Astrophysics Data System (ADS)

Qin, M.; Wan, X.; Shao, Y. Y.; Li, S. Y.

2018-04-01

Vision-based navigation has become an attractive solution for autonomous navigation for planetary exploration. This paper presents our work of designing and building an autonomous vision-based GPS-denied unmanned vehicle and developing an ARFM (Adaptive Robust Feature Matching) based VO (Visual Odometry) software for its autonomous navigation. The hardware system is mainly composed of binocular stereo camera, a pan-and tilt, a master machine, a tracked chassis. And the ARFM-based VO software system contains four modules: camera calibration, ARFM-based 3D reconstruction, position and attitude calculation, BA (Bundle Adjustment) modules. Two VO experiments were carried out using both outdoor images from open dataset and indoor images captured by our vehicle, the results demonstrate that our vision-based unmanned vehicle is able to achieve autonomous localization and has the potential for future planetary exploration.

Machine-Vision Aids for Improved Flight Operations

NASA Technical Reports Server (NTRS)

Menon, P. K.; Chatterji, Gano B.

1996-01-01

The development of machine vision based pilot aids to help reduce night approach and landing accidents is explored. The techniques developed are motivated by the desire to use the available information sources for navigation such as the airport lighting layout, attitude sensors and Global Positioning System to derive more precise aircraft position and orientation information. The fact that airport lighting geometry is known and that images of airport lighting can be acquired by the camera, has lead to the synthesis of machine vision based algorithms for runway relative aircraft position and orientation estimation. The main contribution of this research is the synthesis of seven navigation algorithms based on two broad families of solutions. The first family of solution methods consists of techniques that reconstruct the airport lighting layout from the camera image and then estimate the aircraft position components by comparing the reconstructed lighting layout geometry with the known model of the airport lighting layout geometry. The second family of methods comprises techniques that synthesize the image of the airport lighting layout using a camera model and estimate the aircraft position and orientation by comparing this image with the actual image of the airport lighting acquired by the camera. Algorithms 1 through 4 belong to the first family of solutions while Algorithms 5 through 7 belong to the second family of solutions. Algorithms 1 and 2 are parameter optimization methods, Algorithms 3 and 4 are feature correspondence methods and Algorithms 5 through 7 are Kalman filter centered algorithms. Results of computer simulation are presented to demonstrate the performance of all the seven algorithms developed.

Recognizing sights, smells, and sounds with gnostic fields.

PubMed

Kanan, Christopher

2013-01-01

Mammals rely on vision, audition, and olfaction to remotely sense stimuli in their environment. Determining how the mammalian brain uses this sensory information to recognize objects has been one of the major goals of psychology and neuroscience. Likewise, researchers in computer vision, machine audition, and machine olfaction have endeavored to discover good algorithms for stimulus classification. Almost 50 years ago, the neuroscientist Jerzy Konorski proposed a theoretical model in his final monograph in which competing sets of "gnostic" neurons sitting atop sensory processing hierarchies enabled stimuli to be robustly categorized, despite variations in their presentation. Much of what Konorski hypothesized has been remarkably accurate, and neurons with gnostic-like properties have been discovered in visual, aural, and olfactory brain regions. Surprisingly, there have not been any attempts to directly transform his theoretical model into a computational one. Here, I describe the first computational implementation of Konorski's theory. The model is not domain specific, and it surpasses the best machine learning algorithms on challenging image, music, and olfactory classification tasks, while also being simpler. My results suggest that criticisms of exemplar-based models of object recognition as being computationally intractable due to limited neural resources are unfounded.

Recognizing Sights, Smells, and Sounds with Gnostic Fields

PubMed Central

Kanan, Christopher

2013-01-01

Mammals rely on vision, audition, and olfaction to remotely sense stimuli in their environment. Determining how the mammalian brain uses this sensory information to recognize objects has been one of the major goals of psychology and neuroscience. Likewise, researchers in computer vision, machine audition, and machine olfaction have endeavored to discover good algorithms for stimulus classification. Almost 50 years ago, the neuroscientist Jerzy Konorski proposed a theoretical model in his final monograph in which competing sets of “gnostic” neurons sitting atop sensory processing hierarchies enabled stimuli to be robustly categorized, despite variations in their presentation. Much of what Konorski hypothesized has been remarkably accurate, and neurons with gnostic-like properties have been discovered in visual, aural, and olfactory brain regions. Surprisingly, there have not been any attempts to directly transform his theoretical model into a computational one. Here, I describe the first computational implementation of Konorski's theory. The model is not domain specific, and it surpasses the best machine learning algorithms on challenging image, music, and olfactory classification tasks, while also being simpler. My results suggest that criticisms of exemplar-based models of object recognition as being computationally intractable due to limited neural resources are unfounded. PMID:23365648

Cohort: critical science

NASA Astrophysics Data System (ADS)

Digney, Bruce L.

2007-04-01

Unmanned vehicle systems is an attractive technology for the military, but whose promises have remained largely undelivered. There currently exist fielded remote controlled UGVs and high altitude UAV whose benefits are based on standoff in low complexity environments with sufficiently low control reaction time requirements to allow for teleoperation. While effective within there limited operational niche such systems do not meet with the vision of future military UxV scenarios. Such scenarios envision unmanned vehicles operating effectively in complex environments and situations with high levels of independence and effective coordination with other machines and humans pursing high level, changing and sometimes conflicting goals. While these aims are clearly ambitious they do provide necessary targets and inspiration with hopes of fielding near term useful semi-autonomous unmanned systems. Autonomy involves many fields of research including machine vision, artificial intelligence, control theory, machine learning and distributed systems all of which are intertwined and have goals of creating more versatile broadly applicable algorithms. Cohort is a major Applied Research Program (ARP) led by Defence R&D Canada (DRDC) Suffield and its aim is to develop coordinated teams of unmanned vehicles (UxVs) for urban environments. This paper will discuss the critical science being addressed by DRDC developing semi-autonomous systems.

Vision based nutrient deficiency classification in maize plants using multi class support vector machines

NASA Astrophysics Data System (ADS)

Leena, N.; Saju, K. K.

2018-04-01

Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

Job Prospects for Manufacturing Engineers.

ERIC Educational Resources Information Center

Basta, Nicholas

1985-01-01

Coming from a variety of disciplines, manufacturing engineers are keys to industry's efforts to modernize, with demand exceeding supply. The newest and fastest-growing areas include machine vision, composite materials, and manufacturing automation protocols, each of which is briefly discussed. (JN)

Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

PubMed Central

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G.; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Background Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. Methodology and Principal Findings A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Conclusion and Significance Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level. PMID:22235254

Mapping migratory bird prevalence using remote sensing data fusion.

PubMed

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.

Defect detection and classification of machined surfaces under multiple illuminant directions

NASA Astrophysics Data System (ADS)

Liao, Yi; Weng, Xin; Swonger, C. W.; Ni, Jun

2010-08-01

Continuous improvement of product quality is crucial to the successful and competitive automotive manufacturing industry in the 21st century. The presence of surface porosity located on flat machined surfaces such as cylinder heads/blocks and transmission cases may allow leaks of coolant, oil, or combustion gas between critical mating surfaces, thus causing damage to the engine or transmission. Therefore 100% inline inspection plays an important role for improving product quality. Although the techniques of image processing and machine vision have been applied to machined surface inspection and well improved in the past 20 years, in today's automotive industry, surface porosity inspection is still done by skilled humans, which is costly, tedious, time consuming and not capable of reliably detecting small defects. In our study, an automated defect detection and classification system for flat machined surfaces has been designed and constructed. In this paper, the importance of the illuminant direction in a machine vision system was first emphasized and then the surface defect inspection system under multiple directional illuminations was designed and constructed. After that, image processing algorithms were developed to realize 5 types of 2D or 3D surface defects (pore, 2D blemish, residue dirt, scratch, and gouge) detection and classification. The steps of image processing include: (1) image acquisition and contrast enhancement (2) defect segmentation and feature extraction (3) defect classification. An artificial machined surface and an actual automotive part: cylinder head surface were tested and, as a result, microscopic surface defects can be accurately detected and assigned to a surface defect class. The cycle time of this system can be sufficiently fast that implementation of 100% inline inspection is feasible. The field of view of this system is 150mm×225mm and the surfaces larger than the field of view can be stitched together in software.

Computer vision cracks the leaf code

PubMed Central

Wilf, Peter; Zhang, Shengping; Chikkerur, Sharat; Little, Stefan A.; Wing, Scott L.; Serre, Thomas

2016-01-01

Understanding the extremely variable, complex shape and venation characters of angiosperm leaves is one of the most challenging problems in botany. Machine learning offers opportunities to analyze large numbers of specimens, to discover novel leaf features of angiosperm clades that may have phylogenetic significance, and to use those characters to classify unknowns. Previous computer vision approaches have primarily focused on leaf identification at the species level. It remains an open question whether learning and classification are possible among major evolutionary groups such as families and orders, which usually contain hundreds to thousands of species each and exhibit many times the foliar variation of individual species. Here, we tested whether a computer vision algorithm could use a database of 7,597 leaf images from 2,001 genera to learn features of botanical families and orders, then classify novel images. The images are of cleared leaves, specimens that are chemically bleached, then stained to reveal venation. Machine learning was used to learn a codebook of visual elements representing leaf shape and venation patterns. The resulting automated system learned to classify images into families and orders with a success rate many times greater than chance. Of direct botanical interest, the responses of diagnostic features can be visualized on leaf images as heat maps, which are likely to prompt recognition and evolutionary interpretation of a wealth of novel morphological characters. With assistance from computer vision, leaves are poised to make numerous new contributions to systematic and paleobotanical studies. PMID:26951664

MLBCD: a machine learning tool for big clinical data.

PubMed

Luo, Gang

2015-01-01

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

Neural network expert system for X-ray analysis of welded joints

NASA Astrophysics Data System (ADS)

Kozlov, V. V.; Lapik, N. V.; Popova, N. V.

2018-03-01

The use of intelligent technologies for the automated analysis of product quality is one of the main trends in modern machine building. At the same time, rapid development in various spheres of human activity is experienced by methods associated with the use of artificial neural networks, as the basis for building automated intelligent diagnostic systems. Technologies of machine vision allow one to effectively detect the presence of certain regularities in the analyzed designation, including defects of welded joints according to radiography data.

Right Sizing the Force: Restructuring the Marine Light Attack Helicopter (HML/A) Squadron to Better Meet the Emerging Threat

DTIC Science & Technology

2009-04-29

visual coverage armed with responsive, high volume offensive or defensive door mounted machine guns and nearly 3600 weapons coverage. In addition, a fixed...forward gun option and forward firing rocket capacity substantially expand firepower options. Team this crew and machine with the world’s premiere...escort. 1 Headquarters, U. S. Marine Corps, Vision & Strategy 2025, (Washington, DC: Headquarters, U.S. Marine Corps, June 18, 2008), 9. 2 Scott Atwood

Multispectral images of flowers reveal the adaptive significance of using long-wavelength-sensitive receptors for edge detection in bees.

PubMed

Vasas, Vera; Hanley, Daniel; Kevan, Peter G; Chittka, Lars

2017-04-01

Many pollinating insects acquire their entire nutrition from visiting flowers, and they must therefore be efficient both at detecting flowers and at recognizing familiar rewarding flower types. A crucial first step in recognition is the identification of edges and the segmentation of the visual field into areas that belong together. Honeybees and bumblebees acquire visual information through three types of photoreceptors; however, they only use a single receptor type-the one sensitive to longer wavelengths-for edge detection and movement detection. Here, we show that these long-wavelength receptors (peak sensitivity at ~544 nm, i.e., green) provide the most consistent signals in response to natural objects. Using our multispectral image database of flowering plants, we found that long-wavelength receptor responses had, depending on the specific scenario, up to four times higher signal-to-noise ratios than the short- and medium-wavelength receptors. The reliability of the long-wavelength receptors emerges from an intricate interaction between flower coloration and the bee's visual system. This finding highlights the adaptive significance of bees using only long-wavelength receptors to locate flowers among leaves, before using information provided by all three receptors to distinguish the rewarding flower species through trichromatic color vision.

A comparative study of machine learning models for ethnicity classification

NASA Astrophysics Data System (ADS)

Trivedi, Advait; Bessie Amali, D. Geraldine

2017-11-01

This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

Impact resistance of materials for guards on cutting machine tools--requirements in future European safety standards.

PubMed

Mewes, D; Trapp, R P

2000-01-01

Guards on machine tools are meant to protect operators from injuries caused by tools, workpieces, and fragments hurled out of the machine's working zone. This article presents the impact resistance requirements, which guards according to European safety standards for machine tools must satisfy. Based upon these standards the impact resistance of different guard materials was determined using cylindrical steel projectiles. Polycarbonate proves to be a suitable material for vision panels because of its high energy absorption capacity. The impact resistance of 8-mm thick polycarbonate is roughly equal to that of a 3-mm thick steel sheet Fe P01. The limited ageing stability, however, makes it necessary to protect polycarbonate against cooling lubricants by means of additional panes on both sides.

Technology and Web-Based Support

ERIC Educational Resources Information Center

Smith, Carol

2008-01-01

Many types of technology support caregiving: (1) Assistive devices include medicine dispensers, feeding and bathing machines, clothing with polypropylene fibers that stimulate muscles, intelligent ambulatory walkers for those with both vision and mobility impairment, medication reminders, and safety alarms; (2) Telecare devices ranging from…

Categorization of extraneous matter in cotton using machine vision systems

USDA-ARS?s Scientific Manuscript database

The Cotton Trash Identification System (CTIS) developed at the Southwestern Cotton Ginning Research Laboratory was evaluated for identification and categorization of extraneous matter in cotton. The CTIS bark/grass categorization was evaluated with USDA-Agricultural Marketing Service (AMS) extraneou...

Hyperspectral imaging for nondestructive evaluation of tomatoes

USDA-ARS?s Scientific Manuscript database

Machine vision methods for quality and defect evaluation of tomatoes have been studied for online sorting and robotic harvesting applications. We investigated the use of a hyperspectral imaging system for quality evaluation and defect detection for tomatoes. Hyperspectral reflectance images were a...

Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning

NASA Astrophysics Data System (ADS)

Debats, Stephanie Renee

Smallholder farms dominate in many parts of the world, including Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural landcover. In this thesis, we developed a benchmark labeled data set of high-resolution satellite imagery of agricultural fields in South Africa. We presented a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. The algorithm achieved similar high performance across agricultural types, including spectrally indistinct smallholder fields, and demonstrated the ability to generalize across large geographic areas. In sensitivity analyses, we determined multi-temporal images provided greater performance gains than the addition of multi-spectral bands. We also demonstrated how active learning can be incorporated in the algorithm to create smaller, more efficient training data sets, which reduced computational resources, minimized the need for humans to hand-label data, and boosted performance. We designed a patch-based uncertainty metric to drive the active learning framework, based on the regular grid of a crowdsourcing platform, and demonstrated how subject matter experts can be replaced with fleets of crowdsourcing workers. Our active learning algorithm achieved similar performance as an algorithm trained with randomly selected data, but with 62% less data samples. This thesis furthers the goal of providing accurate agricultural landcover maps, at a scale that is relevant for the dominant smallholder class. Accurate maps are crucial for monitoring and promoting agricultural production. Furthermore, improved agricultural landcover maps will aid a host of other applications, including landcover change assessments, cadastral surveys to strengthen smallholder land rights, and constraints for crop modeling and famine prediction.

Intelligent Vision On The SM9O Mini-Computer Basis And Applications

NASA Astrophysics Data System (ADS)

Hawryszkiw, J.

