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.

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...

CAOS-CMOS camera.

PubMed

Riza, Nabeel A; La Torre, Juan Pablo; Amin, M Junaid

2016-06-13

Proposed and experimentally demonstrated is the CAOS-CMOS camera design that combines the coded access optical sensor (CAOS) imager platform with the CMOS multi-pixel optical sensor. The unique CAOS-CMOS camera engages the classic CMOS sensor light staring mode with the time-frequency-space agile pixel CAOS imager mode within one programmable optical unit to realize a high dynamic range imager for extreme light contrast conditions. The experimentally demonstrated CAOS-CMOS camera is built using a digital micromirror device, a silicon point-photo-detector with a variable gain amplifier, and a silicon CMOS sensor with a maximum rated 51.3 dB dynamic range. White light imaging of three different brightness simultaneously viewed targets, that is not possible by the CMOS sensor, is achieved by the CAOS-CMOS camera demonstrating an 82.06 dB dynamic range. Applications for the camera include industrial machine vision, welding, laser analysis, automotive, night vision, surveillance and multispectral military systems.

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.

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...

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

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.

Photogrammetry research for FAST eleven-meter reflector panel surface shape measurement

NASA Astrophysics Data System (ADS)

Zhou, Rongwei; Zhu, Lichun; Li, Weimin; Hu, Jingwen; Zhai, Xuebing

2010-10-01

In order to design and manufacture the Five-hundred-meter Aperture Spherical Radio Telescope (FAST) active reflector measuring equipment, measurement on each reflector panel surface shape was presented, static measurement of the whole neutral spherical network of nodes was performed, real-time dynamic measurement at the cable network dynamic deformation was undertaken. In the implementation process of the FAST, reflector panel surface shape detection was completed before eleven-meter reflector panel installation. Binocular vision system was constructed based on the method of binocular stereo vision in machine vision, eleven-meter reflector panel surface shape was measured with photogrammetry method. Cameras were calibrated with the feature points. Under the linearity camera model, the lighting spot array was used as calibration standard pattern, and the intrinsic and extrinsic parameters were acquired. The images were collected for digital image processing and analyzing with two cameras, feature points were extracted with the detection algorithm of characteristic points, and those characteristic points were matched based on epipolar constraint method. Three-dimensional reconstruction coordinates of feature points were analyzed and reflective panel surface shape structure was established by curve and surface fitting method. The error of reflector panel surface shape was calculated to realize automatic measurement on reflector panel surface shape. The results show that unit reflector panel surface inspection accuracy was 2.30mm, within the standard deviation error of 5.00mm. Compared with the requirement of reflector panel machining precision, photogrammetry has fine precision and operation feasibility on eleven-meter reflector panel surface shape measurement for FAST.

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.

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.

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.

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.

Intelligent excavator control system for lunar mining system

NASA Astrophysics Data System (ADS)

Lever, Paul J. A.; Wang, Fei-Yue

1995-01-01

A major benefit of utilizing local planetary resources is that it reduces the need and cost of lifting materials from the Earth's surface into Earth orbit. The location of the moon makes it an ideal site for harvesting the materials needed to assist space activities. Here, lunar excavation will take place in the dynamic unstructured lunar environment, in which conditions are highly variable and unpredictable. Autonomous mining (excavation) machines are necessary to remove human operators from this hazardous environment. This machine must use a control system structure that can identify, plan, sense, and control real-time dynamic machine movements in the lunar environment. The solution is a vision-based hierarchical control structure. However, excavation tasks require force/torque sensor feedback to control the excavation tool after it has penetrated the surface. A fuzzy logic controller (FLC) is used to interpret the forces and torques gathered from a bucket mounted force/torque sensor during excavation. Experimental results from several excavation tests using the FLC are presented here. These results represent the first step toward an integrated sensing and control system for a lunar mining system.

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...

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.

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

Emulating short-term synaptic dynamics with memristive devices

NASA Astrophysics Data System (ADS)

Berdan, Radu; Vasilaki, Eleni; Khiat, Ali; Indiveri, Giacomo; Serb, Alexandru; Prodromakis, Themistoklis

2016-01-01

Neuromorphic architectures offer great promise for achieving computation capacities beyond conventional Von Neumann machines. The essential elements for achieving this vision are highly scalable synaptic mimics that do not undermine biological fidelity. Here we demonstrate that single solid-state TiO2 memristors can exhibit non-associative plasticity phenomena observed in biological synapses, supported by their metastable memory state transition properties. We show that, contrary to conventional uses of solid-state memory, the existence of rate-limiting volatility is a key feature for capturing short-term synaptic dynamics. We also show how the temporal dynamics of our prototypes can be exploited to implement spatio-temporal computation, demonstrating the memristors full potential for building biophysically realistic neural processing systems.

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.

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.

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.

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...

The 4-D approach to visual control of autonomous systems

NASA Technical Reports Server (NTRS)

Dickmanns, Ernst D.

1994-01-01

Development of a 4-D approach to dynamic machine vision is described. Core elements of this method are spatio-temporal models oriented towards objects and laws of perspective projection in a foward mode. Integration of multi-sensory measurement data was achieved through spatio-temporal models as invariants for object recognition. Situation assessment and long term predictions were allowed through maintenance of a symbolic 4-D image of processes involving objects. Behavioral capabilities were easily realized by state feedback and feed-foward control.

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

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.

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.

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.

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.

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.

Three-dimensional dynamic deformation monitoring using a laser-scanning system

NASA Astrophysics Data System (ADS)

Al-Hanbali, Nedal N.; Teskey, William F.

1994-10-01

Non-contact dynamic deformation monitoring (e.g. with a laser scanning system) is very useful in monitoring changes in alignment and changes in size and shape of coupled operating machines. If relative movements between coupled operating machines are large, excessive wear in the machines or unplanned shutdowns due to machinery failure will occur. The purpose of non-contact dynamic deformation monitoring is to identify the causes of large movements and point to remedial action that can be taken to prevent them. The laser scanning system is a laser-based 3D vision system. The system-technique is based on an auto- synchronized triangulation scanning scheme. The system provides accurate, fast, and reliable 3D measurements and can measure objects between 0.5 m to 100 m with a field of view of 40 degree(s) X 50 degree(s). The system is flexible in terms of providing control over the scanned area and depth. The system also provides the user with the intensity image in addition to the depth coded image. This paper reports on the preliminary testing of this system to monitor surface movements and target (point) movements. The monitoring resolution achieved for an operating motorized alignment test rig in the lab was 1 mm for surface movements and 0.50 m for target movements. Raw data manipulation, local calibration, and the method of relating measurements to control points will be discussed. Possibilities for improving the resolution and recommendations for future development will also be presented.

A hybrid 2D/3D inspection concept with smart routing optimisation for high throughput, high dynamic range and traceable critical dimension metrology

NASA Astrophysics Data System (ADS)

Jones, Christopher W.; O’Connor, Daniel

2018-07-01

Dimensional surface metrology is required to enable advanced manufacturing process control for products such as large-area electronics, microfluidic structures, and light management films, where performance is determined by micrometre-scale geometry or roughness formed over metre-scale substrates. While able to perform 100% inspection at a low cost, commonly used 2D machine vision systems are insufficient to assess all of the functionally relevant critical dimensions in such 3D products on their own. While current high-resolution 3D metrology systems are able to assess these critical dimensions, they have a relatively small field of view and are thus much too slow to keep up with full production speeds. A hybrid 2D/3D inspection concept is demonstrated, combining a small field of view, high-performance 3D topography-measuring instrument with a large field of view, high-throughput 2D machine vision system. In this concept, the location of critical dimensions and defects are first registered using the 2D system, then smart routing algorithms and high dynamic range (HDR) measurement strategies are used to efficiently acquire local topography using the 3D sensor. A motion control platform with a traceable position referencing system is used to recreate various sheet-to-sheet and roll-to-roll inline metrology scenarios. We present the artefacts and procedures used to calibrate this hybrid sensor system for traceable dimensional measurement, as well as exemplar measurement of optically challenging industrial test structures.

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…

Unsupervised and self-mapping category formation and semantic object recognition for mobile robot vision used in an actual environment

NASA Astrophysics Data System (ADS)

Madokoro, H.; Tsukada, M.; Sato, K.

2013-07-01

This paper presents an unsupervised learning-based object category formation and recognition method for mobile robot vision. Our method has the following features: detection of feature points and description of features using a scale-invariant feature transform (SIFT), selection of target feature points using one class support vector machines (OC-SVMs), generation of visual words using self-organizing maps (SOMs), formation of labels using adaptive resonance theory 2 (ART-2), and creation and classification of categories on a category map of counter propagation networks (CPNs) for visualizing spatial relations between categories. Classification results of dynamic images using time-series images obtained using two different-size robots and according to movements respectively demonstrate that our method can visualize spatial relations of categories while maintaining time-series characteristics. Moreover, we emphasize the effectiveness of our method for category formation of appearance changes of objects.

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

On the Application of Image Processing Methods for Bubble Recognition to the Study of Subcooled Flow Boiling of Water in Rectangular Channels

PubMed Central

Paz, Concepción; Conde, Marcos; Porteiro, Jacobo; Concheiro, Miguel

2017-01-01

This work introduces the use of machine vision in the massive bubble recognition process, which supports the validation of boiling models involving bubble dynamics, as well as nucleation frequency, active site density and size of the bubbles. The two algorithms presented are meant to be run employing quite standard images of the bubbling process, recorded in general-purpose boiling facilities. The recognition routines are easily adaptable to other facilities if a minimum number of precautions are taken in the setup and in the treatment of the information. Both the side and front projections of subcooled flow-boiling phenomenon over a plain plate are covered. Once all of the intended bubbles have been located in space and time, the proper post-process of the recorded data become capable of tracking each of the recognized bubbles, sketching their trajectories and size evolution, locating the nucleation sites, computing their diameters, and so on. After validating the algorithm’s output against the human eye and data from other researchers, machine vision systems have been demonstrated to be a very valuable option to successfully perform the recognition process, even though the optical analysis of bubbles has not been set as the main goal of the experimental facility. PMID:28632158

The Southampton-York Natural Scenes (SYNS) dataset: Statistics of surface attitude

PubMed Central

Adams, Wendy J.; Elder, James H.; Graf, Erich W.; Leyland, Julian; Lugtigheid, Arthur J.; Muryy, Alexander

2016-01-01

Recovering 3D scenes from 2D images is an under-constrained task; optimal estimation depends upon knowledge of the underlying scene statistics. Here we introduce the Southampton-York Natural Scenes dataset (SYNS: https://syns.soton.ac.uk), which provides comprehensive scene statistics useful for understanding biological vision and for improving machine vision systems. In order to capture the diversity of environments that humans encounter, scenes were surveyed at random locations within 25 indoor and outdoor categories. Each survey includes (i) spherical LiDAR range data (ii) high-dynamic range spherical imagery and (iii) a panorama of stereo image pairs. We envisage many uses for the dataset and present one example: an analysis of surface attitude statistics, conditioned on scene category and viewing elevation. Surface normals were estimated using a novel adaptive scale selection algorithm. Across categories, surface attitude below the horizon is dominated by the ground plane (0° tilt). Near the horizon, probability density is elevated at 90°/270° tilt due to vertical surfaces (trees, walls). Above the horizon, probability density is elevated near 0° slant due to overhead structure such as ceilings and leaf canopies. These structural regularities represent potentially useful prior assumptions for human and machine observers, and may predict human biases in perceived surface attitude. PMID:27782103

Multispectral Image Processing for Plants

NASA Technical Reports Server (NTRS)

Miles, Gaines E.

1991-01-01

The development of a machine vision system to monitor plant growth and health is one of three essential steps towards establishing an intelligent system capable of accurately assessing the state of a controlled ecological life support system for long-term space travel. Besides a network of sensors, simulators are needed to predict plant features, and artificial intelligence algorithms are needed to determine the state of a plant based life support system. Multispectral machine vision and image processing can be used to sense plant features, including health and nutritional status.

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.

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.

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.

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.

Paraxial ray solution for liquid-filled variable focus lenses

NASA Astrophysics Data System (ADS)

Wang, Lihui; Oku, Hiromasa; Ishikawa, Masatoshi

2017-12-01

We propose a general solution for determining the cardinal points and effective focal length of a liquid-filled variable focus lens to aid in understanding the dynamic behavior of the lens when the focal length is changed. A prototype of a variable focus lens was fabricated and used to validate the solution. A simplified solution was also presented that can be used to quickly and conveniently calculate the performance of the lens. We expect that the proposed solutions will improve the design of optical systems that contain variable focus lenses, such as machine vision systems with zoom and focus functions.

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.

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.

Generating Contextual Descriptions of Virtual Reality (VR) Spaces

NASA Astrophysics Data System (ADS)

Olson, D. M.; Zaman, C. H.; Sutherland, A.

2017-12-01

Virtual reality holds great potential for science communication, education, and research. However, interfaces for manipulating data and environments in virtual worlds are limited and idiosyncratic. Furthermore, speech and vision are the primary modalities by which humans collect information about the world, but the linking of visual and natural language domains is a relatively new pursuit in computer vision. Machine learning techniques have been shown to be effective at image and speech classification, as well as at describing images with language (Karpathy 2016), but have not yet been used to describe potential actions. We propose a technique for creating a library of possible context-specific actions associated with 3D objects in immersive virtual worlds based on a novel dataset generated natively in virtual reality containing speech, image, gaze, and acceleration data. We will discuss the design and execution of a user study in virtual reality that enabled the collection and the development of this dataset. We will also discuss the development of a hybrid machine learning algorithm linking vision data with environmental affordances in natural language. Our findings demonstrate that it is possible to develop a model which can generate interpretable verbal descriptions of possible actions associated with recognized 3D objects within immersive VR environments. This suggests promising applications for more intuitive user interfaces through voice interaction within 3D environments. It also demonstrates the potential to apply vast bodies of embodied and semantic knowledge to enrich user interaction within VR environments. This technology would allow for applications such as expert knowledge annotation of 3D environments, complex verbal data querying and object manipulation in virtual spaces, and computer-generated, dynamic 3D object affordances and functionality during simulations.

Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources.

PubMed

Liu, Yu-Ting; Pal, Nikhil R; Marathe, Amar R; Wang, Yu-Kai; Lin, Chin-Teng

2017-01-01

A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems.

Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources

PubMed Central

Liu, Yu-Ting; Pal, Nikhil R.; Marathe, Amar R.; Wang, Yu-Kai; Lin, Chin-Teng

2017-01-01

A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems. PMID:28676734

Recent CESAR (Center for Engineering Systems Advanced Research) research activities in sensor based reasoning for autonomous machines

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

Pin, F.G.; de Saussure, G.; Spelt, P.F.

1988-01-01

This paper describes recent research activities at the Center for Engineering Systems Advanced Research (CESAR) in the area of sensor based reasoning, with emphasis being given to their application and implementation on our HERMIES-IIB autonomous mobile vehicle. These activities, including navigation and exploration in a-priori unknown and dynamic environments, goal recognition, vision-guided manipulation and sensor-driven machine learning, are discussed within the framework of a scenario in which an autonomous robot is asked to navigate through an unknown dynamic environment, explore, find and dock at the panel, read and understand the status of the panel's meters and dials, learn the functioningmore » of a process control panel, and successfully manipulate the control devices of the panel to solve a maintenance emergency problems. A demonstration of the successful implementation of the algorithms on our HERMIES-IIB autonomous robot for resolution of this scenario is presented. Conclusions are drawn concerning the applicability of the methodologies to more general classes of problems and implications for future work on sensor-driven reasoning for autonomous robots are discussed. 8 refs., 3 figs.« less

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.

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

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)

Ultra-low power high-dynamic range color pixel embedding RGB to r-g chromaticity transformation

NASA Astrophysics Data System (ADS)

Lecca, Michela; Gasparini, Leonardo; Gottardi, Massimo

2014-05-01

This work describes a novel color pixel topology that converts the three chromatic components from the standard RGB space into the normalized r-g chromaticity space. This conversion is implemented with high-dynamic range and with no dc power consumption, and the auto-exposure capability of the sensor ensures to capture a high quality chromatic signal, even in presence of very bright illuminants or in the darkness. The pixel is intended to become the basic building block of a CMOS color vision sensor, targeted to ultra-low power applications for mobile devices, such as human machine interfaces, gesture recognition, face detection. The experiments show that significant improvements of the proposed pixel with respect to standard cameras in terms of energy saving and accuracy on data acquisition. An application to skin color-based description is presented.

Experiments in cooperative manipulation: A system perspective

NASA Technical Reports Server (NTRS)

Schneider, Stanley A.; Cannon, Robert H., Jr.

1989-01-01

In addition to cooperative dynamic control, the system incorporates real time vision feedback, a novel programming technique, and a graphical high level user interface. By focusing on the vertical integration problem, not only these subsystems are examined, but also their interfaces and interactions. The control system implements a multi-level hierarchical structure; the techniques developed for operator input, strategic command, and cooperative dynamic control are presented. At the highest level, a mouse-based graphical user interface allows an operator to direct the activities of the system. Strategic command is provided by a table-driven finite state machine; this methodology provides a powerful yet flexible technique for managing the concurrent system interactions. The dynamic controller implements object impedance control; an extension of Nevill Hogan's impedance control concept to cooperative arm manipulation of a single object. Experimental results are presented, showing the system locating and identifying a moving object catching it, and performing a simple cooperative assembly. Results from dynamic control experiments are also presented, showing the controller's excellent dynamic trajectory tracking performance, while also permitting control of environmental contact force.

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.

Static and dynamic postural control in low-vision and normal-vision adults.

PubMed

Tomomitsu, Mônica S V; Alonso, Angelica Castilho; Morimoto, Eurica; Bobbio, Tatiana G; Greve, Julia M D

2013-04-01

This study aimed to evaluate the influence of reduced visual information on postural control by comparing low-vision and normal-vision adults in static and dynamic conditions. Twenty-five low-vision subjects and twenty-five normal sighted adults were evaluated for static and dynamic balance using four protocols: 1) the Modified Clinical Test of Sensory Interaction on Balance on firm and foam surfaces with eyes opened and closed; 2) Unilateral Stance with eyes opened and closed; 3) Tandem Walk; and 4) Step Up/Over. The results showed that the low-vision group presented greater body sway compared with the normal vision during balance on a foam surface (p≤0.001), the Unilateral Stance test for both limbs (p≤0.001), and the Tandem Walk test. The low-vision group showed greater step width (p≤0.001) and slower gait speed (p≤0.004). In the Step Up/Over task, low-vision participants were more cautious in stepping up (right p≤0.005 and left p≤0.009) and in executing the movement (p≤0.001). These findings suggest that visual feedback is crucial for determining balance, especially for dynamic tasks and on foam surfaces. Low-vision individuals had worse postural stability than normal-vision adults in terms of dynamic tests and balance on foam surfaces.

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.

Intelligent robot trends for factory automation

NASA Astrophysics Data System (ADS)

Hall, Ernest L.

1997-09-01

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The use of these machines in factory automation can improve productivity, increase product quality and improve competitiveness. This paper presents a discussion of recent economic and technical trends. The robotics industry now has a billion-dollar market in the U.S. and is growing. Feasibility studies are presented which also show unaudited healthy rates of return for a variety of robotic applications. Technically, the machines are faster, cheaper, more repeatable, more reliable and safer. The knowledge base of inverse kinematic and dynamic solutions and intelligent controls is increasing. More attention is being given by industry to robots, vision and motion controls. New areas of usage are emerging for service robots, remote manipulators and automated guided vehicles. However, the road from inspiration to successful application is still long and difficult, often taking decades to achieve a new product. More cooperation between government, industry and universities is needed to speed the development of intelligent robots that will benefit both industry and society.

Multispectral image analysis for object recognition and classification

NASA Astrophysics Data System (ADS)

Viau, C. R.; Payeur, P.; Cretu, A.-M.

2016-05-01

Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate decision-making processes. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various fields including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance. The fundamental objective of this research project was to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM's class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets.

Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr 0.2Ti 0.8O 3 Thin Films

DOE PAGES

Agar, Joshua C.; Cao, Ye; Naul, Brett; ...

2018-05-28

Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures ofmore » ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Lastly, among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.« less

Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr 0.2Ti 0.8O 3 Thin Films

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

Agar, Joshua C.; Cao, Ye; Naul, Brett

Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures ofmore » ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Lastly, among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.« less

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.

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.

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

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.

Method and apparatus for characterizing and enhancing the dynamic performance of machine tools

DOEpatents

Barkman, William E; Babelay, Jr., Edwin F

2013-12-17

Disclosed are various systems and methods for assessing and improving the capability of a machine tool. The disclosure applies to machine tools having at least one slide configured to move along a motion axis. Various patterns of dynamic excitation commands are employed to drive the one or more slides, typically involving repetitive short distance displacements. A quantification of a measurable merit of machine tool response to the one or more patterns of dynamic excitation commands is typically derived for the machine tool. Examples of measurable merits of machine tool performance include dynamic one axis positional accuracy of the machine tool, dynamic cross-axis stability of the machine tool, and dynamic multi-axis positional accuracy of the machine tool.

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.

Visualization of anthropometric measures of workers in computer 3D modeling of work place.

PubMed

Mijović, B; Ujević, D; Baksa, S

2001-12-01

In this work, 3D visualization of a work place by means of a computer-made 3D-machine model and computer animation of a worker have been performed. By visualization of 3D characters in inverse kinematic and dynamic relation with the operating part of a machine, the biomechanic characteristics of worker's body have been determined. The dimensions of a machine have been determined by an inspection of technical documentation as well as by direct measurements and recordings of the machine by camera. On the basis of measured body height of workers all relevant anthropometric measures have been determined by a computer program developed by the authors. By knowing the anthropometric measures, the vision fields and the scope zones while forming work places, exact postures of workers while performing technological procedures were determined. The minimal and maximal rotation angles and the translation of upper and lower arm which are basis for the analysis of worker burdening were analyzed. The dimensions of the seized space of a body are obtained by computer anthropometric analysis of movement, e.g. range of arms, position of legs, head, back. The influence of forming of a work place on correct postures of workers during work has been reconsidered and thus the consumption of energy and fatigue can be reduced to a minimum.

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.

AI And Early Vision - Part II

NASA Astrophysics Data System (ADS)

Julesz, Bela

1989-08-01

A quarter of a century ago I introduced two paradigms into psychology which in the intervening years have had a direct impact on the psychobiology of early vision and an indirect one on artificial intelligence (AI or machine vision). The first, the computer-generated random-dot stereogram (RDS) paradigm (Julesz, 1960) at its very inception posed a strategic question both for AI and neurophysiology. The finding that stereoscopic depth perception (stereopsis) is possible without the many enigmatic cues of monocular form recognition - as assumed previously - demonstrated that stereopsis with its basic problem of finding matches between corresponding random aggregates of dots in the left and right visual fields became ripe for modeling. Indeed, the binocular matching problem of stereopsis opened up an entire field of study, eventually leading to the computational models of David Marr (1982) and his coworkers. The fusion of RDS had an even greater impact on neurophysiologists - including Hubel and Wiesel (1962) - who realized that stereopsis must occur at an early stage, and can be studied easier than form perception. This insight recently culminated in the studies by Gian Poggio (1984) who found binocular-disparity - tuned neurons in the input stage to the visual cortex (layer IVB in V1) in the monkey that were selectively triggered by dynamic RDS. Thus the first paradigm led to a strategic insight: that with stereoscopic vision there is no camouflage, and as such was advantageous for our primate ancestors to evolve the cortical machinery of stereoscopic vision to capture camouflaged prey (insects) at a standstill. Amazingly, although stereopsis evolved relatively late in primates, it captured the very input stages of the visual cortex. (For a detailed review, see Julesz, 1986a)

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.

Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via Sparse Coding

PubMed Central

Li, Xin; Guo, Rui; Chen, Chao

2014-01-01

Sparse coding is an emerging method that has been successfully applied to both robust object tracking and recognition in the vision literature. In this paper, we propose to explore a sparse coding-based approach toward joint object tracking-and-recognition and explore its potential in the analysis of forward-looking infrared (FLIR) video to support nighttime machine vision systems. A key technical contribution of this work is to unify existing sparse coding-based approaches toward tracking and recognition under the same framework, so that they can benefit from each other in a closed-loop. On the one hand, tracking the same object through temporal frames allows us to achieve improved recognition performance through dynamical updating of template/dictionary and combining multiple recognition results; on the other hand, the recognition of individual objects facilitates the tracking of multiple objects (i.e., walking pedestrians), especially in the presence of occlusion within a crowded environment. We report experimental results on both the CASIAPedestrian Database and our own collected FLIR video database to demonstrate the effectiveness of the proposed joint tracking-and-recognition approach. PMID:24961216

Dynamic Visual Acuity: a Functionally Relevant Research Tool

NASA Technical Reports Server (NTRS)

Peters, Brian T.; Brady, Rachel A.; Miller, Chris A.; Mulavara, Ajitkumar P.; Wood, Scott J.; Cohen, Helen S.; Bloomberg, Jacob J.

2010-01-01

Coordinated movements between the eyes and head are required to maintain a stable retinal image during head and body motion. The vestibulo-ocular reflex (VOR) plays a significant role in this gaze control system that functions well for most daily activities. However, certain environmental conditions or interruptions in normal VOR function can lead to inadequate ocular compensation, resulting in oscillopsia, or blurred vision. It is therefore possible to use acuity to determine when the environmental conditions, VOR function, or the combination of the two is not conductive for maintaining clear vision. Over several years we have designed and tested several tests of dynamic visual acuity (DVA). Early tests used the difference between standing and walking acuity to assess decrements in the gaze stabilization system after spaceflight. Supporting ground-based studies measured the responses from patients with bilateral vestibular dysfunction and explored the effects of visual target viewing distance and gait cycle events on walking acuity. Results from these studies show that DVA is affected by spaceflight, is degraded in patients with vestibular dysfunction, changes with target distance, and is not consistent across the gait cycle. We have recently expanded our research to include studies in which seated subjects are translated or rotated passively. Preliminary results from this work indicate that gaze stabilization ability may differ between similar active and passive conditions, may change with age, and can be affected by the location of the visual target with respect to the axis of motion. Use of DVA as a diagnostic tool is becoming more popular but the functional nature of the acuity outcome measure also makes it ideal for identifying conditions that could lead to degraded vision. By doing so, steps can be taken to alter the problematic environments to improve the man-machine interface and optimize performance.

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.

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.

Explicit solution techniques for impact with contact constraints

NASA Technical Reports Server (NTRS)

Mccarty, Robert E.

1993-01-01

Modern military aircraft transparency systems, windshields and canopies, are complex systems which must meet a large and rapidly growing number of requirements. Many of these transparency system requirements are conflicting, presenting difficult balances which must be achieved. One example of a challenging requirements balance or trade is shaping for stealth versus aircrew vision. The large number of requirements involved may be grouped in a variety of areas including man-machine interface; structural integration with the airframe; combat hazards; environmental exposures; and supportability. Some individual requirements by themselves pose very difficult, severely nonlinear analysis problems. One such complex problem is that associated with the dynamic structural response resulting from high energy bird impact. An improved analytical capability for soft-body impact simulation was developed.

Explicit solution techniques for impact with contact constraints

NASA Astrophysics Data System (ADS)

McCarty, Robert E.

1993-08-01

Modern military aircraft transparency systems, windshields and canopies, are complex systems which must meet a large and rapidly growing number of requirements. Many of these transparency system requirements are conflicting, presenting difficult balances which must be achieved. One example of a challenging requirements balance or trade is shaping for stealth versus aircrew vision. The large number of requirements involved may be grouped in a variety of areas including man-machine interface; structural integration with the airframe; combat hazards; environmental exposures; and supportability. Some individual requirements by themselves pose very difficult, severely nonlinear analysis problems. One such complex problem is that associated with the dynamic structural response resulting from high energy bird impact. An improved analytical capability for soft-body impact simulation was developed.

A Comparative Study : Microprogrammed Vs Risc Architectures For Symbolic Processing

NASA Astrophysics Data System (ADS)

Heudin, J. C.; Metivier, C.; Demigny, D.; Maurin, T.; Zavidovique, B.; Devos, F.