1985-02-01

Distinction has to be made between image processing and vision Image processing finds its roots in the strong tradition of linear signal processing and promotes geometrical transform techniques, such as fi I tering , compression, and restoration. Its purpose is to transform an image for a human observer to easily extract from that image information significant for him. For example edges after a gradient operator, or a specific direction after a directional filtering operation. Image processing consists in fact in a set of local or global space-time transforms. The interpretation of the final image is done by the human observer. The purpose of vision is to extract the semantic content of the image. The machine can then understand that content, and run a process of decision, which turns into an action. Thus, intel I i gent vision depends on - Image processing - Pattern recognition - Artificial intel I igence

Quantum vision in three dimensions

NASA Astrophysics Data System (ADS)

Roth, Yehuda

We present four models for describing a 3-D vision. Similar to the mirror scenario, our models allow 3-D vision with no need for additional accessories such as stereoscopic glasses or a hologram film. These four models are based on brain interpretation rather than pure objective encryption. We consider the observer "subjective" selection of a measuring device and the corresponding quantum collapse into one of his selected states, as a tool for interpreting reality in according to the observer concepts. This is the basic concept of our study and it is introduced in the first model. Other models suggests "soften" versions that might be much easier to implement. Our quantum interpretation approach contribute to the following fields. In technology the proposed models can be implemented into real devices, allowing 3-D vision without additional accessories. Artificial intelligence: In the desire to create a machine that exchange information by using human terminologies, our interpretation approach seems to be appropriate.

Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer

NASA Astrophysics Data System (ADS)

Sun, Jia; Shi, Shuo; Gong, Wei; Yang, Jian; Du, Lin; Song, Shalei; Chen, Biwu; Zhang, Zhenbing

2017-01-01

Fast and nondestructive assessment of leaf nitrogen concentration (LNC) is critical for crop growth diagnosis and nitrogen management guidance. In the last decade, multispectral LiDAR (MSL) systems have promoted developments in the earth and ecological sciences with the additional spectral information. With more wavelengths than MSL, the hyperspectral LiDAR (HSL) system provides greater possibilities for remote sensing crop physiological conditions. This study compared the performance of ASD FieldSpec Pro FR, MSL, and HSL for estimating rice (Oryza sativa) LNC. Spectral reflectance and biochemical composition were determined in rice leaves of different cultivars (Yongyou 4949 and Yangliangyou 6) throughout two growing seasons (2014-2015). Results demonstrated that HSL provided the best indicator for predicting rice LNC, yielding a coefficient of determination (R2) of 0.74 and a root mean square error of 2.80 mg/g with a support vector machine, similar to the performance of ASD (R2 = 0.73). Estimation of rice LNC could be significantly improved with the finer spectral resolution of HSL compared with MSL (R2 = 0.56).

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images.

PubMed

Gumaei, Abdu; Sammouda, Rachid; Al-Salman, Abdul Malik; Alsanad, Ahmed

2018-05-15

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang's method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used.

Development of spectral indices for roofing material condition status detection using field spectroscopy and WorldView-3 data

NASA Astrophysics Data System (ADS)

Samsudin, Sarah Hanim; Shafri, Helmi Z. M.; Hamedianfar, Alireza

2016-04-01

Status observations of roofing material degradation are constantly evolving due to urban feature heterogeneities. Although advanced classification techniques have been introduced to improve within-class impervious surface classifications, these techniques involve complex processing and high computation times. This study integrates field spectroscopy and satellite multispectral remote sensing data to generate degradation status maps of concrete and metal roofing materials. Field spectroscopy data were used as bases for selecting suitable bands for spectral index development because of the limited number of multispectral bands. Mapping methods for roof degradation status were established for metal and concrete roofing materials by developing the normalized difference concrete condition index (NDCCI) and the normalized difference metal condition index (NDMCI). Results indicate that the accuracies achieved using the spectral indices are higher than those obtained using supervised pixel-based classification. The NDCCI generated an accuracy of 84.44%, whereas the support vector machine (SVM) approach yielded an accuracy of 73.06%. The NDMCI obtained an accuracy of 94.17% compared with 62.5% for the SVM approach. These findings support the suitability of the developed spectral index methods for determining roof degradation statuses from satellite observations in heterogeneous urban environments.

Multiple Optical Filter Design Simulation Results

NASA Astrophysics Data System (ADS)

Mendelsohn, J.; Englund, D. C.

1986-10-01

In this paper we continue our investigation of the application of matched filters to robotic vision problems. Specifically, we are concerned with the tray-picking problem. Our principal interest in this paper is the examination of summation affects which arise from attempting to reduce the matched filter memory size by averaging of matched filters. While the implementation of matched filtering theory to applications in pattern recognition or machine vision is ideally through the use of optics and optical correlators, in this paper the results were obtained through a digital simulation of the optical process.

Before They Can Speak, They Must Know.

ERIC Educational Resources Information Center

Cromie, William J.; Edson, Lee

1984-01-01

Intelligent relationships with people are among the goals for tomorrow's computers. Knowledge-based systems used and being developed to achieve these goals are discussed. Automatic learning, producing inferences, parallelism, program languages, friendly machines, computer vision, and biomodels are among the topics considered. (JN)

Robust crop and weed segmentation under uncontrolled outdoor illumination

USDA-ARS?s Scientific Manuscript database

A new machine vision for weed detection was developed from RGB color model images. Processes included in the algorithm for the detection were excessive green conversion, threshold value computation by statistical analysis, adaptive image segmentation by adjusting the threshold value, median filter, ...

Teaching Camera Calibration by a Constructivist Methodology

ERIC Educational Resources Information Center

Samper, D.; Santolaria, J.; Pastor, J. J.; Aguilar, J. J.

2010-01-01

This article describes the Metrovisionlab simulation software and practical sessions designed to teach the most important machine vision camera calibration aspects in courses for senior undergraduate students. By following a constructivist methodology, having received introductory theoretical classes, students use the Metrovisionlab application to…

CATEGORIZATION OF EXTRANEOUS MATTER IN COTTON USING MACHINE VISION SYSTEMS

USDA-ARS?s Scientific Manuscript database

The Cotton Trash Identification System (CTIS) was developed at the Southwestern Cotton Ginning Research Laboratory to identify and categorize extraneous matter in cotton. The CTIS bark/grass categorization was evaluated with USDA-Agricultural Marketing Service (AMS) extraneous matter calls assigned ...

Camera positioning and calibration techniques for integrating traffic surveillance video systems with machine-vision vehicle detection devices.

DOT National Transportation Integrated Search

2002-12-01

The Virginia Department of Transportation, like many other transportation agencies, has invested significantly in extensive closed circuit television (CCTV) systems to monitor freeways in urban areas. Although these systems have proven very effective...

Compact Microscope Imaging System with Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark

2004-01-01

The figure presents selected views of a compact microscope imaging system (CMIS) that includes a miniature video microscope, a Cartesian robot (a computer- controlled three-dimensional translation stage), and machine-vision and control subsystems. The CMIS was built from commercial off-the-shelf instrumentation, computer hardware and software, and custom machine-vision software. The machine-vision and control subsystems include adaptive neural networks that afford a measure of artificial intelligence. The CMIS can perform several automated tasks with accuracy and repeatability . tasks that, heretofore, have required the full attention of human technicians using relatively bulky conventional microscopes. In addition, the automation and control capabilities of the system inherently include a capability for remote control. Unlike human technicians, the CMIS is not at risk of becoming fatigued or distracted: theoretically, it can perform continuously at the level of the best human technicians. In its capabilities for remote control and for relieving human technicians of tedious routine tasks, the CMIS is expected to be especially useful in biomedical research, materials science, inspection of parts on industrial production lines, and space science. The CMIS can automatically focus on and scan a microscope sample, find areas of interest, record the resulting images, and analyze images from multiple samples simultaneously. Automatic focusing is an iterative process: The translation stage is used to move the microscope along its optical axis in a succession of coarse, medium, and fine steps. A fast Fourier transform (FFT) of the image is computed at each step, and the FFT is analyzed for its spatial-frequency content. The microscope position that results in the greatest dispersal of FFT content toward high spatial frequencies (indicating that the image shows the greatest amount of detail) is deemed to be the focal position.

Detection of oranges from a color image of an orange tree

NASA Astrophysics Data System (ADS)

Weeks, Arthur R.; Gallagher, A.; Eriksson, J.

1999-10-01

The progress of robotic and machine vision technology has increased the demand for sophisticated methods for performing automatic harvesting of fruit. The harvesting of fruit, until recently, has been performed manually and is quite labor intensive. An automatic robot harvesting system that uses machine vision to locate and extract the fruit would free the agricultural industry from the ups and downs of the labor market. The environment in which robotic fruit harvesters must work presents many challenges due to the inherent variability from one location to the next. This paper takes a step towards this goal by outlining a machine vision algorithm that detects and accurately locates oranges from a color image of an orange tree. Previous work in this area has focused on differentiating the orange regions from the rest of the picture and not locating the actual oranges themselves. Failure to locate the oranges, however, leads to a reduced number of successful pick attempts. This paper presents a new approach for orange region segmentation in which the circumference of the individual oranges as well as partially occluded oranges are located. Accurately defining the circumference of each orange allows a robotic harvester to cut the stem of the orange by either scanning the top of the orange with a laser or by directing a robotic arm towards the stem to automatically cut it. A modified version of the K- means algorithm is used to initially segment the oranges from the canopy of the orange tree. Morphological processing is then used to locate occluded oranges and an iterative circle finding algorithm is used to define the circumference of the segmented oranges.

Anomalous Cases of Astronaut Helmet Detection

NASA Technical Reports Server (NTRS)

Dolph, Chester; Moore, Andrew J.; Schubert, Matthew; Woodell, Glenn

2015-01-01

An astronaut's helmet is an invariant, rigid image element that is well suited for identification and tracking using current machine vision technology. Future space exploration will benefit from the development of astronaut detection software for search and rescue missions based on EVA helmet identification. However, helmets are solid white, except for metal brackets to attach accessories such as supplementary lights. We compared the performance of a widely used machine vision pipeline on a standard-issue NASA helmet with and without affixed experimental feature-rich patterns. Performance on the patterned helmet was far more robust. We found that four different feature-rich patterns are sufficient to identify a helmet and determine orientation as it is rotated about the yaw, pitch, and roll axes. During helmet rotation the field of view changes to frames containing parts of two or more feature-rich patterns. We took reference images in these locations to fill in detection gaps. These multiple feature-rich patterns references added substantial benefit to detection, however, they generated the majority of the anomalous cases. In these few instances, our algorithm keys in on one feature-rich pattern of the multiple feature-rich pattern reference and makes an incorrect prediction of the location of the other feature-rich patterns. We describe and make recommendations on ways to mitigate anomalous cases in which detection of one or more feature-rich patterns fails. While the number of cases is only a small percentage of the tested helmet orientations, they illustrate important design considerations for future spacesuits. In addition to our four successful feature-rich patterns, we present unsuccessful patterns and discuss the cause of their poor performance from a machine vision perspective. Future helmets designed with these considerations will enable automated astronaut detection and thereby enhance mission operations and extraterrestrial search and rescue.

Sensory and Quality Evaluation of Traditional Compared with Power Ultrasound Processed Corn (Zea Mays) Tortilla Chips.

PubMed

Janve, Bhaskar; Yang, Wade; Sims, Charles

2015-06-01

Power ultrasound reduces the traditional corn steeping time from 18 to 1.5 h during tortilla chips dough (masa) processing. This study sought to examine consumer (n = 99) acceptability and quality of tortilla chips made from the masa by traditional compared with ultrasonic methods. Overall appearance, flavor, and texture acceptability scores were evaluated using a 9-point hedonic scale. The baked chips (process intermediate) before and after frying (finished product) were analyzed using a texture analyzer and machine vision. The texture values were determined using the 3-point bend test using breaking force gradient (BFG), peak breaking force (PBF), and breaking distance (BD). The fracturing properties determined by the crisp fracture support rig using fracture force gradient (FFG), peak fracture force (PFF), and fracture distance (FD). The machine vision evaluated the total surface area, lightness (L), color difference (ΔE), Hue (°h), and Chroma (C*). The results were evaluated by analysis of variance and means were separated using Tukey's test. Machine vision values of L, °h, were higher (P < 0.05) and ΔE was lower (P < 0.05) for fried and L, °h were significantly (P < 0.05) higher for baked chips produced from ultra-sonication as compare to traditional. Baked chips texture for ultra-sonication was significantly higher (P < 0.05) on BFG, BPD, PFF, and FD. Fried tortilla chips texture were higher significantly (P < 0.05) in BFG and PFF for ultra-sonication than traditional processing. However, the instrumental differences were not detected in sensory analysis, concluding possibility of power ultrasound as potential tortilla chips processing aid. © 2015 Institute of Food Technologists®

Remote Sensing Application to Land Use Classification in a Rapidly Changing Agricultural/Urban Area: City of Virginia Beach, Virginia. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Odenyo, V. A. O.

1975-01-01

Remote sensing data on computer-compatible tapes of LANDSAT 1 multispectral scanner imager were analyzed to generate a land use map of the City of Virginia Beach. All four bands were used in both the supervised and unsupervised approaches with the LAYSYS software system. Color IR imagery of a U-2 flight of the same area was also digitized and two sample areas were analyzed via the unsupervised approach. The relationships between the mapped land use and the soils of the area were investigated. A land use land cover map at a scale of 1:24,000 was obtained from the supervised analysis of LANDSAT 1 data. It was concluded that machine analysis of remote sensing data to produce land use maps was feasible; that the LAYSYS software system was usable for this purpose; and that the machine analysis was capable of extracting detailed information from the relatively small scale LANDSAT data in a much shorter time without compromising accuracy.

Machine-vision-based roadway health monitoring and assessment : development of a shape-based pavement-crack-detection approach.

DOT National Transportation Integrated Search

2016-01-01

State highway agencies (SHAs) routinely employ semi-automated and automated image-based methods for network-level : pavement-cracking data collection, and there are different types of pavement-cracking data collected by SHAs for reporting and : manag...

A Starter's Guide to Artificial Intelligence.

ERIC Educational Resources Information Center

McConnell, Barry A.; McConnell, Nancy J.

1988-01-01

Discussion of the history and development of artificial intelligence (AI) highlights a bibliography of introductory books on various aspects of AI, including AI programing; problem solving; automated reasoning; game playing; natural language; expert systems; machine learning; robotics and vision; critics of AI; and representative software. (LRW)

Detection of eviscerated poultry spleen enlargement by machine vision

NASA Astrophysics Data System (ADS)

Tao, Yang; Shao, June J.; Skeeles, John K.; Chen, Yud-Ren

1999-01-01

The size of a poultry spleen is an indication of whether the bird is wholesomeness or has a virus-related disease. This study explored the possibility of detecting poultry spleen enlargement with a computer imaging system to assist human inspectors in food safety inspections. Images of 45-day-old hybrid turkey internal viscera were taken using fluorescent and UV lighting systems. Image processing algorithms including linear transformation, morphological operations, and statistical analyses were developed to distinguish the spleen from its surroundings and then to detect abnormal spleens. Experimental results demonstrated that the imaging method could effectively distinguish spleens from other organ and intestine. Based on a total sample of 57 birds, the classification rates were 92% from a self-test set, and 95% from an independent test set for the correct detection of normal and abnormal birds. The methodology indicated the feasibility of using automated machine vision systems in the future to inspect internal organs and check the wholesomeness of poultry carcasses.

Vision Guided Intelligent Robot Design And Experiments

NASA Astrophysics Data System (ADS)

Slutzky, G. D.; Hall, E. L.

1988-02-01

The concept of an intelligent robot is an important topic combining sensors, manipulators, and artificial intelligence to design a useful machine. Vision systems, tactile sensors, proximity switches and other sensors provide the elements necessary for simple game playing as well as industrial applications. These sensors permit adaption to a changing environment. The AI techniques permit advanced forms of decision making, adaptive responses, and learning while the manipulator provides the ability to perform various tasks. Computer languages such as LISP and OPS5, have been utilized to achieve expert systems approaches in solving real world problems. The purpose of this paper is to describe several examples of visually guided intelligent robots including both stationary and mobile robots. Demonstrations will be presented of a system for constructing and solving a popular peg game, a robot lawn mower, and a box stacking robot. The experience gained from these and other systems provide insight into what may be realistically expected from the next generation of intelligent machines.

FAIR principles and the IEDB: short-term improvements and a long-term vision of OBO-foundry mediated machine-actionable interoperability

PubMed Central

Vita, Randi; Overton, James A; Mungall, Christopher J; Sette, Alessandro

2018-01-01

Abstract The Immune Epitope Database (IEDB), at www.iedb.org, has the mission to make published experimental data relating to the recognition of immune epitopes easily available to the scientific public. By presenting curated data in a searchable database, we have liberated it from the tables and figures of journal articles, making it more accessible and usable by immunologists. Recently, the principles of Findability, Accessibility, Interoperability and Reusability have been formulated as goals that data repositories should meet to enhance the usefulness of their data holdings. We here examine how the IEDB complies with these principles and identify broad areas of success, but also areas for improvement. We describe short-term improvements to the IEDB that are being implemented now, as well as a long-term vision of true ‘machine-actionable interoperability’, which we believe will require community agreement on standardization of knowledge representation that can be built on top of the shared use of ontologies. PMID:29688354

Machine vision based teleoperation aid

NASA Technical Reports Server (NTRS)

Hoff, William A.; Gatrell, Lance B.; Spofford, John R.

1991-01-01

When teleoperating a robot using video from a remote camera, it is difficult for the operator to gauge depth and orientation from a single view. In addition, there are situations where a camera mounted for viewing by the teleoperator during a teleoperation task may not be able to see the tool tip, or the viewing angle may not be intuitive (requiring extensive training to reduce the risk of incorrect or dangerous moves by the teleoperator). A machine vision based teleoperator aid is presented which uses the operator's camera view to compute an object's pose (position and orientation), and then overlays onto the operator's screen information on the object's current and desired positions. The operator can choose to display orientation and translation information as graphics and/or text. This aid provides easily assimilated depth and relative orientation information to the teleoperator. The camera may be mounted at any known orientation relative to the tool tip. A preliminary experiment with human operators was conducted and showed that task accuracies were significantly greater with than without this aid.

Calibrators measurement system for headlamp tester of motor vehicle base on machine vision

NASA Astrophysics Data System (ADS)

Pan, Yue; Zhang, Fan; Xu, Xi-ping; Zheng, Zhe

2014-09-01

With the development of photoelectric detection technology, machine vision has a wider use in the field of industry. The paper mainly introduces auto lamps tester calibrator measuring system, of which CCD image sampling system is the core. Also, it shows the measuring principle of optical axial angle and light intensity, and proves the linear relationship between calibrator's facula illumination and image plane illumination. The paper provides an important specification of CCD imaging system. Image processing by MATLAB can get flare's geometric midpoint and average gray level. By fitting the statistics via the method of the least square, we can get regression equation of illumination and gray level. It analyzes the error of experimental result of measurement system, and gives the standard uncertainty of synthesis and the resource of optical axial angle. Optical axial angle's average measuring accuracy is controlled within 40''. The whole testing process uses digital means instead of artificial factors, which has higher accuracy, more repeatability and better mentality than any other measuring systems.

Some distinguishing characteristics of contour and texture phenomena in images

NASA Technical Reports Server (NTRS)

Jobson, Daniel J.

1992-01-01

The development of generalized contour/texture discrimination techniques is a central element necessary for machine vision recognition and interpretation of arbitrary images. Here, the visual perception of texture, selected studies of texture analysis in machine vision, and diverse small samples of contour and texture are all used to provide insights into the fundamental characteristics of contour and texture. From these, an experimental discrimination scheme is developed and tested on a battery of natural images. The visual perception of texture defined fine texture as a subclass which is interpreted as shading and is distinct from coarse figural similarity textures. Also, perception defined the smallest scale for contour/texture discrimination as eight to nine visual acuity units. Three contour/texture discrimination parameters were found to be moderately successful for this scale discrimination: (1) lightness change in a blurred version of the image, (2) change in lightness change in the original image, and (3) percent change in edge counts relative to local maximum.

KENNEDY SPACE CENTER, FLA. - Center Director Jim Kennedy talks to students in Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla. Kennedy made the trip with NASA astronaut Kay Hire to share the agency’s new vision for space exploration with the next generation of explorers. Kennedy is talking with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

NASA Image and Video Library

2004-02-20

KENNEDY SPACE CENTER, FLA. - Center Director Jim Kennedy talks to students in Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla. Kennedy made the trip with NASA astronaut Kay Hire to share the agency’s new vision for space exploration with the next generation of explorers. Kennedy is talking with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

KENNEDY SPACE CENTER, FLA. - Astronaut Kay Hire talks to students in Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla. She joined Center Director Jim Kennedy in sharing the agency’s new vision for space exploration with the next generation of explorers. Kennedy is talking with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

NASA Image and Video Library

2004-02-20

KENNEDY SPACE CENTER, FLA. - Astronaut Kay Hire talks to students in Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla. She joined Center Director Jim Kennedy in sharing the agency’s new vision for space exploration with the next generation of explorers. Kennedy is talking with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

KENNEDY SPACE CENTER, FLA. - Students at Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla., listen attentively to astronaut Kay Hire. She and Center Director Jim Kennedy were at the school to share the agency’s new vision for space exploration with the next generation of explorers. Kennedy is talking with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

NASA Image and Video Library

2004-02-20

KENNEDY SPACE CENTER, FLA. - Students at Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla., listen attentively to astronaut Kay Hire. She and Center Director Jim Kennedy were at the school to share the agency’s new vision for space exploration with the next generation of explorers. Kennedy is talking with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

Identification by machine vision of the rate of motor activity decline as a lifespan predictor in C. elegans

PubMed Central

Hsu, Ao-Lin; Feng, Zhaoyang; Hsieh, Meng-Yin; Xu, X. Z. Shawn

2009-01-01

One challenge in aging research concerns identifying physiological parameters or biomarkers that can reflect the physical health of an animal and predict its lifespan. In C. elegans, a model organism widely used in aging research, motor deficits develop in old worms. Here we employed machine vision to quantify worm locomotion behavior throughout lifespan. We confirm that aging worms undergo a progressive decline in motor activity, beginning in early life. Importantly, the rate of motor activity decline rather than the absolute motor activity in the early-to-mid life of individual worms in an isogenic population inversely correlates with their lifespan, and thus may serve as a lifespan predictor. Long-lived mutant strains with deficits in insulin/IGF-1 signaling or food intake display a reduction in the rate of motor activity decline, suggesting that this parameter might also be used for across-strain comparison of healthspan. Our work identifies an endogenous physiological parameter for lifespan prediction and healthspan comparison. PMID:18255194

Identification by machine vision of the rate of motor activity decline as a lifespan predictor in C. elegans.

PubMed

Hsu, Ao-Lin; Feng, Zhaoyang; Hsieh, Meng-Yin; Xu, X Z Shawn

2009-09-01

One challenge in aging research concerns identifying physiological parameters or biomarkers that can reflect the physical health of an animal and predict its lifespan. In C. elegans, a model organism widely used in aging research, motor deficits develop in old worms. Here we employed machine vision to quantify worm locomotion behavior throughout lifespan. We confirm that aging worms undergo a progressive decline in motor activity, beginning in early life. Importantly, the rate of motor activity decline rather than the absolute motor activity in the early-to-mid life of individual worms in an isogenic population inversely correlates with their lifespan, and thus may serve as a lifespan predictor. Long-lived mutant strains with deficits in insulin/IGF-1 signaling or food intake display a reduction in the rate of motor activity decline, suggesting that this parameter might also be used for across-strain comparison of healthspan. Our work identifies an endogenous physiological parameter for lifespan prediction and healthspan comparison.

Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

NASA Astrophysics Data System (ADS)

Sanal Kumar, K. P.; Bhavani, R., Dr.

2017-08-01

Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

Prediction of pork loin quality using online computer vision system and artificial intelligence model.

PubMed

Sun, Xin; Young, Jennifer; Liu, Jeng-Hung; Newman, David

2018-06-01

The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds. Copyright © 2018 Elsevier Ltd. All rights reserved.

Fish swarm intelligent to optimize real time monitoring of chips drying using machine vision

NASA Astrophysics Data System (ADS)

Hendrawan, Y.; Hawa, L. C.; Damayanti, R.

2018-03-01

This study attempted to apply machine vision-based chips drying monitoring system which is able to optimise the drying process of cassava chips. The objective of this study is to propose fish swarm intelligent (FSI) optimization algorithms to find the most significant set of image features suitable for predicting water content of cassava chips during drying process using artificial neural network model (ANN). Feature selection entails choosing the feature subset that maximizes the prediction accuracy of ANN. Multi-Objective Optimization (MOO) was used in this study which consisted of prediction accuracy maximization and feature-subset size minimization. The results showed that the best feature subset i.e. grey mean, L(Lab) Mean, a(Lab) energy, red entropy, hue contrast, and grey homogeneity. The best feature subset has been tested successfully in ANN model to describe the relationship between image features and water content of cassava chips during drying process with R2 of real and predicted data was equal to 0.9.

Using human brain activity to guide machine learning.

PubMed

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

Deployment and evaluation of a dual-sensor autofocusing method for on-machine measurement of patterns of small holes on freeform surfaces.

PubMed

Chen, Xiaomei; Longstaff, Andrew; Fletcher, Simon; Myers, Alan

2014-04-01

This paper presents and evaluates an active dual-sensor autofocusing system that combines an optical vision sensor and a tactile probe for autofocusing on arrays of small holes on freeform surfaces. The system has been tested on a two-axis test rig and then integrated onto a three-axis computer numerical control (CNC) milling machine, where the aim is to rapidly and controllably measure the hole position errors while the part is still on the machine. The principle of operation is for the tactile probe to locate the nominal positions of holes, and the optical vision sensor follows to focus and capture the images of the holes. The images are then processed to provide hole position measurement. In this paper, the autofocusing deviations are analyzed. First, the deviations caused by the geometric errors of the axes on which the dual-sensor unit is deployed are estimated to be 11 μm when deployed on a test rig and 7 μm on the CNC machine tool. Subsequently, the autofocusing deviations caused by the interaction of the tactile probe, surface, and small hole are mathematically analyzed and evaluated. The deviations are a result of the tactile probe radius, the curvatures at the positions where small holes are drilled on the freeform surface, and the effect of the position error of the hole on focusing. An example case study is provided for the measurement of a pattern of small holes on an elliptical cylinder on the two machines. The absolute sum of the autofocusing deviations is 118 μm on the test rig and 144 μm on the machine tool. This is much less than the 500 μm depth of field of the optical microscope. Therefore, the method is capable of capturing a group of clear images of the small holes on this workpiece for either implementation.

Image Classification Workflow Using Machine Learning Methods

NASA Astrophysics Data System (ADS)

Christoffersen, M. S.; Roser, M.; Valadez-Vergara, R.; Fernández-Vega, J. A.; Pierce, S. A.; Arora, R.

2016-12-01

Recent increases in the availability and quality of remote sensing datasets have fueled an increasing number of scientifically significant discoveries based on land use classification and land use change analysis. However, much of the software made to work with remote sensing data products, specifically multispectral images, is commercial and often prohibitively expensive. The free to use solutions that are currently available come bundled up as small parts of much larger programs that are very susceptible to bugs and difficult to install and configure. What is needed is a compact, easy to use set of tools to perform land use analysis on multispectral images. To address this need, we have developed software using the Python programming language with the sole function of land use classification and land use change analysis. We chose Python to develop our software because it is relatively readable, has a large body of relevant third party libraries such as GDAL and Spectral Python, and is free to install and use on Windows, Linux, and Macintosh operating systems. In order to test our classification software, we performed a K-means unsupervised classification, Gaussian Maximum Likelihood supervised classification, and a Mahalanobis Distance based supervised classification. The images used for testing were three Landsat rasters of Austin, Texas with a spatial resolution of 60 meters for the years of 1984 and 1999, and 30 meters for the year 2015. The testing dataset was easily downloaded using the Earth Explorer application produced by the USGS. The software should be able to perform classification based on any set of multispectral rasters with little to no modification. Our software makes the ease of land use classification using commercial software available without an expensive license.

KSC-04PD-1998

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. (From left) Dr. Julian Earls, director of NASA Glenn Research Center, astronaut Leland Melvin, Sara Thompson, team lead, and KSC Deputy Director Dr. Woodrow Whitlow Jr. pose for a photo at Ronald E. McNair High School in Atlanta, a NASA Explorer School, after a presentation. Dr. Whitlow visited the school to share The vision for space exploration with the next generation of explorers. Whitlow talked with students about our destiny as explorers, NASAs stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space. Dr. Earls discussed the future and the vision for space, plus the NASA careers needed to meet the vision. Melvin talked about the importance of teamwork and what it takes for mission success.

KSC-04PD-1988

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. Dr. Julian Earls, director of the NASA Glenn Research Center, talks to students at Ronald E. McNair High School in Atlanta, a NASA Explorer School. He accompanied KSC Deputy Director Dr. Woodrow Whitlow Jr., who is visiting to the school to share the vision for space exploration with the next generation of explorers. Dr. Earls discussed the future and the vision for space, plus the NASA careers needed to meet the vision. Astronaut Leland Melvin (far right) accompanied Whitlow, talking with students about the importance of teamwork and what it takes for mission success. Whitlow talked with students about our destiny as explorers, NASAs stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

Fuzzy Petri nets to model vision system decisions within a flexible manufacturing system

NASA Astrophysics Data System (ADS)

Hanna, Moheb M.; Buck, A. A.; Smith, R.

1994-10-01

The paper presents a Petri net approach to modelling, monitoring and control of the behavior of an FMS cell. The FMS cell described comprises a pick and place robot, vision system, CNC-milling machine and 3 conveyors. The work illustrates how the block diagrams in a hierarchical structure can be used to describe events at different levels of abstraction. It focuses on Fuzzy Petri nets (Fuzzy logic with Petri nets) including an artificial neural network (Fuzzy Neural Petri nets) to model and control vision system decisions and robot sequences within an FMS cell. This methodology can be used as a graphical modelling tool to monitor and control the imprecise, vague and uncertain situations, and determine the quality of the output product of an FMS cell.

Automatic Welding System of Aluminum Pipe by Monitoring Backside Image of Molten Pool Using Vision Sensor

NASA Astrophysics Data System (ADS)

Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo

An automatic welding system using Tungsten Inert Gas (TIG) welding with vision sensor for welding of aluminum pipe was constructed. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position and moving welding torch with the AC welding machine. The monitoring system consists of a vision sensor using a charge-coupled device (CCD) camera to monitor backside image of molten pool. The captured image was processed to recognize the edge of molten pool by image processing algorithm. Neural network model for welding speed control were constructed to perform the process automatically. From the experimental results it shows the effectiveness of the control system confirmed by good detection of molten pool and sound weld of experimental result.

Evaluation and implementation of a machine vision system to categorize extraneous matter in cotton

USDA-ARS?s Scientific Manuscript database

The Cotton Trash Identification System (CTIS) developed at the Southwestern Cotton Ginning Research Laboratory was evaluated for identification and categorization of extraneous matter (EM) in cotton. The system’s categorization of trash objects in cotton images was evaluated against Agricultural Mar...

Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging

USDA-ARS?s Scientific Manuscript database

Currently, blueberries are inspected and sorted by color, size and/or firmness (or softness) in packinghouses, using different inspection techniques like machine vision and mechanical vibration or impact. A new inspection technique is needed for effectively assessing both external features and inter...

Sensory Interactive Teleoperator Robotic Grasping

NASA Technical Reports Server (NTRS)

Alark, Keli; Lumia, Ron

1997-01-01

As the technological world strives for efficiency, the need for economical equipment that increases operator proficiency in minimal time is fundamental. This system links a CCD camera, a controller and a robotic arm to a computer vision system to provide an alternative method of image analysis. The machine vision system which was employed possesses software tools for acquiring and analyzing images which are received through a CCD camera. After feature extraction on the object in the image was performed, information about the object's location, orientation and distance from the robotic gripper is sent to the robot controller so that the robot can manipulate the object.

Using artificial intelligence to bring evidence-based medicine a step closer to making the individual difference.

PubMed

Sissons, B; Gray, W A; Bater, A; Morrey, D

2007-03-01

The vision of evidence-based medicine is that of experienced clinicians systematically using the best research evidence to meet the individual patient's needs. This vision remains distant from clinical reality, as no complete methodology exists to apply objective, population-based research evidence to the needs of an individual real-world patient. We describe an approach, based on techniques from machine learning, to bridge this gap between evidence and individual patients in oncology. We examine existing proposals for tackling this gap and the relative benefits and challenges of our proposed, k-nearest-neighbour-based, approach.

Adaptive Feedback in Local Coordinates for Real-time Vision-Based Motion Control Over Long Distances

NASA Astrophysics Data System (ADS)

Aref, M. M.; Astola, P.; Vihonen, J.; Tabus, I.; Ghabcheloo, R.; Mattila, J.