1987-05-01

It is oftenclaimed that conventional computers are not well suited for human-like tasks : Vision (Image Processing), Intelligence (Symbolic Processing) ... In the particular case of Artificial Intelligence, dynamic type-checking is one example of basic task that must be improved. The solution implemented in most Lisp work-stations consists in a microprogrammed architecture with a tagged memory. Another way to gain efficiency is to design a well suited instruction set for symbolic processing, which reduces the semantic gap between the high level language and the machine code. In this framework, the RISC concept provides a convenient approach to study new architectures for symbolic processing. This paper compares both approaches and describes our projectof designing a compact symbolic processor for Artificial Intelligence applications.

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.

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

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

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

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-10-01

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

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";…

Intelligent robot trends for 1998

NASA Astrophysics Data System (ADS)

Hall, Ernest L.

1998-10-01

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The use of these machines in factory automation can improve productivity, increase product quality and improve competitiveness. This paper presents a discussion of recent technical and economic trends. Technically, the machines are faster, cheaper, more repeatable, more reliable and safer. The knowledge base of inverse kinematic and dynamic solutions and intelligent controls is increasing. More attention is being given by industry to robots, vision and motion controls. New areas of usage are emerging for service robots, remote manipulators and automated guided vehicles. Economically, the robotics industry now has a 1.1 billion-dollar market in the U.S. and is growing. Feasibility studies results are presented which also show decreasing costs for robots and unaudited healthy rates of return for a variety of robotic applications. However, the road from inspiration to successful application can be long and difficult, often taking decades to achieve a new product. A greater emphasis on mechatronics is needed in our universities. Certainly, more cooperation between government, industry and universities is needed to speed the development of intelligent robots that will benefit industry and society.

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning.

PubMed

Hong, Weizhe; Kennedy, Ann; Burgos-Artizzu, Xavier P; Zelikowsky, Moriel; Navonne, Santiago G; Perona, Pietro; Anderson, David J

2015-09-22

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

PubMed Central

Hong, Weizhe; Kennedy, Ann; Burgos-Artizzu, Xavier P.; Zelikowsky, Moriel; Navonne, Santiago G.; Perona, Pietro; Anderson, David J.

2015-01-01

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body “pose” of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics. PMID:26354123

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.

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

A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data.

PubMed

Manzi, Alessandro; Dario, Paolo; Cavallo, Filippo

2017-05-11

Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.

UAV Research at NASA Langley: Towards Safe, Reliable, and Autonomous Operations

NASA Technical Reports Server (NTRS)

Davila, Carlos G.

2016-01-01

Unmanned Aerial Vehicles (UAV) are fundamental components in several aspects of research at NASA Langley, such as flight dynamics, mission-driven airframe design, airspace integration demonstrations, atmospheric science projects, and more. In particular, NASA Langley Research Center (Langley) is using UAVs to develop and demonstrate innovative capabilities that meet the autonomy and robotics challenges that are anticipated in science, space exploration, and aeronautics. These capabilities will enable new NASA missions such as asteroid rendezvous and retrieval (ARRM), Mars exploration, in-situ resource utilization (ISRU), pollution measurements in historically inaccessible areas, and the integration of UAVs into our everyday lives all missions of increasing complexity, distance, pace, and/or accessibility. Building on decades of NASA experience and success in the design, fabrication, and integration of robust and reliable automated systems for space and aeronautics, Langley Autonomy Incubator seeks to bridge the gap between automation and autonomy by enabling safe autonomous operations via onboard sensing and perception systems in both data-rich and data-deprived environments. The Autonomy Incubator is focused on the challenge of mobility and manipulation in dynamic and unstructured environments by integrating technologies such as computer vision, visual odometry, real-time mapping, path planning, object detection and avoidance, object classification, adaptive control, sensor fusion, machine learning, and natural human-machine teaming. These technologies are implemented in an architectural framework developed in-house for easy integration and interoperability of cutting-edge hardware and software.

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.

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.

Vision-based navigation in a dynamic environment for virtual human

NASA Astrophysics Data System (ADS)

Liu, Yan; Sun, Ji-Zhou; Zhang, Jia-Wan; Li, Ming-Chu

2004-06-01

Intelligent virtual human is widely required in computer games, ergonomics software, virtual environment and so on. We present a vision-based behavior modeling method to realize smart navigation in a dynamic environment. This behavior model can be divided into three modules: vision, global planning and local planning. Vision is the only channel for smart virtual actor to get information from the outside world. Then, the global and local planning module use A* and D* algorithm to find a way for virtual human in a dynamic environment. Finally, the experiments on our test platform (Smart Human System) verify the feasibility of this behavior model.

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.

Ground Simulation of an Autonomous Satellite Rendezvous and Tracking System Using Dual Robotic Systems

NASA Technical Reports Server (NTRS)

Trube, Matthew J.; Hyslop, Andrew M.; Carignan, Craig R.; Easley, Joseph W.

2012-01-01

A hardware-in-the-loop ground system was developed for simulating a robotic servicer spacecraft tracking a target satellite at short range. A relative navigation sensor package "Argon" is mounted on the end-effector of a Fanuc 430 manipulator, which functions as the base platform of the robotic spacecraft servicer. Machine vision algorithms estimate the pose of the target spacecraft, mounted on a Rotopod R-2000 platform, relay the solution to a simulation of the servicer spacecraft running in "Freespace", which performs guidance, navigation and control functions, integrates dynamics, and issues motion commands to a Fanuc platform controller so that it tracks the simulated servicer spacecraft. Results will be reviewed for several satellite motion scenarios at different ranges. Key words: robotics, satellite, servicing, guidance, navigation, tracking, control, docking.

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

When Ultrasonic Sensors and Computer Vision Join Forces for Efficient Obstacle Detection and Recognition

PubMed Central

Mocanu, Bogdan; Tapu, Ruxandra; Zaharia, Titus

2016-01-01

In the most recent report published by the World Health Organization concerning people with visual disabilities it is highlighted that by the year 2020, worldwide, the number of completely blind people will reach 75 million, while the number of visually impaired (VI) people will rise to 250 million. Within this context, the development of dedicated electronic travel aid (ETA) systems, able to increase the safe displacement of VI people in indoor/outdoor spaces, while providing additional cognition of the environment becomes of outmost importance. This paper introduces a novel wearable assistive device designed to facilitate the autonomous navigation of blind and VI people in highly dynamic urban scenes. The system exploits two independent sources of information: ultrasonic sensors and the video camera embedded in a regular smartphone. The underlying methodology exploits computer vision and machine learning techniques and makes it possible to identify accurately both static and highly dynamic objects existent in a scene, regardless on their location, size or shape. In addition, the proposed system is able to acquire information about the environment, semantically interpret it and alert users about possible dangerous situations through acoustic feedback. To determine the performance of the proposed methodology we have performed an extensive objective and subjective experimental evaluation with the help of 21 VI subjects from two blind associations. The users pointed out that our prototype is highly helpful in increasing the mobility, while being friendly and easy to learn. PMID:27801834

When Ultrasonic Sensors and Computer Vision Join Forces for Efficient Obstacle Detection and Recognition.

PubMed

Mocanu, Bogdan; Tapu, Ruxandra; Zaharia, Titus

2016-10-28

In the most recent report published by the World Health Organization concerning people with visual disabilities it is highlighted that by the year 2020, worldwide, the number of completely blind people will reach 75 million, while the number of visually impaired (VI) people will rise to 250 million. Within this context, the development of dedicated electronic travel aid (ETA) systems, able to increase the safe displacement of VI people in indoor/outdoor spaces, while providing additional cognition of the environment becomes of outmost importance. This paper introduces a novel wearable assistive device designed to facilitate the autonomous navigation of blind and VI people in highly dynamic urban scenes. The system exploits two independent sources of information: ultrasonic sensors and the video camera embedded in a regular smartphone. The underlying methodology exploits computer vision and machine learning techniques and makes it possible to identify accurately both static and highly dynamic objects existent in a scene, regardless on their location, size or shape. In addition, the proposed system is able to acquire information about the environment, semantically interpret it and alert users about possible dangerous situations through acoustic feedback. To determine the performance of the proposed methodology we have performed an extensive objective and subjective experimental evaluation with the help of 21 VI subjects from two blind associations. The users pointed out that our prototype is highly helpful in increasing the mobility, while being friendly and easy to learn.

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

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.

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.

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.

Man/Machine Interaction Dynamics And Performance (MMIDAP) capability

NASA Technical Reports Server (NTRS)

Frisch, Harold P.

1991-01-01

The creation of an ability to study interaction dynamics between a machine and its human operator can be approached from a myriad of directions. The Man/Machine Interaction Dynamics and Performance (MMIDAP) project seeks to create an ability to study the consequences of machine design alternatives relative to the performance of both machine and operator. The class of machines to which this study is directed includes those that require the intelligent physical exertions of a human operator. While Goddard's Flight Telerobotic's program was expected to be a major user, basic engineering design and biomedical applications reach far beyond telerobotics. Ongoing efforts are outlined of the GSFC and its University and small business collaborators to integrate both human performance and musculoskeletal data bases with analysis capabilities necessary to enable the study of dynamic actions, reactions, and performance of coupled machine/operator systems.

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.

Reading recognition of pointer meter based on pattern recognition and dynamic three-points on a line

NASA Astrophysics Data System (ADS)

Zhang, Yongqiang; Ding, Mingli; Fu, Wuyifang; Li, Yongqiang

2017-03-01

Pointer meters are frequently applied to industrial production for they are directly readable. They should be calibrated regularly to ensure the precision of the readings. Currently the method of manual calibration is most frequently adopted to accomplish the verification of the pointer meter, and professional skills and subjective judgment may lead to big measurement errors and poor reliability and low efficiency, etc. In the past decades, with the development of computer technology, the skills of machine vision and digital image processing have been applied to recognize the reading of the dial instrument. In terms of the existing recognition methods, all the parameters of dial instruments are supposed to be the same, which is not the case in practice. In this work, recognition of pointer meter reading is regarded as an issue of pattern recognition. We obtain the features of a small area around the detected point, make those features as a pattern, divide those certified images based on Gradient Pyramid Algorithm, train a classifier with the support vector machine (SVM) and complete the pattern matching of the divided mages. Then we get the reading of the pointer meter precisely under the theory of dynamic three points make a line (DTPML), which eliminates the error caused by tiny differences of the panels. Eventually, the result of the experiment proves that the proposed method in this work is superior to state-of-the-art works.

Pathway concepts experiment for head-down synthetic vision displays

NASA Astrophysics Data System (ADS)

Prinzel, Lawrence J., III; Arthur, Jarvis J., III; Kramer, Lynda J.; Bailey, Randall E.

2004-08-01

Eight 757 commercial airline captains flew 22 approaches using the Reno Sparks 16R Visual Arrival under simulated Category I conditions. Approaches were flown using a head-down synthetic vision display to evaluate four tunnel ("minimal", "box", "dynamic pathway", "dynamic crow's feet") and three guidance ("ball", "tadpole", "follow-me aircraft") concepts and compare their efficacy to a baseline condition (i.e., no tunnel, ball guidance). The results showed that the tunnel concepts significantly improved pilot performance and situation awareness and lowered workload compared to the baseline condition. The dynamic crow's feet tunnel and follow-me aircraft guidance concepts were found to be the best candidates for future synthetic vision head-down displays. These results are discussed with implications for synthetic vision display design and future research.

Design of a surgical robot with dynamic vision field control for Single Port Endoscopic Surgery.

PubMed

Kobayashi, Yo; Sekiguchi, Yuta; Tomono, Yu; Watanabe, Hiroki; Toyoda, Kazutaka; Konishi, Kozo; Tomikawa, Morimasa; Ieiri, Satoshi; Tanoue, Kazuo; Hashizume, Makoto; Fujie, Masaktsu G

2010-01-01

Recently, a robotic system was developed to assist Single Port Endoscopic Surgery (SPS). However, the existing system required a manual change of vision field, hindering the surgical task and increasing the degrees of freedom (DOFs) of the manipulator. We proposed a surgical robot for SPS with dynamic vision field control, the endoscope view being manipulated by a master controller. The prototype robot consisted of a positioning and sheath manipulator (6 DOF) for vision field control, and dual tool tissue manipulators (gripping: 5DOF, cautery: 3DOF). Feasibility of the robot was demonstrated in vitro. The "cut and vision field control" (using tool manipulators) is suitable for precise cutting tasks in risky areas while a "cut by vision field control" (using a vision field control manipulator) is effective for rapid macro cutting of tissues. A resection task was accomplished using a combination of both methods.

The dynamic analysis of drum roll lathe for machining of rollers

NASA Astrophysics Data System (ADS)

Qiao, Zheng; Wu, Dongxu; Wang, Bo; Li, Guo; Wang, Huiming; Ding, Fei

2014-08-01

An ultra-precision machine tool for machining of the roller has been designed and assembled, and due to the obvious impact which dynamic characteristic of machine tool has on the quality of microstructures on the roller surface, the dynamic characteristic of the existing machine tool is analyzed in this paper, so is the influence of circumstance that a large scale and slender roller is fixed in the machine on dynamic characteristic of the machine tool. At first, finite element model of the machine tool is built and simplified, and based on that, the paper carries on with the finite element mode analysis and gets the natural frequency and shaking type of four steps of the machine tool. According to the above model analysis results, the weak stiffness systems of machine tool can be further improved and the reasonable bandwidth of control system of the machine tool can be designed. In the end, considering the shock which is caused by Z axis as a result of fast positioning frequently to feeding system and cutting tool, transient analysis is conducted by means of ANSYS analysis in this paper. Based on the results of transient analysis, the vibration regularity of key components of machine tool and its impact on cutting process are explored respectively.

Real Time Data Management for Estimating Probabilities of Incidents and Near Misses

NASA Astrophysics Data System (ADS)

Stanitsas, P. D.; Stephanedes, Y. J.

2011-08-01

Advances in real-time data collection, data storage and computational systems have led to development of algorithms for transport administrators and engineers that improve traffic safety and reduce cost of road operations. Despite these advances, problems in effectively integrating real-time data acquisition, processing, modelling and road-use strategies at complex intersections and motorways remain. These are related to increasing system performance in identification, analysis, detection and prediction of traffic state in real time. This research develops dynamic models to estimate the probability of road incidents, such as crashes and conflicts, and incident-prone conditions based on real-time data. The models support integration of anticipatory information and fee-based road use strategies in traveller information and management. Development includes macroscopic/microscopic probabilistic models, neural networks, and vector autoregressions tested via machine vision at EU and US sites.

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.