2018-03-01

We studied the differences in noise-effects, depth-correlated behavior of sensors, and errors caused by mapping between coordinate systems in robotic applications of machine vision. In particular, the highly range-dependent noise densities for semi-unknown object detection were considered. An equation is proposed to adapt estimation rules to dramatic changes of noise over longer distances. This algorithm also benefits the smooth feedback of wheels to overcome variable latencies of visual perception feedback. Experimental evaluation of the integrated system is presented with/without the algorithm to highlight its effectiveness.

Survey on diagnosis of diseases from retinal images

NASA Astrophysics Data System (ADS)

Das, Sneha; Malathy, C.

2018-04-01

Retina is a thin membranous layer of tissue that occupies at the back of the eye which provides central vision needed for daily routines. Identifying retinal diseases at the early stage is a challenging task since healthy retina is required for central vision. Several retinal diseases affect the eye such as retinal tear, retinal detachment, glaucoma, macular hole and macular degeneration etc. These maladies will encounter a secondary growth in the close future as the age of the person increases. A survey is made which tells about the diagnosis of the retinal diseases from the retinal images using machine learning techniques.

A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data

PubMed Central

Navarro, Pedro J.; Fernández, Carlos; Borraz, Raúl; Alonso, Diego

2016-01-01

This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%). PMID:28025565

A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data.

PubMed

Navarro, Pedro J; Fernández, Carlos; Borraz, Raúl; Alonso, Diego

2016-12-23

This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).

Camera system for multispectral imaging of documents

NASA Astrophysics Data System (ADS)

Christens-Barry, William A.; Boydston, Kenneth; France, Fenella G.; Knox, Keith T.; Easton, Roger L., Jr.; Toth, Michael B.

2009-02-01

A spectral imaging system comprising a 39-Mpixel monochrome camera, LED-based narrowband illumination, and acquisition/control software has been designed for investigations of cultural heritage objects. Notable attributes of this system, referred to as EurekaVision, include: streamlined workflow, flexibility, provision of well-structured data and metadata for downstream processing, and illumination that is safer for the artifacts. The system design builds upon experience gained while imaging the Archimedes Palimpsest and has been used in studies of a number of important objects in the LOC collection. This paper describes practical issues that were considered by EurekaVision to address key research questions for the study of fragile and unique cultural objects over a range of spectral bands. The system is intended to capture important digital records for access by researchers, professionals, and the public. The system was first used for spectral imaging of the 1507 world map by Martin Waldseemueller, the first printed map to reference "America." It was also used to image sections of the Carta Marina 1516 map by the same cartographer for comparative purposes. An updated version of the system is now being utilized by the Preservation Research and Testing Division of the Library of Congress.

Micro Fluidic Channel Machining on Fused Silica Glass Using Powder Blasting

PubMed Central

Jang, Ho-Su; Cho, Myeong-Woo; Park, Dong-Sam

2008-01-01

In this study, micro fluid channels are machined on fused silica glass via powder blasting, a mechanical etching process, and the machining characteristics of the channels are experimentally evaluated. In the process, material removal is performed by the collision of micro abrasives injected by highly compressed air on to the target surface. This approach can be characterized as an integration of brittle mode machining based on micro crack propagation. Fused silica glass, a high purity synthetic amorphous silicon dioxide, is selected as a workpiece material. It has a very low thermal expansion coefficient and excellent optical qualities and exceptional transmittance over a wide spectral range, especially in the ultraviolet range. The powder blasting process parameters affecting the machined results are injection pressure, abrasive particle size and density, stand-off distance, number of nozzle scanning, and shape/size of the required patterns. In this study, the influence of the number of nozzle scanning, abrasive particle size, and pattern size on the formation of micro channels is investigated. Machined shapes and surface roughness are measured using a 3-dimensional vision profiler and the results are discussed. PMID:27879730

Nonlinear programming for classification problems in machine learning

NASA Astrophysics Data System (ADS)

Astorino, Annabella; Fuduli, Antonio; Gaudioso, Manlio

2016-10-01

We survey some nonlinear models for classification problems arising in machine learning. In the last years this field has become more and more relevant due to a lot of practical applications, such as text and web classification, object recognition in machine vision, gene expression profile analysis, DNA and protein analysis, medical diagnosis, customer profiling etc. Classification deals with separation of sets by means of appropriate separation surfaces, which is generally obtained by solving a numerical optimization model. While linear separability is the basis of the most popular approach to classification, the Support Vector Machine (SVM), in the recent years using nonlinear separating surfaces has received some attention. The objective of this work is to recall some of such proposals, mainly in terms of the numerical optimization models. In particular we tackle the polyhedral, ellipsoidal, spherical and conical separation approaches and, for some of them, we also consider the semisupervised versions.

A new method for getting the three-dimensional curve of the groove of a spectacle frame by optical measuring

NASA Astrophysics Data System (ADS)

Rückwardt, M.; Göpfert, A.; Schnellhorn, M.; Correns, M.; Rosenberger, M.; Linß, G.

2010-07-01

Precise measuring of spectacle frames is an important field of quality assurance for opticians and their customers. Different supplier and a number of measuring methods are available but all of them are tactile ones. In this paper the possible employment of optical coordinate measuring machines is discussed for detecting the groove of a spectacle frame. The ambient conditions like deviation and measuring time are even multifaceted like quantity of quality characteristics and measuring objects itself and have to be tested. But the main challenge for an optical coordinate measuring machine is the blocked optical path, because the device under test is located behind an undercut. In this case it is necessary to deflect the beam of the machine for example with a rotating plane mirror. In the next step the difficulties of machine vision connecting to the spectacle frame are explained. Finally first results are given.

Sensory Aids for the Blind.

ERIC Educational Resources Information Center

National Academy of Sciences - National Research Council, Washington, DC. Committee on Prosthetics Research and Development.

The problems of providing sensory aids for the blind are presented and a report on the present status of aids discusses direct translation and recognition reading machines as well as mobility aids. Aspects of required research considered are the following: assessment of needs; vision, audition, taction, and multimodal communication; reading aids,…

Geometric Invariants and Object Recognition.

DTIC Science & Technology

1992-08-01

University of Chicago Press. Maybank , S.J. [1992], "The Projection of Two Non-coplanar Conics", in Geometric Invariance in Machine Vision, eds. J.L...J.L. Mundy and A. Zisserman, MIT Press, Cambridge, MA. Mundy, J.L., Kapur, .. , Maybank , S.J., and Quan, L. [1992a] "Geometric Inter- pretation of

The Need for Alternative Paradigms in Science and Engineering Education

ERIC Educational Resources Information Center

Baggi, Dennis L.

2007-01-01

There are two main claims in this article. First, that the classic pillars of engineering education, namely, traditional mathematics and differential equations, are merely a particular, if not old-fashioned, representation of a broader mathematical vision, which spans from Turing machine programming and symbolic productions sets to sub-symbolic…

Artificial Intelligence and the High School Computer Curriculum.

ERIC Educational Resources Information Center

Dillon, Richard W.

1993-01-01

Describes a four-part curriculum that can serve as a model for incorporating artificial intelligence (AI) into the high school computer curriculum. The model includes examining questions fundamental to AI, creating and designing an expert system, language processing, and creating programs that integrate machine vision with robotics and…

Theater-Level Stochastic Air-to-Air Engagement Modeling via Event Occurrence Networks Using Piecewise Polynomial Approximation

DTIC Science & Technology

2001-09-01

diagnosis natural language understanding circuit fault diagnosis pattern recognition machine vision nancial auditing map learning sensor... ACCA ACCB A ights degree of command and control FCC value is assumed to be the average of all the ACC values of the aircraft in the

ICPR-2016 - International Conference on Pattern Recognition

Science.gov Websites

Learning for Scene Understanding" Speakers ICPR2016 PAPER AWARDS Best Piero Zamperoni Student Paper -Paced Dictionary Learning for Cross-Domain Retrieval and Recognition Xu, Dan; Song, Jingkuan; Alameda discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and

Edge detection

NASA Astrophysics Data System (ADS)

Hildreth, E. C.

1985-09-01

For both biological systems and machines, vision begins with a large and unwieldly array of measurements of the amount of light reflected from surfaces in the environment. The goal of vision is to recover physical properties of objects in the scene such as the location of object boundaries and the structure, color and texture of object surfaces, from the two-dimensional image that is projected onto the eye or camera. This goal is not achieved in a single step: vision proceeds in stages, with each stage producing increasingly more useful descriptions of the image and then the scene. The first clues about the physical properties of the scene are provided by the changes of intensity in the image. The importance of intensity changes and edges in early visual processing has led to extensive research on their detection, description and use, both in computer and biological vision systems. This article reviews some of the theory that underlies the detection of edges, and the methods used to carry out this analysis.

Square tracking sensor for autonomous helicopter hover stabilization

NASA Astrophysics Data System (ADS)

Oertel, Carl-Henrik

1995-06-01

Sensors for synthetic vision are needed to extend the mission profiles of helicopters. A special task for various applications is the autonomous position hold of a helicopter above a ground fixed or moving target. As a proof of concept for a general synthetic vision solution a restricted machine vision system, which is capable of locating and tracking a special target, was developed by the Institute of Flight Mechanics of Deutsche Forschungsanstalt fur Luft- und Raumfahrt e.V. (i.e., German Aerospace Research Establishment). This sensor, which is specialized to detect and track a square, was integrated in the fly-by-wire helicopter ATTHeS (i.e., Advanced Technology Testing Helicopter System). An existing model following controller for the forward flight condition was adapted for the hover and low speed requirements of the flight vehicle. The special target, a black square with a length of one meter, was mounted on top of a car. Flight tests demonstrated the automatic stabilization of the helicopter above the moving car by synthetic vision.

Vision-based aircraft guidance

NASA Technical Reports Server (NTRS)

Menon, P. K.

1993-01-01

Early research on the development of machine vision algorithms to serve as pilot aids in aircraft flight operations is discussed. The research is useful for synthesizing new cockpit instrumentation that can enhance flight safety and efficiency. With the present work as the basis, future research will produce low-cost instrument by integrating a conventional TV camera together with off-the=shelf digitizing hardware for flight test verification. Initial focus of the research will be on developing pilot aids for clear-night operations. Latter part of the research will examine synthetic vision issues for poor visibility flight operations. Both research efforts will contribute towards the high-speed civil transport aircraft program. It is anticipated that the research reported here will also produce pilot aids for conducting helicopter flight operations during emergency search and rescue. The primary emphasis of the present research effort is on near-term, flight demonstrable technologies. This report discusses pilot aids for night landing and takeoff and synthetic vision as an aid to low visibility landing.

Integration of spectral, spatial and morphometric data into lithological mapping: A comparison of different Machine Learning Algorithms in the Kurdistan Region, NE Iraq

NASA Astrophysics Data System (ADS)

Othman, Arsalan A.; Gloaguen, Richard

2017-09-01

Lithological mapping in mountainous regions is often impeded by limited accessibility due to relief. This study aims to evaluate (1) the performance of different supervised classification approaches using remote sensing data and (2) the use of additional information such as geomorphology. We exemplify the methodology in the Bardi-Zard area in NE Iraq, a part of the Zagros Fold - Thrust Belt, known for its chromite deposits. We highlighted the improvement of remote sensing geological classification by integrating geomorphic features and spatial information in the classification scheme. We performed a Maximum Likelihood (ML) classification method besides two Machine Learning Algorithms (MLA): Support Vector Machine (SVM) and Random Forest (RF) to allow the joint use of geomorphic features, Band Ratio (BR), Principal Component Analysis (PCA), spatial information (spatial coordinates) and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The RF algorithm showed reliable results and discriminated serpentinite, talus and terrace deposits, red argillites with conglomerates and limestone, limy conglomerates and limestone conglomerates, tuffites interbedded with basic lavas, limestone and Metamorphosed limestone and reddish green shales. The best overall accuracy (∼80%) was achieved by Random Forest (RF) algorithms in the majority of the sixteen tested combination datasets.

Compact VLSI neural computer integrated with active pixel sensor for real-time ATR applications

NASA Astrophysics Data System (ADS)

Fang, Wai-Chi; Udomkesmalee, Gabriel; Alkalai, Leon

1997-04-01

A compact VLSI neural computer integrated with an active pixel sensor has been under development to mimic what is inherent in biological vision systems. This electronic eye- brain computer is targeted for real-time machine vision applications which require both high-bandwidth communication and high-performance computing for data sensing, synergy of multiple types of sensory information, feature extraction, target detection, target recognition, and control functions. The neural computer is based on a composite structure which combines Annealing Cellular Neural Network (ACNN) and Hierarchical Self-Organization Neural Network (HSONN). The ACNN architecture is a programmable and scalable multi- dimensional array of annealing neurons which are locally connected with their local neurons. Meanwhile, the HSONN adopts a hierarchical structure with nonlinear basis functions. The ACNN+HSONN neural computer is effectively designed to perform programmable functions for machine vision processing in all levels with its embedded host processor. It provides a two order-of-magnitude increase in computation power over the state-of-the-art microcomputer and DSP microelectronics. A compact current-mode VLSI design feasibility of the ACNN+HSONN neural computer is demonstrated by a 3D 16X8X9-cube neural processor chip design in a 2-micrometers CMOS technology. Integration of this neural computer as one slice of a 4'X4' multichip module into the 3D MCM based avionics architecture for NASA's New Millennium Program is also described.

An analysis of metropolitan land-use by machine processing of earth resources technology satellite data

NASA Technical Reports Server (NTRS)

Mausel, P. W.; Todd, W. J.; Baumgardner, M. F.

1976-01-01

A successful application of state-of-the-art remote sensing technology in classifying an urban area into its broad land use classes is reported. This research proves that numerous urban features are amenable to classification using ERTS multispectral data automatically processed by computer. Furthermore, such automatic data processing (ADP) techniques permit areal analysis on an unprecedented scale with a minimum expenditure of time. Also, classification results obtained using ADP procedures are consistent, comparable, and replicable. The results of classification are compared with the proposed U. S. G. S. land use classification system in order to determine the level of classification that is feasible to obtain through ERTS analysis of metropolitan areas.

Computationally efficient target classification in multispectral image data with Deep Neural Networks

NASA Astrophysics Data System (ADS)

Cavigelli, Lukas; Bernath, Dominic; Magno, Michele; Benini, Luca

2016-10-01

Detecting and classifying targets in video streams from surveillance cameras is a cumbersome, error-prone and expensive task. Often, the incurred costs are prohibitive for real-time monitoring. This leads to data being stored locally or transmitted to a central storage site for post-incident examination. The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats. An effective way to overcome these limitations is to build a smart camera that analyzes the data on-site, close to the sensor, and transmits alerts when relevant video sequences are detected. Deep neural networks (DNNs) have come to outperform humans in visual classifications tasks and are also performing exceptionally well on other computer vision tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be extended to make use of higher-dimensional input data such as multispectral data. We explore this opportunity in terms of achievable accuracy and required computational effort. To analyze the precision of DNNs for scene labeling in an urban surveillance scenario we have created a dataset with 8 classes obtained in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR snapshot sensor to assess the potential of multispectral image data for target classification. We evaluate several new DNNs, showing that the spectral information fused together with the RGB frames can be used to improve the accuracy of the system or to achieve similar accuracy with a 3x smaller computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even for scarcely occurring, but particularly interesting classes, such as cars, 75% of the pixels are labeled correctly with errors occurring only around the border of the objects. This high accuracy was obtained with a training set of only 30 labeled images, paving the way for fast adaptation to various application scenarios.

Computer vision for automatic inspection of agricultural produce

NASA Astrophysics Data System (ADS)

Molto, Enrique; Blasco, Jose; Benlloch, Jose V.

1999-01-01

Fruit and vegetables suffer different manipulations from the field to the final consumer. These are basically oriented towards the cleaning and selection of the product in homogeneous categories. For this reason, several research projects, aimed at fast, adequate produce sorting and quality control are currently under development around the world. Moreover, it is possible to find manual and semi- automatic commercial system capable of reasonably performing these tasks.However, in many cases, their accuracy is incompatible with current European market demands, which are constantly increasing. IVIA, the Valencian Research Institute of Agriculture, located in Spain, has been involved in several European projects related with machine vision for real-time inspection of various agricultural produces. This paper will focus on the work related with two products that have different requirements: fruit and olives. In the case of fruit, the Institute has developed a vision system capable of providing assessment of the external quality of single fruit to a robot that also receives information from other senors. The system use four different views of each fruit and has been tested on peaches, apples and citrus. Processing time of each image is under 500 ms using a conventional PC. The system provides information about primary and secondary color, blemishes and their extension, and stem presence and position, which allows further automatic orientation of the fruit in the final box using a robotic manipulator. Work carried out in olives was devoted to fast sorting of olives for consumption at table. A prototype has been developed to demonstrate the feasibility of a machine vision system capable of automatically sorting 2500 kg/h olives using low-cost conventional hardware.