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.

Pathway Concepts Experiment for Head-Down Synthetic Vision Displays

NASA Technical Reports Server (NTRS)

Prinzel, Lawrence J., III; Arthur, Jarvis J., III; Kramer, Lynda J.; Bailey, Randall E.

2004-01-01

Eight 757 commercial airline captains flew 22 approaches using the Reno Sparks 16R Visual Arrival under simulated Category I conditions. Approaches were flown using a head-down synthetic vision display to evaluate four tunnel ("minimal", "box", "dynamic pathway", "dynamic crow s feet") and three guidance ("ball", "tadpole", "follow-me aircraft") concepts and compare their efficacy to a baseline condition (i.e., no tunnel, ball guidance). The results showed that the tunnel concepts significantly improved pilot performance and situation awareness and lowered workload compared to the baseline condition. The dynamic crow s feet tunnel and follow-me aircraft guidance concepts were found to be the best candidates for future synthetic vision head-down displays. These results are discussed with implications for synthetic vision display design and future research.

Dynamic programming and graph algorithms in computer vision.

PubMed

Felzenszwalb, Pedro F; Zabih, Ramin

2011-04-01

Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting since, by carefully exploiting problem structure, they often provide nontrivial guarantees concerning solution quality. In this paper, we review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo, the mid-level problem of interactive object segmentation, and the high-level problem of model-based recognition.

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)

Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna

PubMed Central

Swanson, Alexandra; Kosmala, Margaret; Lintott, Chris; Simpson, Robert; Smith, Arfon; Packer, Craig

2015-01-01

Camera traps can be used to address large-scale questions in community ecology by providing systematic data on an array of wide-ranging species. We deployed 225 camera traps across 1,125 km2 in Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics. The cameras have operated continuously since 2010 and had accumulated 99,241 camera-trap days and produced 1.2 million sets of pictures by 2013. Members of the general public classified the images via the citizen-science website www.snapshotserengeti.org. Multiple users viewed each image and recorded the species, number of individuals, associated behaviours, and presence of young. Over 28,000 registered users contributed 10.8 million classifications. We applied a simple algorithm to aggregate these individual classifications into a final ‘consensus’ dataset, yielding a final classification for each image and a measure of agreement among individual answers. The consensus classifications and raw imagery provide an unparalleled opportunity to investigate multi-species dynamics in an intact ecosystem and a valuable resource for machine-learning and computer-vision research. PMID:26097743

Learning a Mahalanobis Distance-Based Dynamic Time Warping Measure for Multivariate Time Series Classification.

PubMed

Mei, Jiangyuan; Liu, Meizhu; Wang, Yuan-Fang; Gao, Huijun

2016-06-01

Multivariate time series (MTS) datasets broadly exist in numerous fields, including health care, multimedia, finance, and biometrics. How to classify MTS accurately has become a hot research topic since it is an important element in many computer vision and pattern recognition applications. In this paper, we propose a Mahalanobis distance-based dynamic time warping (DTW) measure for MTS classification. The Mahalanobis distance builds an accurate relationship between each variable and its corresponding category. It is utilized to calculate the local distance between vectors in MTS. Then we use DTW to align those MTS which are out of synchronization or with different lengths. After that, how to learn an accurate Mahalanobis distance function becomes another key problem. This paper establishes a LogDet divergence-based metric learning with triplet constraint model which can learn Mahalanobis matrix with high precision and robustness. Furthermore, the proposed method is applied on nine MTS datasets selected from the University of California, Irvine machine learning repository and Robert T. Olszewski's homepage, and the results demonstrate the improved performance of the proposed approach.

The reported incidence of man-machine interface issues in Army aviators using the Aviator's Night Vision System (ANVIS) in a combat theatre

NASA Astrophysics Data System (ADS)

Hiatt, Keith L.; Rash, Clarence E.

2011-06-01

Background: Army Aviators rely on the ANVIS for night operations. Human factors literature notes that the ANVIS man-machine interface results in reports of visual and spinal complaints. This is the first study that has looked at these issues in the much harsher combat environment. Last year, the authors reported on the statistically significant (p<0.01) increased complaints of visual discomfort, degraded visual cues, and incidence of static and dynamic visual illusions in the combat environment [Proc. SPIE, Vol. 7688, 76880G (2010)]. In this paper we present the findings regarding increased spinal complaints and other man-machine interface issues found in the combat environment. Methods: A survey was administered to Aircrew deployed in support of Operation Enduring Freedom (OEF). Results: 82 Aircrew (representing an aggregate of >89,000 flight hours of which >22,000 were with ANVIS) participated. Analysis demonstrated high complaints of almost all levels of back and neck pain. Additionally, the use of body armor and other Aviation Life Support Equipment (ALSE) caused significant ergonomic complaints when used with ANVIS. Conclusions: ANVIS use in a combat environment resulted in higher and different types of reports of spinal symptoms and other man-machine interface issues over what was previously reported. Data from this study may be more operationally relevant than that of the peacetime literature as it is derived from actual combat and not from training flights, and it may have important implications about making combat predictions based on performance in training scenarios. Notably, Aircrew remarked that they could not execute the mission without ANVIS and ALSE and accepted the degraded ergonomic environment.

Design of a dynamic test platform for autonomous robot vision systems

NASA Technical Reports Server (NTRS)

Rich, G. C.

1980-01-01

The concept and design of a dynamic test platform for development and evluation of a robot vision system is discussed. The platform is to serve as a diagnostic and developmental tool for future work with the RPI Mars Rover's multi laser/multi detector vision system. The platform allows testing of the vision system while its attitude is varied, statically or periodically. The vision system is mounted on the test platform. It can then be subjected to a wide variety of simulated can thus be examined in a controlled, quantitative fashion. Defining and modeling Rover motions and designing the platform to emulate these motions are also discussed. Individual aspects of the design process are treated separately, as structural, driving linkages, and motors and transmissions.

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.

Image understanding and the man-machine interface II; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989

NASA Technical Reports Server (NTRS)

Barrett, Eamon B. (Editor); Pearson, James J. (Editor)

1989-01-01

Image understanding concepts and models, image understanding systems and applications, advanced digital processors and software tools, and advanced man-machine interfaces are among the topics discussed. Particular papers are presented on such topics as neural networks for computer vision, object-based segmentation and color recognition in multispectral images, the application of image algebra to image measurement and feature extraction, and the integration of modeling and graphics to create an infrared signal processing test bed.

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

Real-time simulation of the retina allowing visualization of each processing stage

NASA Astrophysics Data System (ADS)

Teeters, Jeffrey L.; Werblin, Frank S.

1991-08-01

The retina computes to let us see, but can we see the retina compute? Until now, the answer has been no, because the unconscious nature of the processing hides it from our view. Here the authors describe a method of seeing computations performed throughout the retina. This is achieved by using neurophysiological data to construct a model of the retina, and using a special-purpose image processing computer (PIPE) to implement the model in real time. Processing in the model is organized into stages corresponding to computations performed by each retinal cell type. The final stage is the transient (change detecting) ganglion cell. A CCD camera forms the input image, and the activity of a selected retinal cell type is the output which is displayed on a TV monitor. By changing the retina cell driving the monitor, the progressive transformations of the image by the retina can be observed. These simulations demonstrate the ubiquitous presence of temporal and spatial variations in the patterns of activity generated by the retina which are fed into the brain. The dynamical aspects make these patterns very different from those generated by the common DOG (Difference of Gaussian) model of receptive field. Because the retina is so successful in biological vision systems, the processing described here may be useful in machine vision.

Driver hand activity analysis in naturalistic driving studies: challenges, algorithms, and experimental studies

NASA Astrophysics Data System (ADS)

Ohn-Bar, Eshed; Martin, Sujitha; Trivedi, Mohan Manubhai

2013-10-01

We focus on vision-based hand activity analysis in the vehicular domain. The study is motivated by the overarching goal of understanding driver behavior, in particular as it relates to attentiveness and risk. First, the unique advantages and challenges for a nonintrusive, vision-based solution are reviewed. Next, two approaches for hand activity analysis, one relying on static (appearance only) cues and another on dynamic (motion) cues, are compared. The motion-cue-based hand detection uses temporally accumulated edges in order to maintain the most reliable and relevant motion information. The accumulated image is fitted with ellipses in order to produce the location of the hands. The method is used to identify three hand activity classes: (1) two hands on the wheel, (2) hand on the instrument panel, (3) hand on the gear shift. The static-cue-based method extracts features in each frame in order to learn a hand presence model for each of the three regions. A second-stage classifier (linear support vector machine) produces the final activity classification. Experimental evaluation with different users and environmental variations under real-world driving shows the promise of applying the proposed systems for both postanalysis of captured driving data as well as for real-time driver assistance.

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...

Relating Standardized Visual Perception Measures to Simulator Visual System Performance

NASA Technical Reports Server (NTRS)

Kaiser, Mary K.; Sweet, Barbara T.

2013-01-01

Human vision is quantified through the use of standardized clinical vision measurements. These measurements typically include visual acuity (near and far), contrast sensitivity, color vision, stereopsis (a.k.a. stereo acuity), and visual field periphery. Simulator visual system performance is specified in terms such as brightness, contrast, color depth, color gamut, gamma, resolution, and field-of-view. How do these simulator performance characteristics relate to the perceptual experience of the pilot in the simulator? In this paper, visual acuity and contrast sensitivity will be related to simulator visual system resolution, contrast, and dynamic range; similarly, color vision will be related to color depth/color gamut. Finally, we will consider how some characteristics of human vision not typically included in current clinical assessments could be used to better inform simulator requirements (e.g., relating dynamic characteristics of human vision to update rate and other temporal display characteristics).

Dynamic Programming and Graph Algorithms in Computer Vision*

PubMed Central

Felzenszwalb, Pedro F.; Zabih, Ramin

2013-01-01

Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting, since by carefully exploiting problem structure they often provide non-trivial guarantees concerning solution quality. In this paper we briefly review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo; the mid-level problem of interactive object segmentation; and the high-level problem of model-based recognition. PMID:20660950

A High-Speed Vision-Based Sensor for Dynamic Vibration Analysis Using Fast Motion Extraction Algorithms.

PubMed

Zhang, Dashan; Guo, Jie; Lei, Xiujun; Zhu, Changan

2016-04-22

The development of image sensor and optics enables the application of vision-based techniques to the non-contact dynamic vibration analysis of large-scale structures. As an emerging technology, a vision-based approach allows for remote measuring and does not bring any additional mass to the measuring object compared with traditional contact measurements. In this study, a high-speed vision-based sensor system is developed to extract structure vibration signals in real time. A fast motion extraction algorithm is required for this system because the maximum sampling frequency of the charge-coupled device (CCD) sensor can reach up to 1000 Hz. Two efficient subpixel level motion extraction algorithms, namely the modified Taylor approximation refinement algorithm and the localization refinement algorithm, are integrated into the proposed vision sensor. Quantitative analysis shows that both of the two modified algorithms are at least five times faster than conventional upsampled cross-correlation approaches and achieve satisfactory error performance. The practicability of the developed sensor is evaluated by an experiment in a laboratory environment and a field test. Experimental results indicate that the developed high-speed vision-based sensor system can extract accurate dynamic structure vibration signals by tracking either artificial targets or natural features.

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.

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...

Development and validation of methods for man-made machine interface evaluation. [for shuttles and shuttle payloads

NASA Technical Reports Server (NTRS)

Malone, T. B.; Micocci, A.

1975-01-01

The alternate methods of conducting a man-machine interface evaluation are classified as static and dynamic, and are evaluated. A dynamic evaluation tool is presented to provide for a determination of the effectiveness of the man-machine interface in terms of the sequence of operations (task and task sequences) and in terms of the physical characteristics of the interface. This dynamic checklist approach is recommended for shuttle and shuttle payload man-machine interface evaluations based on reduced preparation time, reduced data, and increased sensitivity of critical problems.

Multi-exposure high dynamic range image synthesis with camera shake correction

NASA Astrophysics Data System (ADS)

Li, Xudong; Chen, Yongfu; Jiang, Hongzhi; Zhao, Huijie

2017-10-01

Machine vision plays an important part in industrial online inspection. Owing to the nonuniform illuminance conditions and variable working distances, the captured image tends to be over-exposed or under-exposed. As a result, when processing the image such as crack inspection, the algorithm complexity and computing time increase. Multiexposure high dynamic range (HDR) image synthesis is used to improve the quality of the captured image, whose dynamic range is limited. Inevitably, camera shake will result in ghost effect, which blurs the synthesis image to some extent. However, existed exposure fusion algorithms assume that the input images are either perfectly aligned or captured in the same scene. These assumptions limit the application. At present, widely used registration based on Scale Invariant Feature Transform (SIFT) is usually time consuming. In order to rapidly obtain a high quality HDR image without ghost effect, we come up with an efficient Low Dynamic Range (LDR) images capturing approach and propose a registration method based on ORiented Brief (ORB) and histogram equalization which can eliminate the illumination differences between the LDR images. The fusion is performed after alignment. The experiment results demonstrate that the proposed method is robust to illumination changes and local geometric distortion. Comparing with other exposure fusion methods, our method is more efficient and can produce HDR images without ghost effect by registering and fusing four multi-exposure images.

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®

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.

MassMutual Partners with EP for a Dynamic Double Play

ERIC Educational Resources Information Center

Exceptional Parent, 2008

2008-01-01

In 2002 "Exceptional Parent" (EP) magazine had a vision--a vision of a dynamic, community outreach program that would raise the public's awareness about the special needs community. This program, now known as Disability Awareness Night, or DAN, would enlighten the public by calling attention to the dedication, perseverance, and the extraordinary…

Application of dynamic milling in stainless steel processing

NASA Astrophysics Data System (ADS)

Shan, Wenju

2017-09-01

This paper mainly introduces the method of parameter setting for NC programming of stainless steel parts by dynamic milling. Stainless steel is of high plasticity and toughness, serious hard working, large cutting force, high temperature in cutting area and easy wear of tool. It is difficult to process material. Dynamic motion technology is the newest NC programming technology of Mastercam software. It is an advanced machining idea. The tool path generated by the dynamic motion technology is more smooth, more efficient and more stable in the machining process. Dynamic motion technology is very suitable for cutting hard machining materials.

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.

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.

Robot Acting on Moving Bodies (RAMBO): Interaction with tumbling objects

NASA Technical Reports Server (NTRS)

Davis, Larry S.; Dementhon, Daniel; Bestul, Thor; Ziavras, Sotirios; Srinivasan, H. V.; Siddalingaiah, Madhu; Harwood, David

1989-01-01

Interaction with tumbling objects will become more common as human activities in space expand. Attempting to interact with a large complex object translating and rotating in space, a human operator using only his visual and mental capacities may not be able to estimate the object motion, plan actions or control those actions. A robot system (RAMBO) equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a tumbling object, is being developed. RAMBO is given a complete geometric model of the object. A low level vision module extracts and groups characteristic features in images of the object. The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained. This motion estimate is used to plan trajectories of the robot tool to relative locations rearby the object sufficient for achieving the tasks. More specifically, low level vision uses parallel algorithms for image enhancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering. The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features. To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection. This allows use of 2-D lookup tables at each stage of the decomposition. The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel. Trajectory planning combines heuristic and dynamic programming techniques. Then trajectories are created using dynamic interpolations between initial and goal trajectories. All the parallel algorithms run on a Connection Machine CM-2 with 16K processors.

LED lighting for use in multispectral and hyperspectral imaging

USDA-ARS?s Scientific Manuscript database

Lighting for machine vision and hyperspectral imaging is an important component for collecting high quality imagery. However, it is often given minimal consideration in the overall design of an imaging system. Tungsten-halogens lamps are the most common source of illumination for broad spectrum appl...

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...

The role of vision processing in prosthetic vision.

PubMed

Barnes, Nick; He, Xuming; McCarthy, Chris; Horne, Lachlan; Kim, Junae; Scott, Adele; Lieby, Paulette

2012-01-01

Prosthetic vision provides vision which is reduced in resolution and dynamic range compared to normal human vision. This comes about both due to residual damage to the visual system from the condition that caused vision loss, and due to limitations of current technology. However, even with limitations, prosthetic vision may still be able to support functional performance which is sufficient for tasks which are key to restoring independent living and quality of life. Here vision processing can play a key role, ensuring that information which is critical to the performance of key tasks is available within the capability of the available prosthetic vision. In this paper, we frame vision processing for prosthetic vision, highlight some key areas which present problems in terms of quality of life, and present examples where vision processing can help achieve better outcomes.

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%).

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

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

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

Method and apparatus for characterizing and enhancing the functional performance of machine tools

DOEpatents

Barkman, William E; Babelay, Jr., Edwin F; Smith, Kevin Scott; Assaid, Thomas S; McFarland, Justin T; Tursky, David A; Woody, Bethany; Adams, David

2013-04-30

Disclosed are various systems and methods for assessing and improving the capability of a machine tool. The disclosure applies to machine tools having at least one slide configured to move along a motion axis. Various patterns of dynamic excitation commands are employed to drive the one or more slides, typically involving repetitive short distance displacements. A quantification of a measurable merit of machine tool response to the one or more patterns of dynamic excitation commands is typically derived for the machine tool. Examples of measurable merits of machine tool performance include workpiece surface finish, and the ability to generate chips of the desired length.

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.

Investigation of Dynamic Force/Vibration Transmission Characteristics of Four-Square Type Gear Durability Test Machines

NASA Technical Reports Server (NTRS)

Kahraman, Ahmet

2002-01-01

In this study, design requirements for a dynamically viable, four-square type gear test machine are investigated. Variations of four-square type gear test machines have been in use for durability and dynamics testing of both parallel- and cross-axis gear set. The basic layout of these machines is illustrated. The test rig is formed by two gear pairs, of the same reduction ratio, a test gear pair and a reaction gear pair, connected to each other through shafts of certain torsional flexibility to form an efficient, closed-loop system. A desired level of constant torque is input to the circuit through mechanical (a split coupling with a torque arm) or hydraulic (a hydraulic actuator) means. The system is then driven at any desired speed by a small DC motor. The main task in hand is the isolation of the test gear pair from the reaction gear pair under dynamic conditions. Any disturbances originated at the reaction gear mesh might potentially travel to the test gearbox, altering the dynamic loading conditions of the test gear mesh, and hence, influencing the outcome of the durability or dynamics test. Therefore, a proper design of connecting structures becomes a major priority. Also, equally important is the issue of how close the operating speed of the machine is to the resonant frequencies of the gear meshes. This study focuses on a detailed analysis of the current NASA Glenn Research Center gear pitting test machine for evaluation of its resonance and vibration isolation characteristics. A number of these machines as the one illustrated has been used over last 30 years to establish an extensive database regarding the influence of the gear materials, processes surface treatments and lubricants on gear durability. This study is intended to guide an optimum design of next generation test machines for the most desirable dynamic characteristics.

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.

Dynamic Vision for Control

DTIC Science & Technology

2006-07-27

unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT The goal of this project was to develop analytical and computational tools to make vision a Viable sensor for...vision.ucla. edu July 27, 2006 Abstract The goal of this project was to develop analytical and computational tools to make vision a viable sensor for the ... sensors . We have proposed the framework of stereoscopic segmentation where multiple images of the same obejcts were jointly processed to extract geometry

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.

Learning Physics-based Models in Hydrology under the Framework of Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

Karpatne, A.; Kumar, V.

2017-12-01

Generative adversarial networks (GANs), that have been highly successful in a number of applications involving large volumes of labeled and unlabeled data such as computer vision, offer huge potential for modeling the dynamics of physical processes that have been traditionally studied using simulations of physics-based models. While conventional physics-based models use labeled samples of input/output variables for model calibration (estimating the right parametric forms of relationships between variables) or data assimilation (identifying the most likely sequence of system states in dynamical systems), there is a greater opportunity to explore the full power of machine learning (ML) methods (e.g, GANs) for studying physical processes currently suffering from large knowledge gaps, e.g. ground-water flow. However, success in this endeavor requires a principled way of combining the strengths of ML methods with physics-based numerical models that are founded on a wealth of scientific knowledge. This is especially important in scientific domains like hydrology where the number of data samples is small (relative to Internet-scale applications such as image recognition where machine learning methods has found great success), and the physical relationships are complex (high-dimensional) and non-stationary. We will present a series of methods for guiding the learning of GANs using physics-based models, e.g., by using the outputs of physics-based models as input data to the generator-learner framework, and by using physics-based models as generators trained using validation data in the adversarial learning framework. These methods are being developed under the broad paradigm of theory-guided data science that we are developing to integrate scientific knowledge with data science methods for accelerating scientific discovery.

Topology of Functional Connectivity and Hub Dynamics in the Beta Band As Temporal Prior for Natural Vision in the Human Brain.

PubMed

Betti, Viviana; Corbetta, Maurizio; de Pasquale, Francesco; Wens, Vincent; Della Penna, Stefania

2018-04-11

Networks hubs represent points of convergence for the integration of information across many different nodes and systems. Although a great deal is known on the topology of hub regions in the human brain, little is known about their temporal dynamics. Here, we examine the static and dynamic centrality of hub regions when measured in the absence of a task (rest) or during the observation of natural or synthetic visual stimuli. We used Magnetoencephalography (MEG) in humans (both sexes) to measure static and transient regional and network-level interaction in α- and β-band limited power (BLP) in three conditions: visual fixation (rest), viewing of movie clips (natural vision), and time-scrambled versions of the same clips (scrambled vision). Compared with rest, we observed in both movie conditions a robust decrement of α-BLP connectivity. Moreover, both movie conditions caused a significant reorganization of connections in the α band, especially between networks. In contrast, β-BLP connectivity was remarkably similar between rest and natural vision. Not only the topology did not change, but the joint dynamics of hubs in a core network during natural vision was predicted by similar fluctuations in the resting state. We interpret these findings by suggesting that slow-varying fluctuations of integration occurring in higher-order regions in the β band may be a mechanism to anticipate and predict slow-varying temporal patterns of the visual environment. SIGNIFICANCE STATEMENT A fundamental question in neuroscience concerns the function of spontaneous brain connectivity. Here, we tested the hypothesis that topology of intrinsic brain connectivity and its dynamics might predict those observed during natural vision. Using MEG, we tracked the static and time-varying brain functional connectivity when observers were either fixating or watching different movie clips. The spatial distribution of connections and the dynamics of centrality of a set of regions were similar during rest and movie in the β band, but not in the α band. These results support the hypothesis that the intrinsic β-rhythm integration occurs with a similar temporal structure during natural vision, possibly providing advanced information about incoming stimuli. Copyright © 2018 the authors 0270-6474/18/383858-14$15.00/0.

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.

Calculus: The Dynamics of Change. MAA Notes Number 39.

ERIC Educational Resources Information Center

Roberts, A. Wayne, Ed.

This book discusses the calculus reform effort. The first essay captures the basic themes that should characterize a calculus course that is modern in its vision as well as its pedagogy and content. The next section contains essays on the vision of calculus reform: "Visions of Calculus" (Sharon Cutler Ross); "Nonalgebraic Approaches…

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.

Prototype Automated Equipment to Perform Poising and Beat Rate Operations on the M577 MTSQ Fuze.

DTIC Science & Technology

1978-09-30

Regulation Machine which sets the M577 Fuze Timer beat rate and the Automatic Poising Machine which J dynamically balances the Timer balance wheel...in trouble shooting., The Automatic Poising Machine Figure 3 which inspects and corrects the dynamic I balance of the Balance Wheel Assembly was...machine is intimately related to the fastening method of the wire to the Timer at one end and the Balance Wheel at the other, a review of the history

Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat.

PubMed

Zhou, Ji; Applegate, Christopher; Alonso, Albor Dobon; Reynolds, Daniel; Orford, Simon; Mackiewicz, Michal; Griffiths, Simon; Penfield, Steven; Pullen, Nick

2017-01-01

Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch image processing, multiple traits analyses, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic. Here, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different computing platforms. To facilitate diverse scientific communities, we provide three software versions, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and a well-commented interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat ( Triticum aestivum ) growth studies at the Norwich Research Park (NRP, UK). By quantifying a number of growth phenotypes over time, we have identified diverse plant growth patterns between different genotypes under several experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices (e.g. smartphones and digital cameras) and still produced reliable biological outputs, we therefore believe that our automated analysis workflow and customised computer vision based feature extraction software implementation can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, we believe that our software not only can contribute to biological research, but also demonstrates how to utilise existing open numeric and scientific libraries (e.g. Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, in a efficient and effective way. Leaf-GP is a sophisticated software application that provides three approaches to quantify growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and HPC, with open Python-based scientific libraries preinstalled. Our work presents the advancement of how to integrate computer vision, image analysis, machine learning and software engineering in plant phenomics software implementation. To serve the plant research community, our modulated source code, detailed comments, executables (.exe for Windows; .app for Mac), and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

Dynamical Systems and Motion Vision.

DTIC Science & Technology

1988-04-01

TASK Artificial Inteligence Laboratory AREA I WORK UNIT NUMBERS 545 Technology Square . Cambridge, MA 02139 C\\ II. CONTROLLING OFFICE NAME ANO0 ADDRESS...INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY A.I.Memo No. 1037 April, 1988 Dynamical Systems and Motion Vision Joachim Heel Abstract: In this... Artificial Intelligence L3 Laboratory of the Massachusetts Institute of Technology. Support for the Laboratory’s [1 Artificial Intelligence Research is

Computational dynamics of soft machines

NASA Astrophysics Data System (ADS)

Hu, Haiyan; Tian, Qiang; Liu, Cheng

2017-06-01

Soft machine refers to a kind of mechanical system made of soft materials to complete sophisticated missions, such as handling a fragile object and crawling along a narrow tunnel corner, under low cost control and actuation. Hence, soft machines have raised great challenges to computational dynamics. In this review article, recent studies of the authors on the dynamic modeling, numerical simulation, and experimental validation of soft machines are summarized in the framework of multibody system dynamics. The dynamic modeling approaches are presented first for the geometric nonlinearities of coupled overall motions and large deformations of a soft component, the physical nonlinearities of a soft component made of hyperelastic or elastoplastic materials, and the frictional contacts/impacts of soft components, respectively. Then the computation approach is outlined for the dynamic simulation of soft machines governed by a set of differential-algebraic equations of very high dimensions, with an emphasis on the efficient computations of the nonlinear elastic force vector of finite elements. The validations of the proposed approaches are given via three case studies, including the locomotion of a soft quadrupedal robot, the spinning deployment of a solar sail of a spacecraft, and the deployment of a mesh reflector of a satellite antenna, as well as the corresponding experimental studies. Finally, some remarks are made for future studies.

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 Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

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.

Multi-camera and structured-light vision system (MSVS) for dynamic high-accuracy 3D measurements of railway tunnels.

PubMed

Zhan, Dong; Yu, Long; Xiao, Jian; Chen, Tanglong

2015-04-14

Railway tunnel 3D clearance inspection is critical to guaranteeing railway operation safety. However, it is a challenge to inspect railway tunnel 3D clearance using a vision system, because both the spatial range and field of view (FOV) of such measurements are quite large. This paper summarizes our work on dynamic railway tunnel 3D clearance inspection based on a multi-camera and structured-light vision system (MSVS). First, the configuration of the MSVS is described. Then, the global calibration for the MSVS is discussed in detail. The onboard vision system is mounted on a dedicated vehicle and is expected to suffer from multiple degrees of freedom vibrations brought about by the running vehicle. Any small vibration can result in substantial measurement errors. In order to overcome this problem, a vehicle motion deviation rectifying method is investigated. Experiments using the vision inspection system are conducted with satisfactory online measurement results.

Drosophila learn efficient paths to a food source.

PubMed

Navawongse, Rapeechai; Choudhury, Deepak; Raczkowska, Marlena; Stewart, James Charles; Lim, Terrence; Rahman, Mashiur; Toh, Alicia Guek Geok; Wang, Zhiping; Claridge-Chang, Adam

2016-05-01

Elucidating the genetic, and neuronal bases for learned behavior is a central problem in neuroscience. A leading system for neurogenetic discovery is the vinegar fly Drosophila melanogaster; fly memory research has identified genes and circuits that mediate aversive and appetitive learning. However, methods to study adaptive food-seeking behavior in this animal have lagged decades behind rodent feeding analysis, largely due to the challenges presented by their small scale. There is currently no method to dynamically control flies' access to food. In rodents, protocols that use dynamic food delivery are a central element of experimental paradigms that date back to the influential work of Skinner. This method is still commonly used in the analysis of learning, memory, addiction, feeding, and many other subjects in experimental psychology. The difficulty of microscale food delivery means this is not a technique used in fly behavior. In the present manuscript we describe a microfluidic chip integrated with machine vision and automation to dynamically control defined liquid food presentations and sensory stimuli. Strikingly, repeated presentations of food at a fixed location produced improvements in path efficiency during food approach. This shows that improved path choice is a learned behavior. Active control of food availability using this microfluidic system is a valuable addition to the methods currently available for the analysis of learned feeding behavior in flies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

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

Illumination-based synchronization of high-speed vision sensors.