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images

PubMed Central

Sammouda, Rachid; Al-Salman, Abdul Malik; Alsanad, Ahmed

2018-01-01

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used. PMID:29762519

Development of a body motion interactive system with a weight voting mechanism and computer vision technology

NASA Astrophysics Data System (ADS)

Lin, Chern-Sheng; Chen, Chia-Tse; Shei, Hung-Jung; Lay, Yun-Long; Chiu, Chuang-Chien

2012-09-01

This study develops a body motion interactive system with computer vision technology. This application combines interactive games, art performing, and exercise training system. Multiple image processing and computer vision technologies are used in this study. The system can calculate the characteristics of an object color, and then perform color segmentation. When there is a wrong action judgment, the system will avoid the error with a weight voting mechanism, which can set the condition score and weight value for the action judgment, and choose the best action judgment from the weight voting mechanism. Finally, this study estimated the reliability of the system in order to make improvements. The results showed that, this method has good effect on accuracy and stability during operations of the human-machine interface of the sports training system.

KSC-04PD-1985

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. Principal Albert Sye, astronaut Leland Melvin, Dr. Julian Earls and KSC Deputy Director Dr. Woodrow Whitlow Jr. share the stage at Ronald E. McNair High School in Atlanta, a NASA Explorer School. Dr. Earls is director of the NASA Glenn Research Center. He joined Dr. Whitlow on a visit to the school to share the vision for space exploration with the next generation of explorers. Whitlow talked with students about our destiny as explorers, NASAs stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space. Dr. Earls discussed the future and the vision for space, plus the NASA careers needed to meet the vision. Melvin talked about the importance of teamwork and what it takes for mission success.

KSC-04PD-1990

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. KSC Deputy Director Dr. Woodrow Whitlow Jr. talks to students at Ronald E. McNair High School in Atlanta, a NASA Explorer School. He is visiting to the school to share the vision for space exploration with the next generation of explorers. Astronaut Leland Melvin(second from right) accompanied Whitlow, talking with students about the importance of teamwork and what it takes for mission success. Also on the visit was Dr. Julian Earls (far right), director of NASA Glenn Research Center, who discussed the future and the vision for space, plus the NASA careers needed to meet the vision. Whitlow talked with students about our destiny as explorers, NASAs stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

Estimating atmospheric parameters and reducing noise for multispectral imaging

DOEpatents

Conger, James Lynn

2014-02-25

A method and system for estimating atmospheric radiance and transmittance. An atmospheric estimation system is divided into a first phase and a second phase. The first phase inputs an observed multispectral image and an initial estimate of the atmospheric radiance and transmittance for each spectral band and calculates the atmospheric radiance and transmittance for each spectral band, which can be used to generate a "corrected" multispectral image that is an estimate of the surface multispectral image. The second phase inputs the observed multispectral image and the surface multispectral image that was generated by the first phase and removes noise from the surface multispectral image by smoothing out change in average deviations of temperatures.

Gesture-Controlled Interfaces for Self-Service Machines

NASA Technical Reports Server (NTRS)

Cohen, Charles J.; Beach, Glenn

2006-01-01

Gesture-controlled interfaces are software- driven systems that facilitate device control by translating visual hand and body signals into commands. Such interfaces could be especially attractive for controlling self-service machines (SSMs) for example, public information kiosks, ticket dispensers, gasoline pumps, and automated teller machines (see figure). A gesture-controlled interface would include a vision subsystem comprising one or more charge-coupled-device video cameras (at least two would be needed to acquire three-dimensional images of gestures). The output of the vision system would be processed by a pure software gesture-recognition subsystem. Then a translator subsystem would convert a sequence of recognized gestures into commands for the SSM to be controlled; these could include, for example, a command to display requested information, change control settings, or actuate a ticket- or cash-dispensing mechanism. Depending on the design and operational requirements of the SSM to be controlled, the gesture-controlled interface could be designed to respond to specific static gestures, dynamic gestures, or both. Static and dynamic gestures can include stationary or moving hand signals, arm poses or motions, and/or whole-body postures or motions. Static gestures would be recognized on the basis of their shapes; dynamic gestures would be recognized on the basis of both their shapes and their motions. Because dynamic gestures include temporal as well as spatial content, this gesture- controlled interface can extract more information from dynamic than it can from static gestures.

Scalable Nearest Neighbor Algorithms for High Dimensional Data.

PubMed

Muja, Marius; Lowe, David G

2014-11-01

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.

Proposal for continued research in intelligent machines at the Center for Engineering Systems Advanced Research (CESAR) for FY 1988 to FY 1991

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

Weisbin, C.R.

1987-03-01

This document reviews research accomplishments achieved by the staff of the Center for Engineering Systems Advanced Research (CESAR) during the fiscal years 1984 through 1987. The manuscript also describes future CESAR objectives for the 1988-1991 planning horizon, and beyond. As much as possible, the basic research goals are derived from perceived Department of Energy (DOE) needs for increased safety, productivity, and competitiveness in the United States energy producing and consuming facilities. Research areas covered include the HERMIES-II Robot, autonomous robot navigation, hypercube computers, machine vision, and manipulators.

[On machines and instruments (II): the world in the eye of the work of E. T. A. Hoffmann].

PubMed

Montiel, L

2008-01-01

Continuing with the subject of the previous work, this article considers the whole series of problems connected to the question of vision provoked by the mere existence of the body of the automaton. The eyes of the android and, above all, the reactions aroused by looking at these human-shaped machines are the object of Hoffman's reflections, from a viewpoint apparently firmly set within the Goethean concept of the - Welt des Auges. Similarly, there is as it had been perceived in science since the seventeenth century.

Nanohole-array-based device for 2D snapshot multispectral imaging

PubMed Central

Najiminaini, Mohamadreza; Vasefi, Fartash; Kaminska, Bozena; Carson, Jeffrey J. L.

2013-01-01

We present a two-dimensional (2D) snapshot multispectral imager that utilizes the optical transmission characteristics of nanohole arrays (NHAs) in a gold film to resolve a mixture of input colors into multiple spectral bands. The multispectral device consists of blocks of NHAs, wherein each NHA has a unique periodicity that results in transmission resonances and minima in the visible and near-infrared regions. The multispectral device was illuminated over a wide spectral range, and the transmission was spectrally unmixed using a least-squares estimation algorithm. A NHA-based multispectral imaging system was built and tested in both reflection and transmission modes. The NHA-based multispectral imager was capable of extracting 2D multispectral images representative of four independent bands within the spectral range of 662 nm to 832 nm for a variety of targets. The multispectral device can potentially be integrated into a variety of imaging sensor systems. PMID:24005065

Large Scale Structure From Motion for Autonomous Underwater Vehicle Surveys

DTIC Science & Technology

2004-09-01

Govern the Formation of Multiple Images of a Scene and Some of Their Applications. MIT Press, 2001. [26] 0. Faugeras and S. Maybank . Motion from point...Machine Vision Conference, volume 1, pages 384-393, September 2002. [69] S. Maybank and 0. Faugeras. A theory of self-calibration of a moving camera

The Potential of Artificial Intelligence in Aids for the Disabled.

ERIC Educational Resources Information Center

Boyer, John J.

The paper explores the possibilities for applying the knowledge of artificial intelligence (AI) research to aids for the disabled. Following a definition of artificial intelligence, the paper reviews areas of basic AI research, such as computer vision, machine learning, and planning and problem solving. Among application areas relevant to the…

Feasibility of Robotics and Machine Vision in Military Combat Ration Inspection (Short Term Project STP No. 11)

DTIC Science & Technology

1994-06-01

signals. Industrial robot controllers have several general purpose ports which can be programmed within manipulator program. In this way the gen ri...well as a fanc - tional end- effector was developed and evaluated. The workcell was found technologically feasible; however, further experimental work

Human-Machine Cooperation in Large-Scale Multimedia Retrieval: A Survey

ERIC Educational Resources Information Center

Shirahama, Kimiaki; Grzegorzek, Marcin; Indurkhya, Bipin

2015-01-01

"Large-Scale Multimedia Retrieval" (LSMR) is the task to fast analyze a large amount of multimedia data like images or videos and accurately find the ones relevant to a certain semantic meaning. Although LSMR has been investigated for more than two decades in the fields of multimedia processing and computer vision, a more…

Peter Koch: wizard of wood use

Treesearch

M.E. Lora

1978-01-01

Like his pioneer forefathers, Peter Koch sees opportunity where others see obstacles. And his vision is helping to reshape the wood industry. Since 1963 Koch has directed research on processing southern woods for the U.S. Forest Service's Southern Forest Experiment Station in Pineville, Louisiana. In that time, he has invented six revolutionary machines, developed...

Why the Future Doesn't Come from Machines: Unfounded Prophecies and the Design of Naturoids

ERIC Educational Resources Information Center

Negrotti, Massimo

2008-01-01

Technological imagination and actual technological achievements have always been two very different things. Sudden and unpredictable events always seem to intervene between our visions regarding possible futures and the subsequent concrete realizations. Thus, our ideas and projects are continually being redirected. In the field of…

A dataset of stereoscopic images and ground-truth disparity mimicking human fixations in peripersonal space

PubMed Central

Canessa, Andrea; Gibaldi, Agostino; Chessa, Manuela; Fato, Marco; Solari, Fabio; Sabatini, Silvio P.

2017-01-01

Binocular stereopsis is the ability of a visual system, belonging to a live being or a machine, to interpret the different visual information deriving from two eyes/cameras for depth perception. From this perspective, the ground-truth information about three-dimensional visual space, which is hardly available, is an ideal tool both for evaluating human performance and for benchmarking machine vision algorithms. In the present work, we implemented a rendering methodology in which the camera pose mimics realistic eye pose for a fixating observer, thus including convergent eye geometry and cyclotorsion. The virtual environment we developed relies on highly accurate 3D virtual models, and its full controllability allows us to obtain the stereoscopic pairs together with the ground-truth depth and camera pose information. We thus created a stereoscopic dataset: GENUA PESTO—GENoa hUman Active fixation database: PEripersonal space STereoscopic images and grOund truth disparity. The dataset aims to provide a unified framework useful for a number of problems relevant to human and computer vision, from scene exploration and eye movement studies to 3D scene reconstruction. PMID:28350382

Coupling sensing to crop models for closed-loop plant production in advanced life support systems

NASA Astrophysics Data System (ADS)

Cavazzoni, James; Ling, Peter P.

1999-01-01

We present a conceptual framework for coupling sensing to crop models for closed-loop analysis of plant production for NASA's program in advanced life support. Crop status may be monitored through non-destructive observations, while models may be independently applied to crop production planning and decision support. To achieve coupling, environmental variables and observations are linked to mode inputs and outputs, and monitoring results compared with model predictions of plant growth and development. The information thus provided may be useful in diagnosing problems with the plant growth system, or as a feedback to the model for evaluation of plant scheduling and potential yield. In this paper, we demonstrate this coupling using machine vision sensing of canopy height and top projected canopy area, and the CROPGRO crop growth model. Model simulations and scenarios are used for illustration. We also compare model predictions of the machine vision variables with data from soybean experiments conducted at New Jersey Agriculture Experiment Station Horticulture Greenhouse Facility, Rutgers University. Model simulations produce reasonable agreement with the available data, supporting our illustration.

Proteus: a reconfigurable computational network for computer vision

NASA Astrophysics Data System (ADS)

Haralick, Robert M.; Somani, Arun K.; Wittenbrink, Craig M.; Johnson, Robert; Cooper, Kenneth; Shapiro, Linda G.; Phillips, Ihsin T.; Hwang, Jenq N.; Cheung, William; Yao, Yung H.; Chen, Chung-Ho; Yang, Larry; Daugherty, Brian; Lorbeski, Bob; Loving, Kent; Miller, Tom; Parkins, Larye; Soos, Steven L.

1992-04-01

The Proteus architecture is a highly parallel MIMD, multiple instruction, multiple-data machine, optimized for large granularity tasks such as machine vision and image processing The system can achieve 20 Giga-flops (80 Giga-flops peak). It accepts data via multiple serial links at a rate of up to 640 megabytes/second. The system employs a hierarchical reconfigurable interconnection network with the highest level being a circuit switched Enhanced Hypercube serial interconnection network for internal data transfers. The system is designed to use 256 to 1,024 RISC processors. The processors use one megabyte external Read/Write Allocating Caches for reduced multiprocessor contention. The system detects, locates, and replaces faulty subsystems using redundant hardware to facilitate fault tolerance. The parallelism is directly controllable through an advanced software system for partitioning, scheduling, and development. System software includes a translator for the INSIGHT language, a parallel debugger, low and high level simulators, and a message passing system for all control needs. Image processing application software includes a variety of point operators neighborhood, operators, convolution, and the mathematical morphology operations of binary and gray scale dilation, erosion, opening, and closing.

Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

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

Jha, Sumit Kumar; Pullum, Laura L; Ramanathan, Arvind

Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studyingmore » the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.« less

Robust crop and weed segmentation under uncontrolled outdoor illumination.

PubMed

Jeon, Hong Y; Tian, Lei F; Zhu, Heping

2011-01-01

An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA).

Using image analysis to predict the weight of Alaskan salmon of different species.

PubMed

Balaban, Murat O; Unal Sengör, Gülgün F; Gil Soriano, Mario; Guillén Ruiz, Elena

2010-04-01

After harvesting, salmon is sorted by species, size, and quality. This is generally manually done by operators. Automation would bring repeatability, objectivity, and record-keeping capabilities to these tasks. Machine vision (MV) and image analysis have been used in sorting many agricultural products. Four salmon species were tested: pink (Oncorhynchus gorbuscha), red (Oncorhynchus nerka), silver (Oncorhynchus kisutch), and chum (Oncorhynchus keta). A total of 60 whole fish from each species were first weighed, then placed in a light box to take their picture. Weight compared with view area as well as length and width correlations were developed. In addition the effect of "hump" development (see text) of pink salmon on this correlation was investigated. It was possible to predict the weight of a salmon by view area, regardless of species, and regardless of the development of a hump for pinks. Within pink salmon there was a small but insignificant difference between predictive equations for the weight of "regular" fish and "humpy" fish. Machine vision can accurately predict the weight of whole salmon for sorting.

Combined measurement system for double shield tunnel boring machine guidance based on optical and visual methods.

PubMed

Lin, Jiarui; Gao, Kai; Gao, Yang; Wang, Zheng

2017-10-01

In order to detect the position of the cutting shield at the head of a double shield tunnel boring machine (TBM) during the excavation, this paper develops a combined measurement system which is mainly composed of several optical feature points, a monocular vision sensor, a laser target sensor, and a total station. The different elements of the combined system are mounted on the TBM in suitable sequence, and the position of the cutting shield in the reference total station frame is determined by coordinate transformations. Subsequently, the structure of the feature points and matching technique for them are expounded, the position measurement method based on monocular vision is presented, and the calibration methods for the unknown relationships among different parts of the system are proposed. Finally, a set of experimental platforms to simulate the double shield TBM is established, and accuracy verification experiments are conducted. Experimental results show that the mean deviation of the system is 6.8 mm, which satisfies the requirements of double shield TBM guidance.

KENNEDY SPACE CENTER, FLA. - Astronaut Kay Hire poses with 8th grader Kristy Wiggins at Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla. Hire joined Center Director Jim Kennedy at the school in sharing the agency’s new vision for space exploration with the next generation of explorers. Kennedy is talking with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

NASA Image and Video Library

2004-02-20

KENNEDY SPACE CENTER, FLA. - Astronaut Kay Hire poses with 8th grader Kristy Wiggins at Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla. Hire joined Center Director Jim Kennedy at the school in sharing the agency’s new vision for space exploration with the next generation of explorers. Kennedy is talking with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

KENNEDY SPACE CENTER, FLA. - Warren Elly (left), with WTVT-TV, Fox News, talks with Center Director Jim Kennedy at Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla. Kennedy was joined by astronaut Kay Hire in sharing the agency’s new vision for space exploration with the next generation of explorers. Kennedy talked with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

NASA Image and Video Library

2004-02-20

KENNEDY SPACE CENTER, FLA. - Warren Elly (left), with WTVT-TV, Fox News, talks with Center Director Jim Kennedy at Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla. Kennedy was joined by astronaut Kay Hire in sharing the agency’s new vision for space exploration with the next generation of explorers. Kennedy talked with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

KENNEDY SPACE CENTER, FLA. - Center Director Jim Kennedy talks to WTSP-ABC News about his trip to Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla. Kennedy made the trip with NASA astronaut Kay Hire to share the agency’s new vision for space exploration with the next generation of explorers. Kennedy is talking with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

NASA Image and Video Library

2004-02-20

KENNEDY SPACE CENTER, FLA. - Center Director Jim Kennedy talks to WTSP-ABC News about his trip to Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla. Kennedy made the trip with NASA astronaut Kay Hire to share the agency’s new vision for space exploration with the next generation of explorers. Kennedy is talking with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

Home Camera-Based Fall Detection System for the Elderly.

PubMed

de Miguel, Koldo; Brunete, Alberto; Hernando, Miguel; Gambao, Ernesto

2017-12-09

Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.

Home Camera-Based Fall Detection System for the Elderly

PubMed Central

de Miguel, Koldo

2017-01-01

Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%. PMID:29232846

Multispectral radiation envelope characteristics of aerial infrared targets

NASA Astrophysics Data System (ADS)

Kou, Tian; Zhou, Zhongliang; Liu, Hongqiang; Yang, Yuanzhi; Lu, Chunguang

2018-07-01

Multispectral detection signals are relatively stable and complementary to single spectral detection signals with deficiencies of severe scintillation and poor anti-interference. To take advantage of multispectral radiation characteristics in the application of infrared target detection, the concept of a multispectral radiation envelope is proposed. To build the multispectral radiation envelope model, the temperature distribution of an aerial infrared target is calculated first. By considering the coupling heat transfer process, the heat balance equation is built by using the node network, and the convective heat transfer laws as a function of target speed are uncovered. Then, the tail flame temperature distribution model is built and the temperature distributions at different horizontal distances are calculated. Second, to obtain the optimal detection angles, envelope models of reflected background multispectral radiation and target multispectral radiation are built. Finally, the envelope characteristics of the aerial target multispectral radiation are analyzed in different wavebands in detail. The results we obtained reflect Wien's displacement law and prove the effectiveness and reasonableness of the envelope model, and also indicate that the major difference between multispectral wavebands is greatly influenced by the target speed. Moreover, optimal detection angles are obtained by numerical simulation, and these are very important for accurate and fast target detection, attack decision-making and developing multispectral detection platforms.

Detection of Alzheimer's Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging.

PubMed

Wang, Shuihua; Zhang, Yudong; Liu, Ge; Phillips, Preetha; Yuan, Ti-Fei

2016-01-01

Within the past decade, computer scientists have developed many methods using computer vision and machine learning techniques to detect Alzheimer's disease (AD) in its early stages. However, some of these methods are unable to achieve excellent detection accuracy, and several other methods are unable to locate AD-related regions. Hence, our goal was to develop a novel AD brain detection method. In this study, our method was based on the three-dimensional (3D) displacement-field (DF) estimation between subjects in the healthy elder control group and AD group. The 3D-DF was treated with AD-related features. The three feature selection measures were used in the Bhattacharyya distance, Student's t-test, and Welch's t-test (WTT). Two non-parallel support vector machines, i.e., generalized eigenvalue proximal support vector machine and twin support vector machine (TSVM), were then used for classification. A 50 × 10-fold cross validation was implemented for statistical analysis. The results showed that "3D-DF+WTT+TSVM" achieved the best performance, with an accuracy of 93.05 ± 2.18, a sensitivity of 92.57 ± 3.80, a specificity of 93.18 ± 3.35, and a precision of 79.51 ± 2.86. This method also exceled in 13 state-of-the-art approaches. Additionally, we were able to detect 17 regions related to AD by using the pure computer-vision technique. These regions include sub-gyral, inferior parietal lobule, precuneus, angular gyrus, lingual gyrus, supramarginal gyrus, postcentral gyrus, third ventricle, superior parietal lobule, thalamus, middle temporal gyrus, precentral gyrus, superior temporal gyrus, superior occipital gyrus, cingulate gyrus, culmen, and insula. These regions were reported in recent publications. The 3D-DF is effective in AD subject and related region detection.

Research interface on a programmable ultrasound scanner.

PubMed

Shamdasani, Vijay; Bae, Unmin; Sikdar, Siddhartha; Yoo, Yang Mo; Karadayi, Kerem; Managuli, Ravi; Kim, Yongmin

2008-07-01

Commercial ultrasound machines in the past did not provide the ultrasound researchers access to raw ultrasound data. Lack of this ability has impeded evaluation and clinical testing of novel ultrasound algorithms and applications. Recently, we developed a flexible ultrasound back-end where all the processing for the conventional ultrasound modes, such as B, M, color flow and spectral Doppler, was performed in software. The back-end has been incorporated into a commercial ultrasound machine, the Hitachi HiVision 5500. The goal of this work is to develop an ultrasound research interface on the back-end for acquiring raw ultrasound data from the machine. The research interface has been designed as a software module on the ultrasound back-end. To increase the amount of raw ultrasound data that can be spooled in the limited memory available on the back-end, we have developed a method that can losslessly compress the ultrasound data in real time. The raw ultrasound data could be obtained in any conventional ultrasound mode, including duplex and triplex modes. Furthermore, use of the research interface does not decrease the frame rate or otherwise affect the clinical usability of the machine. The lossless compression of the ultrasound data in real time can increase the amount of data spooled by approximately 2.3 times, thus allowing more than 6s of raw ultrasound data to be acquired in all the modes. The interface has been used not only for early testing of new ideas with in vitro data from phantoms, but also for acquiring in vivo data for fine-tuning ultrasound applications and conducting clinical studies. We present several examples of how newer ultrasound applications, such as elastography, vibration imaging and 3D imaging, have benefited from this research interface. Since the research interface is entirely implemented in software, it can be deployed on existing HiVision 5500 ultrasound machines and may be easily upgraded in the future. The developed research interface can aid researchers in the rapid testing and clinical evaluation of new ultrasound algorithms and applications. Additionally, we believe that our approach would be applicable to designing research interfaces on other ultrasound machines.

An integrated compact airborne multispectral imaging system using embedded computer

NASA Astrophysics Data System (ADS)

Zhang, Yuedong; Wang, Li; Zhang, Xuguo

2015-08-01

An integrated compact airborne multispectral imaging system using embedded computer based control system was developed for small aircraft multispectral imaging application. The multispectral imaging system integrates CMOS camera, filter wheel with eight filters, two-axis stabilized platform, miniature POS (position and orientation system) and embedded computer. The embedded computer has excellent universality and expansibility, and has advantages in volume and weight for airborne platform, so it can meet the requirements of control system of the integrated airborne multispectral imaging system. The embedded computer controls the camera parameters setting, filter wheel and stabilized platform working, image and POS data acquisition, and stores the image and data. The airborne multispectral imaging system can connect peripheral device use the ports of the embedded computer, so the system operation and the stored image data management are easy. This airborne multispectral imaging system has advantages of small volume, multi-function, and good expansibility. The imaging experiment results show that this system has potential for multispectral remote sensing in applications such as resource investigation and environmental monitoring.

Data acquisition and analysis of range-finding systems for spacing construction

NASA Technical Reports Server (NTRS)

Shen, C. N.

1981-01-01

For space missions of future, completely autonomous robotic machines will be required to free astronauts from routine chores of equipment maintenance, servicing of faulty systems, etc. and to extend human capabilities in hazardous environments full of cosmic and other harmful radiations. In places of high radiation and uncontrollable ambient illuminations, T.V. camera based vision systems cannot work effectively. However, a vision system utilizing directly measured range information with a time of flight laser rangefinder, can successfully operate under these environments. Such a system will be independent of proper illumination conditions and the interfering effects of intense radiation of all kinds will be eliminated by the tuned input of the laser instrument. Processing the range data according to certain decision, stochastic estimation and heuristic schemes, the laser based vision system will recognize known objects and thus provide sufficient information to the robot's control system which can develop strategies for various objectives.

Identification and location of catenary insulator in complex background based on machine vision

NASA Astrophysics Data System (ADS)

Yao, Xiaotong; Pan, Yingli; Liu, Li; Cheng, Xiao

2018-04-01

It is an important premise to locate insulator precisely for fault detection. Current location algorithms for insulator under catenary checking images are not accurate, a target recognition and localization method based on binocular vision combined with SURF features is proposed. First of all, because of the location of the insulator in complex environment, using SURF features to achieve the coarse positioning of target recognition; then Using binocular vision principle to calculate the 3D coordinates of the object which has been coarsely located, realization of target object recognition and fine location; Finally, Finally, the key is to preserve the 3D coordinate of the object's center of mass, transfer to the inspection robot to control the detection position of the robot. Experimental results demonstrate that the proposed method has better recognition efficiency and accuracy, can successfully identify the target and has a define application value.

KSC-04PD-1993

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. Julian Earls (left), a school board member of Ronald E. McNair High School in Atlanta, and Sarah Copelin-Wood (far left), chair of the Board of Education, sign a Memorandum of Understanding after a presentation by KSC Deputy Director Dr. Woodrow Whitlow Jr., astronaut Leland Melvin and Dr. Julian Earls, director of NASA Glenn Research Center. McNair is a NASA Explorer School (NES). Whitlow visited the school to share the vision for space exploration with the next generation of explorers. He talked with students about our destiny as explorers, NASAs stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space. Dr. Earls discussed the future and the vision for space, plus the NASA careers needed to meet the vision. Melvin talked about the importance of teamwork and what it takes for mission success.

Algorithms and architectures for robot vision

NASA Technical Reports Server (NTRS)

Schenker, Paul S.

1990-01-01

The scope of the current work is to develop practical sensing implementations for robots operating in complex, partially unstructured environments. A focus in this work is to develop object models and estimation techniques which are specific to requirements of robot locomotion, approach and avoidance, and grasp and manipulation. Such problems have to date received limited attention in either computer or human vision - in essence, asking not only how perception is in general modeled, but also what is the functional purpose of its underlying representations. As in the past, researchers are drawing on ideas from both the psychological and machine vision literature. Of particular interest is the development 3-D shape and motion estimates for complex objects when given only partial and uncertain information and when such information is incrementally accrued over time. Current studies consider the use of surface motion, contour, and texture information, with the longer range goal of developing a fused sensing strategy based on these sources and others.

Adaptive multisensor fusion for planetary exploration rovers

NASA Technical Reports Server (NTRS)

Collin, Marie-France; Kumar, Krishen; Pampagnin, Luc-Henri

1992-01-01

The purpose of the adaptive multisensor fusion system currently being designed at NASA/Johnson Space Center is to provide a robotic rover with assured vision and safe navigation capabilities during robotic missions on planetary surfaces. Our approach consists of using multispectral sensing devices ranging from visible to microwave wavelengths to fulfill the needs of perception for space robotics. Based on the illumination conditions and the sensors capabilities knowledge, the designed perception system should automatically select the best subset of sensors and their sensing modalities that will allow the perception and interpretation of the environment. Then, based on reflectance and emittance theoretical models, the sensor data are fused to extract the physical and geometrical surface properties of the environment surface slope, dielectric constant, temperature and roughness. The theoretical concepts, the design and first results of the multisensor perception system are presented.

Coniferous forest classification and inventory using Landsat and digital terrain data

NASA Technical Reports Server (NTRS)

Franklin, J.; Logan, T. L.; Woodcock, C. E.; Strahler, A. H.

1986-01-01

Machine-processing techniques were used in a Forest Classification and Inventory System (FOCIS) procedure to extract and process tonal, textural, and terrain information from registered Landsat multispectral and digital terrain data. Using FOCIS as a basis for stratified sampling, the softwood timber volumes of the Klamath National Forest and Eldorado National Forest were estimated within standard errors of 4.8 and 4.0 percent, respectively. The accuracy of these large-area inventories is comparable to the accuracy yielded by use of conventional timber inventory methods, but, because of automation, the FOCIS inventories are more rapid (9-12 months compared to 2-3 years for conventional manual photointerpretation, map compilation and drafting, field sampling, and data processing) and are less costly.

a New Object-Based Framework to Detect Shodows in High-Resolution Satellite Imagery Over Urban Areas

NASA Astrophysics Data System (ADS)

Tatar, N.; Saadatseresht, M.; Arefi, H.; Hadavand, A.

2015-12-01

In this paper a new object-based framework to detect shadow areas in high resolution satellite images is proposed. To produce shadow map in pixel level state of the art supervised machine learning algorithms are employed. Automatic ground truth generation based on Otsu thresholding on shadow and non-shadow indices is used to train the classifiers. It is followed by segmenting the image scene and create image objects. To detect shadow objects, a majority voting on pixel-based shadow detection result is designed. GeoEye-1 multi-spectral image over an urban area in Qom city of Iran is used in the experiments. Results shows the superiority of our proposed method over traditional pixel-based, visually and quantitatively.

Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, Ind., June 29-July 1, 1976, Proceedings

NASA Technical Reports Server (NTRS)

1976-01-01

Papers are presented on the applicability of Landsat data to water management and control needs, IBIS, a geographic information system based on digital image processing and image raster datatype, and the Image Data Access Method (IDAM) for the Earth Resources Interactive Processing System. Attention is also given to the Prototype Classification and Mensuration System (PROCAMS) applied to agricultural data, the use of Landsat for water quality monitoring in North Carolina, and the analysis of geophysical remote sensing data using multivariate pattern recognition. The Illinois crop-acreage estimation experiment, the Pacific Northwest Resources Inventory Demonstration, and the effects of spatial misregistration on multispectral recognition are also considered. Individual items are announced in this issue.

Automated hardwood lumber grading utilizing a multiple sensor machine vision technology

Treesearch

D. Earl Kline; Chris Surak; Philip A. Araman

2003-01-01

Over the last 10 years, scientists at the Thomas M. Brooks Forest Products Center, the Bradley Department of Electrical and Computer Engineering, and the USDA Forest Service have been working on lumber scanning systems that can accurately locate and identify defects in hardwood lumber. Current R&D efforts are targeted toward developing automated lumber grading...

Inside the Global Teaching Machine: MOOCs, Academic Labour and the Future of the University

ERIC Educational Resources Information Center

Peters, Michael A.

2016-01-01

This special issue focused on "Digital Media and Contested Visions of Education" provides an opportunity to examine the tendency to hypothesise a rupture in the history of the university. It does so by contrasting the traditional Humboldtian ideals of the university with a neoliberal marketised version and in order to ask questions…

The Future System for Roughmill Optimization

Treesearch

Richard W. Conners; D.Earl Kline; Philip A. Araman; Thomas T. Drayer

1997-01-01

From forest to finished product, wood is moved from one processing stage to the next, subject to the decisions of individuals along the way. While this process has worked for hundreds of years, the technology exists today to provide more complete information to the decision makers. Virginia Tech has developed this technology, creating a machine vision prototype for...

A Talking Computers System for Persons with Vision and Speech Handicaps. Final Report.

ERIC Educational Resources Information Center

Visek & Maggs, Urbana, IL.

This final report contains a detailed description of six software systems designed to assist individuals with blindness and/or speech disorders in using inexpensive, off-the-shelf computers rather than expensive custom-made devices. The developed software is not written in the native machine language of any particular brand of computer, but in the…

Methods for Identifying Object Class, Type, and Orientation in the Presence of Uncertainty

DTIC Science & Technology

1990-08-01

on Range Finding Techniques for Computer Vision," IEEE Trans. on Pattern Analysis and Machine Intellegence PAMI-5 (2), pp 129-139 March 1983. 15. Yang... Artificial Intelligence Applications, pp 199-205, December 1984. 16. Flynn, P.J. and Jain, A.K.," On Reliable Curvature Estimation, " Proceedings of the

Adaptive Machine Vision.