PubMed

Hou, Lei; Kagami, Shingo; Hashimoto, Koichi

2010-01-01

To acquire images of dynamic scenes from multiple points of view simultaneously, the acquisition time of vision sensors should be synchronized. This paper describes an illumination-based synchronization method derived from the phase-locked loop (PLL) algorithm. Incident light to a vision sensor from an intensity-modulated illumination source serves as the reference signal for synchronization. Analog and digital computation within the vision sensor forms a PLL to regulate the output signal, which corresponds to the vision frame timing, to be synchronized with the reference. Simulated and experimental results show that a 1,000 Hz frame rate vision sensor was successfully synchronized with 32 μs jitters.

Financial heat machine

NASA Astrophysics Data System (ADS)

Khrennikov, Andrei

2005-05-01

We consider dynamics of financial markets as dynamics of expectations and discuss such a dynamics from the point of view of phenomenological thermodynamics. We describe a financial Carnot cycle and the financial analog of a heat machine. We see, that while in physics a perpetuum mobile is absolutely impossible, in economics such mobile may exist under some conditions.

Modeling and analysis of dynamic characteristics of carrier system of machining center in MSC.Adams

NASA Astrophysics Data System (ADS)

Grinek, A. V.; Rybina, A. V.; Boychuk, I. P.; Dantsevich, I. M.; Hurtasenko, A. V.

2018-03-01

The simulation model with the help of vibration analysis was developed in MSC.Adams/Vibration and experimental research of the dynamic characteristics of a five-axis machining center was carried out. The amplitude-frequency characteristics, resonant frequencies in various directions are investigated. Dynamic and static rigidity, damping intensity and the coefficient of dynamism of the center are determined.

Advances in molecular dynamics simulation of ultra-precision machining of hard and brittle materials

NASA Astrophysics Data System (ADS)

Guo, Xiaoguang; Li, Qiang; Liu, Tao; Kang, Renke; Jin, Zhuji; Guo, Dongming

2017-03-01

Hard and brittle materials, such as silicon, SiC, and optical glasses, are widely used in aerospace, military, integrated circuit, and other fields because of their excellent physical and chemical properties. However, these materials display poor machinability because of their hard and brittle properties. Damages such as surface micro-crack and subsurface damage often occur during machining of hard and brittle materials. Ultra-precision machining is widely used in processing hard and brittle materials to obtain nanoscale machining quality. However, the theoretical mechanism underlying this method remains unclear. This paper provides a review of present research on the molecular dynamics simulation of ultra-precision machining of hard and brittle materials. The future trends in this field are also discussed.

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Chong; Wang, Lun; Liao, T. Warren

2015-10-01

Currently, chatter has become the critical factor in hindering machining quality and productivity in machining processes. To avoid cutting chatter, a new method based on dynamic cutting force simulation model and support vector machine (SVM) is presented for the prediction of chatter stability lobes. The cutting force is selected as the monitoring signal, and the wavelet energy entropy theory is used to extract the feature vectors. A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data. Then combining with the dynamic cutting force simulation model, the stability lobes diagram (SLD) can be estimated. Finally, the predicted results are compared with existing methods such as zero-order analytical (ZOA) and semi-discretization (SD) method as well as actual cutting experimental results to confirm the validity of this new method.

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.

Molecular Dynamics Modeling and Simulation of Diamond Cutting of Cerium.

PubMed

Zhang, Junjie; Zheng, Haibing; Shuai, Maobing; Li, Yao; Yang, Yang; Sun, Tao

2017-12-01

The coupling between structural phase transformations and dislocations induces challenges in understanding the deformation behavior of metallic cerium at the nanoscale. In the present work, we elucidate the underlying mechanism of cerium under ultra-precision diamond cutting by means of molecular dynamics modeling and simulations. The molecular dynamics model of diamond cutting of cerium is established by assigning empirical potentials to describe atomic interactions and evaluating properties of two face-centered cubic cerium phases. Subsequent molecular dynamics simulations reveal that dislocation slip dominates the plastic deformation of cerium under the cutting process. In addition, the analysis based on atomic radial distribution functions demonstrates that there are trivial phase transformations from the γ-Ce to the δ-Ce occurred in both machined surface and formed chip. Following investigations on machining parameter dependence reveal the optimal machining conditions for achieving high quality of machined surface of cerium.

Molecular Dynamics Modeling and Simulation of Diamond Cutting of Cerium

NASA Astrophysics Data System (ADS)

Zhang, Junjie; Zheng, Haibing; Shuai, Maobing; Li, Yao; Yang, Yang; Sun, Tao

2017-07-01

The coupling between structural phase transformations and dislocations induces challenges in understanding the deformation behavior of metallic cerium at the nanoscale. In the present work, we elucidate the underlying mechanism of cerium under ultra-precision diamond cutting by means of molecular dynamics modeling and simulations. The molecular dynamics model of diamond cutting of cerium is established by assigning empirical potentials to describe atomic interactions and evaluating properties of two face-centered cubic cerium phases. Subsequent molecular dynamics simulations reveal that dislocation slip dominates the plastic deformation of cerium under the cutting process. In addition, the analysis based on atomic radial distribution functions demonstrates that there are trivial phase transformations from the γ-Ce to the δ-Ce occurred in both machined surface and formed chip. Following investigations on machining parameter dependence reveal the optimal machining conditions for achieving high quality of machined surface of cerium.

Taming Crowded Visual Scenes

DTIC Science & Technology

2014-08-12

Nolan Warner, Mubarak Shah. Tracking in Dense Crowds Using Prominenceand Neighborhood Motion Concurrence, IEEE Transactions on Pattern Analysis...of  computer  vision,   computer   graphics  and  evacuation  dynamics  by  providing  a  common  platform,  and  provides...areas  that  includes  Computer  Vision,  Computer   Graphics ,  and  Pedestrian   Evacuation  Dynamics.  Despite  the

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.

The Design, Synthesis, and Study of Solid-State Molecular Rotors: Structure/Function Relationships for Condensed-Phase Anisotropic Dynamics

NASA Astrophysics Data System (ADS)

Vogelsberg, Cortnie Sue

Amphidynamic crystals are an extremely promising platform for the development of artificial molecular machines and stimuli-responsive materials. In analogy to skeletal muscle, their function will rely upon the collective operation of many densely packed molecular machines (i.e. actin-bound myosin) that are self-assembled in a highly organized anisotropic medium. By choosing lattice-forming elements and moving "parts" with specific functionalities, individual molecular machines may be synthesized and self-assembled in order to carry out desirable functions. In recent years, efforts in the design of amphidynamic materials based on molecular gyroscopes and compasses have shown that a certain amount of free volume is essential to facilitate internal rotation and reorientation within a crystal. In order to further establish structure/function relationships to advance the development of increasingly complex molecular machinery, molecular rotors and a molecular "spinning" top were synthesized and incorporated into a variety of solid-state architectures with different degrees of periodicity, dimensionality, and free volume. Specifically, lamellar molecular crystals, hierarchically ordered periodic mesoporous organosilicas, and metal-organic frameworks were targeted for the development of solid-state molecular machines. Using an array of solid-state nuclear magnetic resonance spectroscopy techniques, the dynamic properties of these novel molecular machine assemblies were determined and correlated with their corresponding structural features. It was found that architecture type has a profound influence on functional dynamics. The study of layered molecular crystals, composed of either molecular rotors or "spinning" tops, probed functional dynamics within dense, highly organized environments. From their study, it was discovered that: 1) crystallographically distinct sites may be utilized to differentiate machine function, 2) halogen bonding interactions are sufficiently strong to direct an assembly of molecular machines, 3) the relative flexibility of the crystal environment proximate to a dynamic component may have a significant effect on its function, and, 4) molecular machines, which possess both solid-state photochemical reactivity and dynamics may show complex reaction kinetics if the correlation time of the dynamic process and the lifetime of the excited state occur on the same time scale and the dynamic moiety inherently participates as a reaction intermediate. The study of periodic mesoporous organosilica with hierarchical order probed molecular dynamics within 2D layers of molecular rotors, organized in only one dimension and with ca. 50% exposed to the mesopore free volume. From their study, it was discovered that: 1) molecular rotors, which comprise the layers of the mesopore walls, form a 2D rotational glass, 2) rotator dynamics within the 2D rotational glass undergo a transition to a 2D rotational fluid, and, 3) a 2D rotational glass transition may be exploited to develop hyper-sensitive thermally activated molecular machines. The study of a metal-organic framework assembled from molecular rotors probed dynamics in a periodic three-dimensional free-volume environment, without the presence of close contacts. From the study of this solid-state material, it was determined that: 1) the intrinsic electronic barrier is one of the few factors, which may affect functional dynamics in a true free-volume environment, and, 2) molecular machines with dynamic barriers <

A computer simulation experiment of supervisory control of remote manipulation. M.S. Thesis

NASA Technical Reports Server (NTRS)

Mccandlish, S. G.

1966-01-01

A computer simulation of a remote manipulation task and a rate-controlled manipulator is described. Some low-level automatic decision making ability which could be used at the operator's discretion to augment his direct continuous control was built into the manipulator. Experiments were made on the effect of transmission delay, dynamic lag, and intermittent vision on human manipulative ability. Delay does not make remote manipulation impossible. Intermittent visual feedback, and the absence of rate information in the display presented to the operator do not seem to impair the operator's performance. A small-capacity visual feedback channel may be sufficient for remote manipulation tasks, or one channel might be time-shared between several operators. In other experiments the operator called in sequence various on-site automatic control programs of the machine, and thereby acted as a supervisor. The supervisory mode of operation has some advantages when the task to be performed is difficult for a human controlling directly.

The NASA Program Management Tool: A New Vision in Business Intelligence

NASA Technical Reports Server (NTRS)

Maluf, David A.; Swanson, Keith; Putz, Peter; Bell, David G.; Gawdiak, Yuri

2006-01-01

This paper describes a novel approach to business intelligence and program management for large technology enterprises like the U.S. National Aeronautics and Space Administration (NASA). Two key distinctions of the approach are that 1) standard business documents are the user interface, and 2) a "schema-less" XML database enables flexible integration of technology information for use by both humans and machines in a highly dynamic environment. The implementation utilizes patent-pending NASA software called the NASA Program Management Tool (PMT) and its underlying "schema-less" XML database called Netmark. Initial benefits of PMT include elimination of discrepancies between business documents that use the same information and "paperwork reduction" for program and project management in the form of reducing the effort required to understand standard reporting requirements and to comply with those reporting requirements. We project that the underlying approach to business intelligence will enable significant benefits in the timeliness, integrity and depth of business information available to decision makers on all organizational levels.

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.

Dynamic Resource Allocation and Access Class Barring Scheme for Delay-Sensitive Devices in Machine to Machine (M2M) Communications.

PubMed

Li, Ning; Cao, Chao; Wang, Cong

2017-06-15

Supporting simultaneous access of machine-type devices is a critical challenge in machine-to-machine (M2M) communications. In this paper, we propose an optimal scheme to dynamically adjust the Access Class Barring (ACB) factor and the number of random access channel (RACH) resources for clustered machine-to-machine (M2M) communications, in which Delay-Sensitive (DS) devices coexist with Delay-Tolerant (DT) ones. In M2M communications, since delay-sensitive devices share random access resources with delay-tolerant devices, reducing the resources consumed by delay-sensitive devices means that there will be more resources available to delay-tolerant ones. Our goal is to optimize the random access scheme, which can not only satisfy the requirements of delay-sensitive devices, but also take the communication quality of delay-tolerant ones into consideration. We discuss this problem from the perspective of delay-sensitive services by adjusting the resource allocation and ACB scheme for these devices dynamically. Simulation results show that our proposed scheme realizes good performance in satisfying the delay-sensitive services as well as increasing the utilization rate of the random access resources allocated to them.

Progress on Fault Mechanisms for Gear Transmissions in Coal Cutting Machines: From Macro to Nano Models.

PubMed

Jiang, Yu; Zhang, Xiaogang; Zhang, Chao; Li, Zhixiong; Sheng, Chenxing

2017-04-01

Numerical modeling has been recognized as the dispensable tools for mechanical fault mechanism analysis. Techniques, ranging from macro to nano levels, include the finite element modeling boundary element modeling, modular dynamic modeling, nano dynamic modeling and so forth. This work firstly reviewed the progress on the fault mechanism analysis for gear transmissions from the tribological and dynamic aspects. Literature review indicates that the tribological and dynamic properties were separately investigated to explore the fault mechanism in gear transmissions. However, very limited work has been done to address the links between the tribological and dynamic properties and scarce researches have been done for coal cutting machines. For this reason, the tribo-dynamic coupled model was introduced to bridge the gap between the tribological and dynamic models in fault mechanism analysis for gear transmissions in coal cutting machines. The modular dynamic modeling and nano dynamic modeling techniques are expected to establish the links between the tribological and dynamic models. Possible future research directions using the tribo dynamic coupled model were summarized to provide potential references for researchers in the field.

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...

Back in Time

NASA Technical Reports Server (NTRS)

1998-01-01

Under a Jet Propulsion Laboratory SBIR (Small Business Innovative Research), Cambridge Research and Instrumentation Inc., developed a new class of filters for the construction of small, low-cost multispectral imagers. The VariSpec liquid crystal enables users to obtain multi-spectral, ultra-high resolution images using a monochrome CCD (charge coupled device) camera. Application areas include biomedical imaging, remote sensing, and machine vision.

Image-plane processing of visual information

NASA Technical Reports Server (NTRS)

Huck, F. O.; Fales, C. L.; Park, S. K.; Samms, R. W.