DTIC Science & Technology

1992-06-18

developed by Fukushima . The system has potential use for SDI target/decoy discrimination. For testing purposes, simulated angle-angle and range-Doppler...properties and computational requirements of the Neocognitron, a patern recognition neural network developed by Fukushima . The RADONN effort builds upon...and Information Processing, 17-21 June 1991, Plymouth State College, Plymouth, New Hampshire.) 5.0 References 1. Kunihiko Fukushima , Sei Miyake, and

Machine Vision System for Color Sorting Wood Edge-Glued Panel Parts

Treesearch

Qiang Lu; S. Srikanteswara; W. King; T. Drayer; Richard Conners; D. Earl Kline; Philip A. Araman

1997-01-01

This paper describes an automatic color sorting system for hardwood edge-glued panel parts. The color sorting system simultaneously examines both faces of a panel part and then determines which face has the "better" color given specified color uniformity and priority defined by management. The real-time color sorting system software and hardware are briefly...

Gas flow parameters in laser cutting of wood- nozzle design

Treesearch

Kali Mukherjee; Tom Grendzwell; Parwaiz A.A. Khan; Charles McMillin

1990-01-01

The Automated Lumber Processing System (ALPS) is an ongoing team research effort to optimize the yield of parts in a furniture rough mill. The process is designed to couple aspects of computer vision, computer optimization of yield, and laser cutting. This research is focused on optimizing laser wood cutting. Laser machining of lumber has the advantage over...

Lighting Studies for Fuelling Machine Deployed Visual Inspection Tool

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

Stoots, Carl; Griffith, George

2015-04-01

Under subcontract to James Fisher Nuclear, Ltd., INL has been reviewing advanced vision systems for inspection of graphite in high radiation, high temperature, and high pressure environments. INL has performed calculations and proof-of-principle measurements of optics and lighting techniques to be considered for visual inspection of graphite fuel channels in AGR reactors in UK.

A machine vision system for high speed sorting of small spots on grains

USDA-ARS?s Scientific Manuscript database

A sorting system was developed to detect and remove individual grain kernels with small localized blemishes or defects. The system uses a color VGA sensor to capture images of the kernels at high speed as the grain drops off an inclined chute. The image data are directly input into a field-programma...

Next-Generation Terrestrial Laser Scanning to Measure Forest Canopy Structure

NASA Astrophysics Data System (ADS)

Danson, M.

2015-12-01

Terrestrial laser scanners (TLS) are now capable of semi-automatic reconstruction of the structure of complete trees or forest stands and have the potential to provide detailed information on tree architecture and foliage biophysical properties. The trends for the next generation of TLS are towards higher resolution, faster scanning and full-waveform data recording, with mobile, multispectral laser devices. The convergence of these technological advances in the next generation of TLS will allow the production of information for forest and woodland mapping and monitoring that is far more detailed, more accurate, and more comprehensive than any available today. This paper describes recent scientific advances in the application of TLS for characterising forest and woodland areas, drawing on the authors' development of the Salford Advanced Laser Canopy Analyser (SALCA), the activities of the Terrestrial Laser Scanner International Interest Group (TLSIIG), and recent advances in laser scanner technology around the world. The key findings illustrated in the paper are that (i) a complete understanding of system measurement characteristics is required for quantitative analysis of TLS data, (ii) full-waveform data recording is required for extraction of forest biophysical variables and, (iii) multi-wavelength systems provide additional spectral information that is essential for classifying different vegetation components. The paper uses a range of recent experimental TLS measurements to support these findings, and sets out a vision for new research to develop an information-rich future-forest information system, populated by mobile autonomous multispectral TLS devices.

Fundamentals and advances in the development of remote welding fabrication systems

NASA Technical Reports Server (NTRS)

Agapakis, J. E.; Masubuchi, K.; Von Alt, C.

1986-01-01

Operational and man-machine issues for welding underwater, in outer space, and at other remote sites are investigated, and recent process developments are described. Probable remote welding missions are classified, and the essential characteristics of fundamental remote welding tasks are analyzed. Various possible operational modes for remote welding fabrication are identified, and appropriate roles for humans and machines are suggested. Human operator performance in remote welding fabrication tasks is discussed, and recent advances in the development of remote welding systems are described, including packaged welding systems, stud welding systems, remotely operated welding systems, and vision-aided remote robotic welding and autonomous welding systems.

Machine intelligence and autonomy for aerospace systems

NASA Technical Reports Server (NTRS)

Heer, Ewald (Editor); Lum, Henry (Editor)

1988-01-01

The present volume discusses progress toward intelligent robot systems in aerospace applications, NASA Space Program automation and robotics efforts, the supervisory control of telerobotics in space, machine intelligence and crew/vehicle interfaces, expert-system terms and building tools, and knowledge-acquisition for autonomous systems. Also discussed are methods for validation of knowledge-based systems, a design methodology for knowledge-based management systems, knowledge-based simulation for aerospace systems, knowledge-based diagnosis, planning and scheduling methods in AI, the treatment of uncertainty in AI, vision-sensing techniques in aerospace applications, image-understanding techniques, tactile sensing for robots, distributed sensor integration, and the control of articulated and deformable space structures.

Automation of the CCTV-mediated detection of individuals illegally carrying firearms: combining psychological and technological approaches

NASA Astrophysics Data System (ADS)

Darker, Iain T.; Kuo, Paul; Yang, Ming Yuan; Blechko, Anastassia; Grecos, Christos; Makris, Dimitrios; Nebel, Jean-Christophe; Gale, Alastair G.

2009-05-01

Findings from the current UK national research programme, MEDUSA (Multi Environment Deployable Universal Software Application), are presented. MEDUSA brings together two approaches to facilitate the design of an automatic, CCTV-based firearm detection system: psychological-to elicit strategies used by CCTV operators; and machine vision-to identify key cues derived from camera imagery. Potentially effective human- and machine-based strategies have been identified; these will form elements of the final system. The efficacies of these algorithms have been tested on staged CCTV footage in discriminating between firearms and matched distractor objects. Early results indicate the potential for this combined approach.

The role of robotics in computer controlled polishing of large and small optics

NASA Astrophysics Data System (ADS)

Walker, David; Dunn, Christina; Yu, Guoyu; Bibby, Matt; Zheng, Xiao; Wu, Hsing Yu; Li, Hongyu; Lu, Chunlian

2015-08-01

Following formal acceptance by ESO of three 1.4m hexagonal off-axis prototype mirror segments, one circular segment, and certification of our optical test facility, we turn our attention to the challenge of segment mass-production. In this paper, we focus on the role of industrial robots, highlighting complementarity with Zeeko CNC polishing machines, and presenting results using robots to provide intermediate processing between CNC grinding and polishing. We also describe the marriage of robots and Zeeko machines to automate currently manual operations; steps towards our ultimate vision of fully autonomous manufacturing cells, with impact throughout the optical manufacturing community and beyond.

Electronic Nose and Electronic Tongue

NASA Astrophysics Data System (ADS)

Bhattacharyya, Nabarun; Bandhopadhyay, Rajib

Human beings have five senses, namely, vision, hearing, touch, smell and taste. The sensors for vision, hearing and touch have been developed for several years. The need for sensors capable of mimicking the senses of smell and taste have been felt only recently in food industry, environmental monitoring and several industrial applications. In the ever-widening horizon of frontier research in the field of electronics and advanced computing, emergence of electronic nose (E-Nose) and electronic tongue (E-Tongue) have been drawing attention of scientists and technologists for more than a decade. By intelligent integration of multitudes of technologies like chemometrics, microelectronics and advanced soft computing, human olfaction has been successfully mimicked by such new techniques called machine olfaction (Pearce et al. 2002). But the very essence of such research and development efforts has centered on development of customized electronic nose and electronic tongue solutions specific to individual applications. In fact, research trends as of date clearly points to the fact that a machine olfaction system as versatile, universal and broadband as human nose and human tongue may not be feasible in the decades to come. But application specific solutions may definitely be demonstrated and commercialized by modulation in sensor design and fine-tuning the soft computing solutions. This chapter deals with theory, developments of E-Nose and E-Tongue technology and their applications. Also a succinct account of future trends of R&D efforts in this field with an objective of establishing co-relation between machine olfaction and human perception has been included.

Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.

PubMed

Rey-Villamizar, Nicolas; Somasundar, Vinay; Megjhani, Murad; Xu, Yan; Lu, Yanbin; Padmanabhan, Raghav; Trett, Kristen; Shain, William; Roysam, Badri

2014-01-01

In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

The research on visual industrial robot which adopts fuzzy PID control algorithm

NASA Astrophysics Data System (ADS)

Feng, Yifei; Lu, Guoping; Yue, Lulin; Jiang, Weifeng; Zhang, Ye

2017-03-01

The control system of six degrees of freedom visual industrial robot based on the control mode of multi-axis motion control cards and PC was researched. For the variable, non-linear characteristics of industrial robot`s servo system, adaptive fuzzy PID controller was adopted. It achieved better control effort. In the vision system, a CCD camera was used to acquire signals and send them to video processing card. After processing, PC controls the six joints` motion by motion control cards. By experiment, manipulator can operate with machine tool and vision system to realize the function of grasp, process and verify. It has influence on the manufacturing of the industrial robot.

Automated detection and classification of dice

NASA Astrophysics Data System (ADS)

Correia, Bento A. B.; Silva, Jeronimo A.; Carvalho, Fernando D.; Guilherme, Rui; Rodrigues, Fernando C.; de Silva Ferreira, Antonio M.

1995-03-01

This paper describes a typical machine vision system in an unusual application, the automated visual inspection of a Casino's playing tables. The SORTE computer vision system was developed at INETI under a contract with the Portuguese Gaming Inspection Authorities IGJ. It aims to automate the tasks of detection and classification of the dice's scores on the playing tables of the game `Banca Francesa' (which means French Banking) in Casinos. The system is based on the on-line analysis of the images captured by a monochrome CCD camera placed over the playing tables, in order to extract relevant information concerning the score indicated by the dice. Image processing algorithms for real time automatic throwing detection and dice classification were developed and implemented.

BI-sparsity pursuit for robust subspace recovery

DOE PAGES

Bian, Xiao; Krim, Hamid

2015-09-01

Here, the success of sparse models in computer vision and machine learning in many real-world applications, may be attributed in large part, to the fact that many high dimensional data are distributed in a union of low dimensional subspaces. The underlying structure may, however, be adversely affected by sparse errors, thus inducing additional complexity in recovering it. In this paper, we propose a bi-sparse model as a framework to investigate and analyze this problem, and provide as a result , a novel algorithm to recover the union of subspaces in presence of sparse corruptions. We additionally demonstrate the effectiveness ofmore » our method by experiments on real-world vision data.« less

Experimental Semiautonomous Vehicle

NASA Technical Reports Server (NTRS)

Wilcox, Brian H.; Mishkin, Andrew H.; Litwin, Todd E.; Matthies, Larry H.; Cooper, Brian K.; Nguyen, Tam T.; Gat, Erann; Gennery, Donald B.; Firby, Robert J.; Miller, David P.;

Application of Machine Learning in Urban Greenery Land Cover Extraction

NASA Astrophysics Data System (ADS)

Qiao, X.; Li, L. L.; Li, D.; Gan, Y. L.; Hou, A. Y.

2018-04-01

Urban greenery is a critical part of the modern city and the greenery coverage information is essential for land resource management, environmental monitoring and urban planning. It is a challenging work to extract the urban greenery information from remote sensing image as the trees and grassland are mixed with city built-ups. In this paper, we propose a new automatic pixel-based greenery extraction method using multispectral remote sensing images. The method includes three main steps. First, a small part of the images is manually interpreted to provide prior knowledge. Secondly, a five-layer neural network is trained and optimised with the manual extraction results, which are divided to serve as training samples, verification samples and testing samples. Lastly, the well-trained neural network will be applied to the unlabelled data to perform the greenery extraction. The GF-2 and GJ-1 high resolution multispectral remote sensing images were used to extract greenery coverage information in the built-up areas of city X. It shows a favourable performance in the 619 square kilometers areas. Also, when comparing with the traditional NDVI method, the proposed method gives a more accurate delineation of the greenery region. Due to the advantage of low computational load and high accuracy, it has a great potential for large area greenery auto extraction, which saves a lot of manpower and resources.

High Performance Parallel Architectures

NASA Technical Reports Server (NTRS)

El-Ghazawi, Tarek; Kaewpijit, Sinthop

1998-01-01

Traditional remote sensing instruments are multispectral, where observations are collected at a few different spectral bands. Recently, many hyperspectral instruments, that can collect observations at hundreds of bands, have been operational. Furthermore, there have been ongoing research efforts on ultraspectral instruments that can produce observations at thousands of spectral bands. While these remote sensing technology developments hold great promise for new findings in the area of Earth and space science, they present many challenges. These include the need for faster processing of such increased data volumes, and methods for data reduction. Dimension Reduction is a spectral transformation, aimed at concentrating the vital information and discarding redundant data. One such transformation, which is widely used in remote sensing, is the Principal Components Analysis (PCA). This report summarizes our progress on the development of a parallel PCA and its implementation on two Beowulf cluster configuration; one with fast Ethernet switch and the other with a Myrinet interconnection. Details of the implementation and performance results, for typical sets of multispectral and hyperspectral NASA remote sensing data, are presented and analyzed based on the algorithm requirements and the underlying machine configuration. It will be shown that the PCA application is quite challenging and hard to scale on Ethernet-based clusters. However, the measurements also show that a high- performance interconnection network, such as Myrinet, better matches the high communication demand of PCA and can lead to a more efficient PCA execution.

Experimental Demonstration of Adaptive Infrared Multispectral Imaging Using Plasmonic Filter Array (Postprint)

DTIC Science & Technology

2016-10-10

AFRL-RX-WP-JA-2017-0189 EXPERIMENTAL DEMONSTRATION OF ADAPTIVE INFRARED MULTISPECTRAL IMAGING USING PLASMONIC FILTER ARRAY...March 2016 – 23 May 2016 4. TITLE AND SUBTITLE EXPERIMENTAL DEMONSTRATION OF ADAPTIVE INFRARED MULTISPECTRAL IMAGING USING PLASMONIC FILTER ARRAY...experimental demonstration of adaptive multispectral imagery using fabricated plasmonic spectral filter arrays and proposed target detection scenarios

Multispectral photography for earth resources

NASA Technical Reports Server (NTRS)

Wenderoth, S.; Yost, E.; Kalia, R.; Anderson, R.

1972-01-01

A guide for producing accurate multispectral results for earth resource applications is presented along with theoretical and analytical concepts of color and multispectral photography. Topics discussed include: capabilities and limitations of color and color infrared films; image color measurements; methods of relating ground phenomena to film density and color measurement; sensitometry; considerations in the selection of multispectral cameras and components; and mission planning.

Classification by Using Multispectral Point Cloud Data

NASA Astrophysics Data System (ADS)

Liao, C. T.; Huang, H. H.

2012-07-01

Remote sensing images are generally recorded in two-dimensional format containing multispectral information. Also, the semantic information is clearly visualized, which ground features can be better recognized and classified via supervised or unsupervised classification methods easily. Nevertheless, the shortcomings of multispectral images are highly depending on light conditions, and classification results lack of three-dimensional semantic information. On the other hand, LiDAR has become a main technology for acquiring high accuracy point cloud data. The advantages of LiDAR are high data acquisition rate, independent of light conditions and can directly produce three-dimensional coordinates. However, comparing with multispectral images, the disadvantage is multispectral information shortage, which remains a challenge in ground feature classification through massive point cloud data. Consequently, by combining the advantages of both LiDAR and multispectral images, point cloud data with three-dimensional coordinates and multispectral information can produce a integrate solution for point cloud classification. Therefore, this research acquires visible light and near infrared images, via close range photogrammetry, by matching images automatically through free online service for multispectral point cloud generation. Then, one can use three-dimensional affine coordinate transformation to compare the data increment. At last, the given threshold of height and color information is set as threshold in classification.

Three Dimensional Urban Characterization by IFSAR Measurements

NASA Technical Reports Server (NTRS)

Gamba, P.; Houshmand, B.