1984-01-01

Shannon's theory of information is used to optimize the optical design of sensor-array imaging systems which use neighborhood image-plane signal processing for enhancing edges and compressing dynamic range during image formation. The resultant edge-enhancement, or band-pass-filter, response is found to be very similar to that of human vision. Comparisons of traits in human vision with results from information theory suggest that: (1) Image-plane processing, like preprocessing in human vision, can improve visual information acquisition for pattern recognition when resolving power, sensitivity, and dynamic range are constrained. Improvements include reduced sensitivity to changes in lighter levels, reduced signal dynamic range, reduced data transmission and processing, and reduced aliasing and photosensor noise degradation. (2) Information content can be an appropriate figure of merit for optimizing the optical design of imaging systems when visual information is acquired for pattern recognition. The design trade-offs involve spatial response, sensitivity, and sampling interval.

Stroke dynamics and frequency of 3 phacoemulsification machines.

PubMed

Tognetto, Daniele; Cecchini, Paolo; Leon, Pia; Di Nicola, Marta; Ravalico, Giuseppe

2012-02-01

To measure the working frequency and the stroke dynamics of the phaco tip of 3 phacoemulsification machines. University Eye Clinic of Trieste, Italy. Experimental study. A video wet fixture was assembled to measure the working frequency using a micro camera and a micropulsed strobe-light system. A different video wet fixture was created to measure tip displacement as vectorial movement at different phaco powers using a microscopic video apparatus. The working frequency of the Infiniti Ozil machine was 43.0 kHz in longitudinal mode and 31.6 kHz in torsional mode. The frequency of the Whitestar Signature machine was 29.0 kHz in longitudinal mode and 38.0 kHz with the Ellips FX handpiece. The Stellaris machine had a frequency of 28.8 kHz. The longitudinal stroke of the 3 machines at different phaco powers was statistically significantly different. The Stellaris machine had the highest stroke extent (139 μm). The lateral movement of the Infiniti Ozil and Whitestar Signature machines differed significantly. No movement on the y-axis was observed for the Infiniti Ozil machine in torsional mode. The elliptical path of the Ellips FX handpiece had different x and y components at different phaco powers. The 3 phaco machines performed differently in terms of working frequency and stroke dynamics. The knowledge of the peculiar lateral and elliptical path strokes of Infiniti and Whitestar Signature machines may allow the surgeon to fully use these features for lens removal. Copyright © 2012 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

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.

A system framework of inter-enterprise machining quality control based on fractal theory

NASA Astrophysics Data System (ADS)

Zhao, Liping; Qin, Yongtao; Yao, Yiyong; Yan, Peng

2014-03-01

In order to meet the quality control requirement of dynamic and complicated product machining processes among enterprises, a system framework of inter-enterprise machining quality control based on fractal was proposed. In this system framework, the fractal-specific characteristic of inter-enterprise machining quality control function was analysed, and the model of inter-enterprise machining quality control was constructed by the nature of fractal structures. Furthermore, the goal-driven strategy of inter-enterprise quality control and the dynamic organisation strategy of inter-enterprise quality improvement were constructed by the characteristic analysis on this model. In addition, the architecture of inter-enterprise machining quality control based on fractal was established by means of Web service. Finally, a case study for application was presented. The result showed that the proposed method was available, and could provide guidance for quality control and support for product reliability in inter-enterprise machining processes.

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.

Robot acting on moving bodies (RAMBO): Preliminary results

NASA Technical Reports Server (NTRS)

Davis, Larry S.; Dementhon, Daniel; Bestul, Thor; Ziavras, Sotirios; Srinivasan, H. V.; Siddalingaiah, Madju; Harwood, David

1989-01-01

A robot system called RAMBO is being developed. It is equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a moving object. RAMBO is given a complete geometric model of the object. A low level vision module extracts and groups characteristic features in images of the object. The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained. This motion estimate is used to plan trajectories of the robot tool to relative locations nearby the object sufficient for achieving the tasks. More specifically, low level vision uses parallel algorithms for image enchancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering. The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features. To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection. This allows the use of 2-D lookup tables at each stage of the decomposition. The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel. Trajectory planning combines heuristic and dynamic programming techniques. Then trajectories are created using parametric cubic splines between initial and goal trajectories. All the parallel algorithms run on a Connection Machine CM-2 with 16K processors.

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.;

Dynamic modeling of brushless dc motors for aerospace actuation

NASA Technical Reports Server (NTRS)

Demerdash, N. A.; Nehl, T. W.

1980-01-01

A discrete time model for simulation of the dynamics of samarium cobalt-type permanent magnet brushless dc machines is presented. The simulation model includes modeling of the interaction between these machines and their attached power conditioners. These are transistorized conditioner units. This model is part of an overall discrete-time analysis of the dynamic performance of electromechanical actuators, which was conducted as part of prototype development of such actuators studied and built for NASA-Johnson Space Center as a prospective alternative to hydraulic actuators presently used in shuttle orbiter applications. The resulting numerical simulations of the various machine and power conditioner current and voltage waveforms gave excellent correlation to the actual waveforms collected from actual hardware experimental testing. These results, numerical and experimental, are presented here for machine motoring, regeneration and dynamic braking modes. Application of the resulting model to the determination of machine current and torque profiles during closed-loop actuator operation were also analyzed and the results are given here. These results are given in light of an overall view of the actuator system components. The applicability of this method of analysis to design optimization and trouble-shooting in such prototype development is also discussed in light of the results at hand.

Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

DOE PAGES

Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; ...

2014-12-18

Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

Single Molecule Cluster Analysis Identifies Signature Dynamic Conformations along the Splicing Pathway

PubMed Central

Blanco, Mario R.; Martin, Joshua S.; Kahlscheuer, Matthew L.; Krishnan, Ramya; Abelson, John; Laederach, Alain; Walter, Nils G.

2016-01-01

The spliceosome is the dynamic RNA-protein machine responsible for faithfully splicing introns from precursor messenger RNAs (pre-mRNAs). Many of the dynamic processes required for the proper assembly, catalytic activation, and disassembly of the spliceosome as it acts on its pre-mRNA substrate remain poorly understood, a challenge that persists for many biomolecular machines. Here, we developed a fluorescence-based Single Molecule Cluster Analysis (SiMCAn) tool to dissect the manifold conformational dynamics of a pre-mRNA through the splicing cycle. By clustering common dynamic behaviors derived from selectively blocked splicing reactions, SiMCAn was able to identify signature conformations and dynamic behaviors of multiple ATP-dependent intermediates. In addition, it identified a conformation adopted late in splicing by a 3′ splice site mutant, invoking a mechanism for substrate proofreading. SiMCAn presents a novel framework for interpreting complex single molecule behaviors that should prove widely useful for the comprehensive analysis of a plethora of dynamic cellular machines. PMID:26414013

Robotic space simulation integration of vision algorithms into an orbital operations simulation

NASA Technical Reports Server (NTRS)

Bochsler, Daniel C.

1987-01-01

In order to successfully plan and analyze future space activities, computer-based simulations of activities in low earth orbit will be required to model and integrate vision and robotic operations with vehicle dynamics and proximity operations procedures. The orbital operations simulation (OOS) is configured and enhanced as a testbed for robotic space operations. Vision integration algorithms are being developed in three areas: preprocessing, recognition, and attitude/attitude rates. The vision program (Rice University) was modified for use in the OOS. Systems integration testing is now in progress.

State machine analysis of sensor data from dynamic processes

DOEpatents

Cook, William R.; Brabson, John M.; Deland, Sharon M.

2003-12-23

A state machine model analyzes sensor data from dynamic processes at a facility to identify the actual processes that were performed at the facility during a period of interest for the purpose of remote facility inspection. An inspector can further input the expected operations into the state machine model and compare the expected, or declared, processes to the actual processes to identify undeclared processes at the facility. The state machine analysis enables the generation of knowledge about the state of the facility at all levels, from location of physical objects to complex operational concepts. Therefore, the state machine method and apparatus may benefit any agency or business with sensored facilities that stores or manipulates expensive, dangerous, or controlled materials or information.

Machine learning molecular dynamics for the simulation of infrared spectra.

PubMed

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

Dynamic behavior of a rolling housing

NASA Astrophysics Data System (ADS)

Gentile, A.; Messina, A. M.; Trentadue, Bartolo

1994-09-01

One of the major objectives of industry is to curtail costs. An element, among others, that enables to achieve such goal is the efficiency of the production cycle machines. Such efficiency lies in the reliability of the upkeeping operations. Among maintenance procedures, measuring and analyzing vibrations is a way to detect structure modifications over the machine's lifespan. Further, the availability of a mathematical model describing the influence of each individual part of the machine on the total dynamic behavior of the whole machine may help localizing breakdowns during diagnosis operations. The paper hereof illustrates an analytical-numerical model which can simulate the behavior of a rolling housing. The aforesaid mathematical model has been obtained by FEM techniques, the dynamic response by mode superposition and the synthesis of the vibration time sequence in the frequency versus by FFT numerical techniques.

Prediction of turning stability using receptance coupling

NASA Astrophysics Data System (ADS)

Jasiewicz, Marcin; Powałka, Bartosz

2018-01-01

This paper presents an issue of machining stability prediction of dynamic "lathe - workpiece" system evaluated using receptance coupling method. Dynamic properties of the lathe components (the spindle and the tailstock) are assumed to be constant and can be determined experimentally based on the results of the impact test. Hence, the variable of the system "machine tool - holder - workpiece" is the machined part, which can be easily modelled analytically. The method of receptance coupling enables a synthesis of experimental (spindle, tailstock) and analytical (machined part) models, so impact testing of the entire system becomes unnecessary. The paper presents methodology of analytical and experimental models synthesis, evaluation of the stability lobes and experimental validation procedure involving both the determination of the dynamic properties of the system and cutting tests. In the summary the experimental verification results would be presented and discussed.

Envisioning engineering education and practice in the coming intelligence convergence era — a complex adaptive systems approach

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.

2013-12-01

Some of the recent attempts for improving and transforming engineering education are reviewed. The attempts aim at providing the entry level engineers with the skills needed to address the challenges of future large-scale complex systems and projects. Some of the frontier sectors and future challenges for engineers are outlined. The major characteristics of the coming intelligence convergence era (the post-information age) are identified. These include the prevalence of smart devices and environments, the widespread applications of anticipatory computing and predictive / prescriptive analytics, as well as a symbiotic relationship between humans and machines. Devices and machines will be able to learn from, and with, humans in a natural collaborative way. The recent game changers in learnscapes (learning paradigms, technologies, platforms, spaces, and environments) that can significantly impact engineering education in the coming era are identified. Among these are open educational resources, knowledge-rich classrooms, immersive interactive 3D learning, augmented reality, reverse instruction / flipped classroom, gamification, robots in the classroom, and adaptive personalized learning. Significant transformative changes in, and mass customization of, learning are envisioned to emerge from the synergistic combination of the game changers and other technologies. The realization of the aforementioned vision requires the development of a new multidisciplinary framework of emergent engineering for relating innovation, complexity and cybernetics, within the future learning environments. The framework can be used to treat engineering education as a complex adaptive system, with dynamically interacting and communicating components (instructors, individual, small, and large groups of learners). The emergent behavior resulting from the interactions can produce progressively better, and continuously improving, learning environment. As a first step towards the realization of the vision, intelligent adaptive cyber-physical ecosystems need to be developed to facilitate collaboration between the various stakeholders of engineering education, and to accelerate the development of a skilled engineering workforce. The major components of the ecosystems include integrated knowledge discovery and exploitation facilities, blended learning and research spaces, novel ultra-intelligent software agents, multimodal and autonomous interfaces, and networked cognitive and tele-presence robots.

Preliminary Development of Real Time Usage-Phase Monitoring System for CNC Machine Tools with a Case Study on CNC Machine VMC 250

NASA Astrophysics Data System (ADS)

Budi Harja, Herman; Prakosa, Tri; Raharno, Sri; Yuwana Martawirya, Yatna; Nurhadi, Indra; Setyo Nogroho, Alamsyah

2018-03-01

The production characteristic of job-shop industry at which products have wide variety but small amounts causes every machine tool will be shared to conduct production process with dynamic load. Its dynamic condition operation directly affects machine tools component reliability. Hence, determination of maintenance schedule for every component should be calculated based on actual usage of machine tools component. This paper describes study on development of monitoring system to obtaining information about each CNC machine tool component usage in real time approached by component grouping based on its operation phase. A special device has been developed for monitoring machine tool component usage by utilizing usage phase activity data taken from certain electronics components within CNC machine. The components are adaptor, servo driver and spindle driver, as well as some additional components such as microcontroller and relays. The obtained data are utilized for detecting machine utilization phases such as power on state, machine ready state or spindle running state. Experimental result have shown that the developed CNC machine tool monitoring system is capable of obtaining phase information of machine tool usage as well as its duration and displays the information at the user interface application.

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.

Mining key elements for severe convection prediction based on CNN

NASA Astrophysics Data System (ADS)

Liu, Ming; Pan, Ning; Zhang, Changan; Sha, Hongzhou; Zhang, Bolei; Liu, Liang; Zhang, Meng

2017-04-01

Severe convective weather is a kind of weather disasters accompanied by heavy rainfall, gust wind, hail, etc. Along with recent developments on remote sensing and numerical modeling, there are high-volume and long-term observational and modeling data accumulated to capture massive severe convective events over particular areas and time periods. With those high-volume and high-variety weather data, most of the existing studies and methods carry out the dynamical laws, cause analysis, potential rule study, and prediction enhancement by utilizing the governing equations from fluid dynamics and thermodynamics. In this study, a key-element mining method is proposed for severe convection prediction based on convolution neural network (CNN). It aims to identify the key areas and key elements from huge amounts of historical weather data including conventional measurements, weather radar, satellite, so as numerical modeling and/or reanalysis data. Under this manner, the machine-learning based method could help the human forecasters on their decision-making on operational weather forecasts on severe convective weathers by extracting key information from the real-time and historical weather big data. In this paper, it first utilizes computer vision technology to complete the data preprocessing work of the meteorological variables. Then, it utilizes the information such as radar map and expert knowledge to annotate all images automatically. And finally, by using CNN model, it cloud analyze and evaluate each weather elements (e.g., particular variables, patterns, features, etc.), and identify key areas of those critical weather elements, then help forecasters quickly screen out the key elements from huge amounts of observation data by current weather conditions. Based on the rich weather measurement and model data (up to 10 years) over Fujian province in China, where the severe convective weathers are very active during the summer months, experimental tests are conducted with the new machine-learning method via CNN models. Based on the analysis of those experimental results and case studies, the proposed new method have below benefits for the severe convection prediction: (1) helping forecasters to narrow down the scope of analysis and saves lead-time for those high-impact severe convection; (2) performing huge amount of weather big data by machine learning methods rather relying on traditional theory and knowledge, which provide new method to explore and quantify the severe convective weathers; (3) providing machine learning based end-to-end analysis and processing ability with considerable scalability on data volumes, and accomplishing the analysis work without human intervention.