1998-01-01

In this paper a machine vision approach is applied to Interferometric Synthetic Aperture Radars (IFSAR) data to extract the most relevant built structures in a dense urban environment. The algorithm tries to cluster primitives (line segments) into more complex surfaces (planes) to approximate the 3D shape of these objects. Very interesting results starting from TOPSAR data recorded over S, Monica are presented.

Optical Constituents at the Mouth of the Columbia River: Variability and Signature in Remotely Sensed Reflectance

DTIC Science & Technology

2013-09-30

constructed at BIO, carried the new Machine Vision Floc Camera (MVFC), a Sequoia Scientific LISST 100x Type B, an RBR CTD, and two pressure-actuated...WetStar CDOM fluorometer, a Sequoia Scientific flow control switch, and a SeaBird 37 CTD. The flow-control switch allows the ac- 9 to collect 0.2-um

Nondestructive evaluation of hardwood logs:CT scanning, machine vision and data utilization

Treesearch

Daniel L. Schmoldt; Luis G. Occena; A. Lynn Abbott; Nand K. Gupta

1999-01-01

Sawing of hardwood logs still relies on relatively simple technologies that, in spite of their lack of sophistication, have been successful for many years due to wood?s traditional low cost and ready availability. These characteristics of the hardwood resource have changed dramatically over the past 20 years, however, forcing wood processors to become more efficient in...

A brief review of machine vision in the context of automated wood identification systems

Treesearch

John C. Hermanson; Alex C. Wiedenhoeft

2011-01-01

The need for accurate and rapid field identification of wood to combat illegal logging around the world is outpacing the ability to train personnel to perform this task. Despite increased interest in non-anatomical (DNA, spectroscopic, chemical) methods for wood identification, anatomical characteristics are the least labile data that can be extracted from solid wood...

A Multisensor Machine Vision System for Hardwood Defect Detection

Treesearch

Richard W. Conners; Tai-Hoon Cho; Chong T. Ng; Thomas T. Drayer; Philip A. Araman; Robert L. Brisbon

1990-01-01

Over the next decade there is going to be a substantial change in the way forest products manufacturing industries do business. The economic forces responsible for these changes include the heightened economic competition that will result from the new world economy and the continued increase in the cost of both raw material and labor. These factors are going to force...

A Comparison of Rule-Based, K-Nearest Neighbor, and Neural Net Classifiers for Automated

Treesearch

Tai-Hoon Cho; Richard W. Conners; Philip A. Araman

1991-01-01

Over the last few years the authors have been involved in research aimed at developing a machine vision system for locating and identifying surface defects on materials. The particular problem being studied involves locating surface defects on hardwood lumber in a species independent manner. Obviously, the accurate location and identification of defects is of paramount...

Proceedings of the international conference on cybernetics and societ

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

Not Available

1985-01-01

This book presents the papers given at a conference on artificial intelligence, expert systems and knowledge bases. Topics considered at the conference included automating expert system development, modeling expert systems, causal maps, data covariances, robot vision, image processing, multiprocessors, parallel processing, VLSI structures, man-machine systems, human factors engineering, cognitive decision analysis, natural language, computerized control systems, and cybernetics.

Characterization of Defects in Lumber Using Color, Shape, and Density Information

Treesearch

B.H. Bond; D. Earl Kline; Philip A. Araman

1998-01-01

To help guide the development of multi-sensor machine vision systems for defect detection in lumber, a fundamental understanding of wood defects is needed. The purpose of this research was to advance the basic understanding of defects in lumber by describing them in terms of parameters that can be derived from color and x-ray scanning technologies and to demonstrate...

Evaluation of a multi-sensor machine vision system for automated hardwood lumber grading

Treesearch

D. Earl Kline; Chris Surak; Philip A. Araman

2000-01-01

Over the last 10 years, scientists at the Thomas M. Brooks Forest Products Center, the Bradley Department of Electrical Engineering, and the USDA Forest Service have been working on lumber scanning systems that can accurately locate and identify defects in hardwood lumber. Current R&D efforts are targeted toward developing automated lumber grading technologies. The...

Colour Model for Outdoor Machine Vision for Tropical Regions and its Comparison with the CIE Model

NASA Astrophysics Data System (ADS)

Sahragard, Nasrolah; Ramli, Abdul Rahman B.; Hamiruce Marhaban, Mohammad; Mansor, Shattri B.

2011-02-01

Accurate modeling of daylight and surface reflectance are very useful for most outdoor machine vision applications specifically those which are based on color recognition. Existing daylight CIE model has drawbacks that limit its ability to predict the color of incident light. These limitations include lack of considering ambient light, effects of light reflected off the ground, and context specific information. Previously developed color model is only tested for a few geographical places in North America and its accountability is under question for other places in the world. Besides, existing surface reflectance models are not easily applied to outdoor images. A reflectance model with combined diffuse and specular reflection in normalized HSV color space could be used to predict color. In this paper, a new daylight color model showing the color of daylight for a broad range of sky conditions is developed which will suit weather conditions of tropical places such as Malaysia. A comparison of this daylight color model and daylight CIE model will be discussed. The colors of matte and specular surfaces have been estimated by use of the developed color model and surface reflection function in this paper. The results are shown to be highly reliable.

Roadway Marking Optics for Autonomous Vehicle Guidance and Other Machine Vision Applications

NASA Astrophysics Data System (ADS)

Konopka, Anthony T.

This work determines optimal planar geometric light source and optical imager configurations and electromagnetic wavelengths for maximizing the reflected signal intensity when using machine vision technology to image roadway markings with embedded spherical glass beads. It is found through a first set of experiments that roadway marking samples exhibiting little or no bead rolling effects are uniformly reflective with respect to the azimuthal angle of observation when measured for retroreflectivity within industry standard 30-meter geometry. A second set of experiments indicate that white roadway markings exhibit higher reflectivity throughout the visible spectrum than yellow roadway markings. A roadway marking optical model capable of being used to determine optimal geometric light source and optical imager configurations for maximizing the reflected signal intensities of roadway marking targets is constructed and simulated using optical engineering software. It is found through a third set of experiments that high signal intensities can be measured when the polar angles of the light source and optical imager along a plane normal to a roadway marking are equal, with the maximum signal intensity being measured when the polar angles of both the light source and optical imager are 90°.

Automated Counting of Particles To Quantify Cleanliness

NASA Technical Reports Server (NTRS)

Rhode, James

2005-01-01

A machine vision system, similar to systems used in microbiological laboratories to count cultured microbes, has been proposed for quantifying the cleanliness of nominally precisely cleaned hardware by counting residual contaminant particles. The system would include a microscope equipped with an electronic camera and circuitry to digitize the camera output, a personal computer programmed with machine-vision and interface software, and digital storage media. A filter pad, through which had been aspirated solvent from rinsing the hardware in question, would be placed on the microscope stage. A high-resolution image of the filter pad would be recorded. The computer would analyze the image and present a histogram of sizes of particles on the filter. On the basis of the histogram and a measure of the desired level of cleanliness, the hardware would be accepted or rejected. If the hardware were accepted, the image would be saved, along with other information, as a quality record. If the hardware were rejected, the histogram and ancillary information would be recorded for analysis of trends. The software would perceive particles that are too large or too numerous to meet a specified particle-distribution profile. Anomalous particles or fibrous material would be flagged for inspection.

Deep Learning: A Primer for Radiologists.

PubMed

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

A real-time visual inspection method of fastening bolts in freight car operation

NASA Astrophysics Data System (ADS)

Nan, Guo; Yao, JunEn

2015-10-01

A real-time inspection of the key components is necessary for ensuring safe operation of freight car. While traditional inspection depends on the trained human inspectors, which is time-consuming and lower efficient. With the development of machine vision, vision-based inspection methods get more railway on-spot applications. The cross rod end fastening bolts are important components on both sides of the train body that fixing locking plates together with the freight car main structure. In our experiment, we get the images containing fastening bolt components, and accurately locate the locking plate position using a linear Support Vector Machine (SVM) locating model trained with Histograms of Oriented Gradients (HOG) features. Then we extract the straight line segment using the Line Segment Detector (LSD) and encoding them in a range, which constitute a straight line segment dataset. Lastly we determine the locking plate's working state by the linear pattern. The experiment result shows that the localization accurate rate is over 99%, the fault detection rate is over 95%, and the module implementation time is 2f/s. The overall performance can completely meet the practical railway safety assurance application.

Machine vision and the OMV

NASA Technical Reports Server (NTRS)

Mcanulty, M. A.

1986-01-01

The orbital Maneuvering Vehicle (OMV) is intended to close with orbiting targets for relocation or servicing. It will be controlled via video signals and thruster activation based upon Earth or space station directives. A human operator is squarely in the middle of the control loop for close work. Without directly addressing future, more autonomous versions of a remote servicer, several techniques that will doubtless be important in a future increase of autonomy also have some direct application to the current situation, particularly in the area of image enhancement and predictive analysis. Several techniques are presentet, and some few have been implemented, which support a machine vision capability proposed to be adequate for detection, recognition, and tracking. Once feasibly implemented, they must then be further modified to operate together in real time. This may be achieved by two courses, the use of an array processor and some initial steps toward data reduction. The methodology or adapting to a vector architecture is discussed in preliminary form, and a highly tentative rationale for data reduction at the front end is also discussed. As a by-product, a working implementation of the most advanced graphic display technique, ray-casting, is described.

The JPL/KSC telerobotic inspection demonstration

NASA Technical Reports Server (NTRS)

Mittman, David; Bon, Bruce; Collins, Carol; Fleischer, Gerry; Litwin, Todd; Morrison, Jack; Omeara, Jacquie; Peters, Stephen; Brogdon, John; Humeniuk, Bob

1990-01-01

An ASEA IRB90 robotic manipulator with attached inspection cameras was moved through a Space Shuttle Payload Assist Module (PAM) Cradle under computer control. The Operator and Operator Control Station, including graphics simulation, gross-motion spatial planning, and machine vision processing, were located at JPL. The Safety and Support personnel, PAM Cradle, IRB90, and image acquisition system, were stationed at the Kennedy Space Center (KSC). Images captured at KSC were used both for processing by a machine vision system at JPL, and for inspection by the JPL Operator. The system found collision-free paths through the PAM Cradle, demonstrated accurate knowledge of the location of both objects of interest and obstacles, and operated with a communication delay of two seconds. Safe operation of the IRB90 near Shuttle flight hardware was obtained both through the use of a gross-motion spatial planner developed at JPL using artificial intelligence techniques, and infrared beams and pressure sensitive strips mounted to the critical surfaces of the flight hardward at KSC. The Demonstration showed that telerobotics is effective for real tasks, safe for personnel and hardware, and highly productive and reliable for Shuttle payload operations and Space Station external operations.

Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination

PubMed Central

Jeon, Hong Y.; Tian, Lei F.; Zhu, Heping

2011-01-01

An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA). PMID:22163954

Automatic detection system of shaft part surface defect based on machine vision

NASA Astrophysics Data System (ADS)

Jiang, Lixing; Sun, Kuoyuan; Zhao, Fulai; Hao, Xiangyang

2015-05-01

Surface physical damage detection is an important part of the shaft parts quality inspection and the traditional detecting methods are mostly human eye identification which has many disadvantages such as low efficiency, bad reliability. In order to improve the automation level of the quality detection of shaft parts and establish its relevant industry quality standard, a machine vision inspection system connected with MCU was designed to realize the surface detection of shaft parts. The system adopt the monochrome line-scan digital camera and use the dark-field and forward illumination technology to acquire images with high contrast; the images were segmented to Bi-value images through maximum between-cluster variance method after image filtering and image enhancing algorithms; then the mainly contours were extracted based on the evaluation criterion of the aspect ratio and the area; then calculate the coordinates of the centre of gravity of defects area, namely locating point coordinates; At last, location of the defects area were marked by the coding pen communicated with MCU. Experiment show that no defect was omitted and false alarm error rate was lower than 5%, which showed that the designed system met the demand of shaft part on-line real-time detection.

KENNEDY SPACE CENTER, FLA. - Center Director Jim Kennedy talks to radio station WFLA-AM and Florida Radio Network about his trip to Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla. Kennedy made the trip with NASA astronaut Kay Hire to share the agency’s new vision for space exploration with the next generation of explorers. Kennedy is talking with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

NASA Image and Video Library

2004-02-20

KENNEDY SPACE CENTER, FLA. - Center Director Jim Kennedy talks to radio station WFLA-AM and Florida Radio Network about his trip to Garland V. Stewart Magnet Middle School, a NASA Explorer School (NES) in Tampa, Fla. Kennedy made the trip with NASA astronaut Kay Hire to share the agency’s new vision for space exploration with the next generation of explorers. Kennedy is talking with students about our destiny as explorers, NASA’s stepping stone approach to exploring Earth, the Moon, Mars and beyond, how space impacts our lives, and how people and machines rely on each other in space.

Gimbaled multispectral imaging system and method

DOEpatents

Brown, Kevin H.; Crollett, Seferino; Henson, Tammy D.; Napier, Matthew; Stromberg, Peter G.

2016-01-26

A gimbaled multispectral imaging system and method is described herein. In an general embodiment, the gimbaled multispectral imaging system has a cross support that defines a first gimbal axis and a second gimbal axis, wherein the cross support is rotatable about the first gimbal axis. The gimbaled multispectral imaging system comprises a telescope that fixed to an upper end of the cross support, such that rotation of the cross support about the first gimbal axis causes the tilt of the telescope to alter. The gimbaled multispectral imaging system includes optics that facilitate on-gimbal detection of visible light and off-gimbal detection of infrared light.

Confessions of a robot lobotomist

NASA Technical Reports Server (NTRS)

Gottshall, R. Marc

1994-01-01

Since its inception, numerically controlled (NC) machining methods have been used throughout the aerospace industry to mill, drill, and turn complex shapes by sequentially stepping through motion programs. However, the recent demand for more precision, faster feeds, exotic sensors, and branching execution have existing computer numerical control (CNC) and distributed numerical control (DNC) systems running at maximum controller capacity. Typical disadvantages of current CNC's include fixed memory capacities, limited communication ports, and the use of multiple control languages. The need to tailor CNC's to meet specific applications, whether it be expanded memory, additional communications, or integrated vision, often requires replacing the original controller supplied with the commercial machine tool with a more powerful and capable system. This paper briefly describes the process and equipment requirements for new controllers and their evolutionary implementation in an aerospace environment. The process of controller retrofit with currently available machines is examined, along with several case studies and their computational and architectural implications.

A multispectral imaging approach for diagnostics of skin pathologies

NASA Astrophysics Data System (ADS)

Lihacova, Ilze; Derjabo, Aleksandrs; Spigulis, Janis

2013-06-01

Noninvasive multispectral imaging method was applied for different skin pathology such as nevus, basal cell carcinoma, and melanoma diagnostics. Developed melanoma diagnostic parameter, using three spectral bands (540 nm, 650 nm and 950 nm), was calculated for nevus, melanoma and basal cell carcinoma. Simple multispectral diagnostic device was established and applied for skin assessment. Development and application of multispectral diagnostics method described further in this article.

Computer vision applied to herbarium specimens of German trees: testing the future utility of the millions of herbarium specimen images for automated identification.

PubMed

Unger, Jakob; Merhof, Dorit; Renner, Susanne

2016-11-16

Global Plants, a collaborative between JSTOR and some 300 herbaria, now contains about 2.48 million high-resolution images of plant specimens, a number that continues to grow, and collections that are digitizing their specimens at high resolution are allocating considerable recourses to the maintenance of computer hardware (e.g., servers) and to acquiring digital storage space. We here apply machine learning, specifically the training of a Support-Vector-Machine, to classify specimen images into categories, ideally at the species level, using the 26 most common tree species in Germany as a test case. We designed an analysis pipeline and classification system consisting of segmentation, normalization, feature extraction, and classification steps and evaluated the system in two test sets, one with 26 species, the other with 17, in each case using 10 images per species of plants collected between 1820 and 1995, which simulates the empirical situation that most named species are represented in herbaria and databases, such as JSTOR, by few specimens. We achieved 73.21% accuracy of species assignments in the larger test set, and 84.88% in the smaller test set. The results of this first application of a computer vision algorithm trained on images of herbarium specimens shows that despite the problem of overlapping leaves, leaf-architectural features can be used to categorize specimens to species with good accuracy. Computer vision is poised to play a significant role in future rapid identification at least for frequently collected genera or species in the European flora.