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.

Consequences of nonclassical measurement for the algorithmic description of continuous dynamical systems

NASA Technical Reports Server (NTRS)

Fields, Chris

1989-01-01

Continuous dynamical systems intuitively seem capable of more complex behavior than discrete systems. If analyzed in the framework of the traditional theory of computation, a continuous dynamical system with countably many quasistable states has at least the computational power of a universal Turing machine. Such an analysis assumes, however, the classical notion of measurement. If measurement is viewed nonclassically, a continuous dynamical system cannot, even in principle, exhibit behavior that cannot be simulated by a universal Turing machine.

Consequences of nonclassical measurement for the algorithmic description of continuous dynamical systems

NASA Technical Reports Server (NTRS)

Fields, Chris

1989-01-01

Continuous dynamical systems intuitively seem capable of more complex behavior than discrete systems. If analyzed in the framework of the traditional theory of computation, a continuous dynamical system with countablely many quasistable states has at least the computational power of a universal Turing machine. Such an analyses assumes, however, the classical notion of measurement. If measurement is viewed nonclassically, a continuous dynamical system cannot, even in principle, exhibit behavior that cannot be simulated by a universal Turing machine.

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.

Dynamic Simulation Research on Chain Drive Mechanism of Corn Seeder Based on ADAMS

NASA Astrophysics Data System (ADS)

Wang, Y. B.; Jia, H. P.

2017-12-01

In order to reduce the damage to the chain and improve the seeding quality of the seeding machine, the corn seeder has the characteristics of the seeding quality and some technical indexes in the work of the corn seeding machine. The dynamic analysis of the chain drive mechanism is carried out by using the dynamic virtual prototype. In this paper, the speed of the corn planter is 5km/h, and the speed of the simulated knuckle is 0.1~0.9s. The velocity is 0.12m/s, which is equal to the chain speed when the seeder is running normally. Of the dynamic simulation of the movement and the actual situation is basically consistent with the apparent speed of the drive wheel has changed the acceleration and additional dynamic load, the chain drive has a very serious damage, and the maximum load value of 47.28N, in order to reduce the damage to the chain, As far as possible so that the sowing machine in the work to maintain a reasonable uniform speed, to avoid a greater acceleration, the corn sowing machine drive the design of a certain reference.

Feature Extraction and Machine Learning for the Classification of Brazilian Savannah Pollen Grains

PubMed Central

Souza, Junior Silva; da Silva, Gercina Gonçalves

2016-01-01

The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types that can be used to train and test computer vision based automatic pollen classifiers. A first baseline human and computer performance for this dataset has been established using 805 pollen images of 23 pollen types. In order to access the computer performance, a combination of three feature extractors and four machine learning techniques has been implemented, fine tuned and tested. The results of these tests are also presented in this paper. PMID:27276196

Crystal nucleation in metallic alloys using x-ray radiography and machine learning

PubMed Central

Arteta, Carlos; Lempitsky, Victor

2018-01-01

The crystallization of solidifying Al-Cu alloys over a wide range of conditions was studied in situ by synchrotron x-ray radiography, and the data were analyzed using a computer vision algorithm trained using machine learning. The effect of cooling rate and solute concentration on nucleation undercooling, crystal formation rate, and crystal growth rate was measured automatically for thousands of separate crystals, which was impossible to achieve manually. Nucleation undercooling distributions confirmed the efficiency of extrinsic grain refiners and gave support to the widely assumed free growth model of heterogeneous nucleation. We show that crystallization occurred in temporal and spatial bursts associated with a solute-suppressed nucleation zone. PMID:29662954

A large-scale solar dynamics observatory image dataset for computer vision applications.

PubMed

Kucuk, Ahmet; Banda, Juan M; Angryk, Rafal A

2017-01-01

The National Aeronautics Space Agency (NASA) Solar Dynamics Observatory (SDO) mission has given us unprecedented insight into the Sun's activity. By capturing approximately 70,000 images a day, this mission has created one of the richest and biggest repositories of solar image data available to mankind. With such massive amounts of information, researchers have been able to produce great advances in detecting solar events. In this resource, we compile SDO solar data into a single repository in order to provide the computer vision community with a standardized and curated large-scale dataset of several hundred thousand solar events found on high resolution solar images. This publicly available resource, along with the generation source code, will accelerate computer vision research on NASA's solar image data by reducing the amount of time spent performing data acquisition and curation from the multiple sources we have compiled. By improving the quality of the data with thorough curation, we anticipate a wider adoption and interest from the computer vision to the solar physics community.

Experimental research of kinetic and dynamic characteristics of temperature movements of machines

NASA Astrophysics Data System (ADS)

Parfenov, I. V.; Polyakov, A. N.

2018-03-01

Nowadays, the urgency of informational support of machines at different stages of their life cycle is increasing in the form of various experimental characteristics that determine the criteria for working capacity. The effectiveness of forming the base of experimental characteristics of machines is related directly to the duration of their field tests. In this research, the authors consider a new technique that allows reducing the duration of full-scale testing of machines by 30%. To this end, three new indicator coefficients were calculated in real time to determine the moments corresponding to the characteristic points. In the work, new terms for thermal characteristics of machine tools are introduced: kinetic and dynamic characteristics of the temperature movements of the machine. This allow taking into account not only the experimental values for the temperature displacements of the elements of the carrier system of the machine, but also their derivatives up to the third order, inclusively. The work is based on experimental data obtained in the course of full-scale thermal tests of a drilling-milling and boring CNC machine.

Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming.

PubMed

Adhikari, Shyam Prasad; Yang, Changju; Slot, Krzysztof; Kim, Hyongsuk

2018-01-10

This paper presents a vision sensor-based solution to the challenging problem of detecting and following trails in highly unstructured natural environments like forests, rural areas and mountains, using a combination of a deep neural network and dynamic programming. The deep neural network (DNN) concept has recently emerged as a very effective tool for processing vision sensor signals. A patch-based DNN is trained with supervised data to classify fixed-size image patches into "trail" and "non-trail" categories, and reshaped to a fully convolutional architecture to produce trail segmentation map for arbitrary-sized input images. As trail and non-trail patches do not exhibit clearly defined shapes or forms, the patch-based classifier is prone to misclassification, and produces sub-optimal trail segmentation maps. Dynamic programming is introduced to find an optimal trail on the sub-optimal DNN output map. Experimental results showing accurate trail detection for real-world trail datasets captured with a head mounted vision system are presented.

Vision-based system identification technique for building structures using a motion capture system

NASA Astrophysics Data System (ADS)

Oh, Byung Kwan; Hwang, Jin Woo; Kim, Yousok; Cho, Tongjun; Park, Hyo Seon

2015-11-01

This paper presents a new vision-based system identification (SI) technique for building structures by using a motion capture system (MCS). The MCS with outstanding capabilities for dynamic response measurements can provide gage-free measurements of vibrations through the convenient installation of multiple markers. In this technique, from the dynamic displacement responses measured by MCS, the dynamic characteristics (natural frequency, mode shape, and damping ratio) of building structures are extracted after the processes of converting the displacement from MCS to acceleration and conducting SI by frequency domain decomposition. A free vibration experiment on a three-story shear frame was conducted to validate the proposed technique. The SI results from the conventional accelerometer-based method were compared with those from the proposed technique and showed good agreement, which confirms the validity and applicability of the proposed vision-based SI technique for building structures. Furthermore, SI directly employing MCS measured displacements to FDD was performed and showed identical results to those of conventional SI method.

The analysis of the influence of fractal structure of stimuli on fractal dynamics in fixational eye movements and EEG signal

NASA Astrophysics Data System (ADS)

Namazi, Hamidreza; Kulish, Vladimir V.; Akrami, Amin

2016-05-01

One of the major challenges in vision research is to analyze the effect of visual stimuli on human vision. However, no relationship has been yet discovered between the structure of the visual stimulus, and the structure of fixational eye movements. This study reveals the plasticity of human fixational eye movements in relation to the ‘complex’ visual stimulus. We demonstrated that the fractal temporal structure of visual dynamics shifts towards the fractal dynamics of the visual stimulus (image). The results showed that images with higher complexity (higher fractality) cause fixational eye movements with lower fractality. Considering the brain, as the main part of nervous system that is engaged in eye movements, we analyzed the governed Electroencephalogram (EEG) signal during fixation. We have found out that there is a coupling between fractality of image, EEG and fixational eye movements. The capability observed in this research can be further investigated and applied for treatment of different vision disorders.

Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System

PubMed Central

García-Garrido, Miguel A.; Ocaña, Manuel; Llorca, David F.; Arroyo, Estefanía; Pozuelo, Jorge; Gavilán, Miguel

2012-01-01

This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance. PMID:22438704

Complete vision-based traffic sign recognition supported by an I2V communication system.

PubMed

García-Garrido, Miguel A; Ocaña, Manuel; Llorca, David F; Arroyo, Estefanía; Pozuelo, Jorge; Gavilán, Miguel

2012-01-01

This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance.

USC orthogonal multiprocessor for image processing with neural networks

NASA Astrophysics Data System (ADS)

Hwang, Kai; Panda, Dhabaleswar K.; Haddadi, Navid

1990-07-01

This paper presents the architectural features and imaging applications of the Orthogonal MultiProcessor (OMP) system, which is under construction at the University of Southern California with research funding from NSF and assistance from several industrial partners. The prototype OMP is being built with 16 Intel i860 RISC microprocessors and 256 parallel memory modules using custom-designed spanning buses, which are 2-D interleaved and orthogonally accessed without conflicts. The 16-processor OMP prototype is targeted to achieve 430 MIPS and 600 Mflops, which have been verified by simulation experiments based on the design parameters used. The prototype OMP machine will be initially applied for image processing, computer vision, and neural network simulation applications. We summarize important vision and imaging algorithms that can be restructured with neural network models. These algorithms can efficiently run on the OMP hardware with linear speedup. The ultimate goal is to develop a high-performance Visual Computer (Viscom) for integrated low- and high-level image processing and vision tasks.

Advanced integrated enhanced vision systems

NASA Astrophysics Data System (ADS)

Kerr, J. R.; Luk, Chiu H.; Hammerstrom, Dan; Pavel, Misha

2003-09-01

In anticipation of its ultimate role in transport, business and rotary wing aircraft, we clarify the role of Enhanced Vision Systems (EVS): how the output data will be utilized, appropriate architecture for total avionics integration, pilot and control interfaces, and operational utilization. Ground-map (database) correlation is critical, and we suggest that "synthetic vision" is simply a subset of the monitor/guidance interface issue. The core of integrated EVS is its sensor processor. In order to approximate optimal, Bayesian multi-sensor fusion and ground correlation functionality in real time, we are developing a neural net approach utilizing human visual pathway and self-organizing, associative-engine processing. In addition to EVS/SVS imagery, outputs will include sensor-based navigation and attitude signals as well as hazard detection. A system architecture is described, encompassing an all-weather sensor suite; advanced processing technology; intertial, GPS and other avionics inputs; and pilot and machine interfaces. Issues of total-system accuracy and integrity are addressed, as well as flight operational aspects relating to both civil certification and military applications in IMC.

InPRO: Automated Indoor Construction Progress Monitoring Using Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Hamledari, Hesam

In this research, an envisioned automated intelligent robotic solution for automated indoor data collection and inspection that employs a series of unmanned aerial vehicles (UAV), entitled "InPRO", is presented. InPRO consists of four stages, namely: 1) automated path planning; 2) autonomous UAV-based indoor inspection; 3) automated computer vision-based assessment of progress; and, 4) automated updating of 4D building information models (BIM). The works presented in this thesis address the third stage of InPRO. A series of computer vision-based methods that automate the assessment of construction progress using images captured at indoor sites are introduced. The proposed methods employ computer vision and machine learning techniques to detect the components of under-construction indoor partitions. In particular, framing (studs), insulation, electrical outlets, and different states of drywall sheets (installing, plastering, and painting) are automatically detected using digital images. High accuracy rates, real-time performance, and operation without a priori information are indicators of the methods' promising performance.

Collaborated measurement of three-dimensional position and orientation errors of assembled miniature devices with two vision systems

NASA Astrophysics Data System (ADS)

Wang, Xiaodong; Zhang, Wei; Luo, Yi; Yang, Weimin; Chen, Liang

2013-01-01

In assembly of miniature devices, the position and orientation of the parts to be assembled should be guaranteed during or after assembly. In some cases, the relative position or orientation errors among the parts can not be measured from only one direction using visual method, because of visual occlusion or for the features of parts located in a three-dimensional way. An automatic assembly system for precise miniature devices is introduced. In the modular assembly system, two machine vision systems were employed for measurement of the three-dimensionally distributed assembly errors. High resolution CCD cameras and high position repeatability precision stages were integrated to realize high precision measurement in large work space. The two cameras worked in collaboration in measurement procedure to eliminate the influence of movement errors of the rotational or translational stages. A set of templates were designed for calibration of the vision systems and evaluation of the system's measurement accuracy.

Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

PubMed

Zhang, Jianhua; Yin, Zhong; Wang, Rubin

2017-01-01

This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

Image-based automatic recognition of larvae

NASA Astrophysics Data System (ADS)

Sang, Ru; Yu, Guiying; Fan, Weijun; Guo, Tiantai

2010-08-01

As the main objects, imagoes have been researched in quarantine pest recognition in these days. However, pests in their larval stage are latent, and the larvae spread abroad much easily with the circulation of agricultural and forest products. It is presented in this paper that, as the new research objects, larvae are recognized by means of machine vision, image processing and pattern recognition. More visional information is reserved and the recognition rate is improved as color image segmentation is applied to images of larvae. Along with the characteristics of affine invariance, perspective invariance and brightness invariance, scale invariant feature transform (SIFT) is adopted for the feature extraction. The neural network algorithm is utilized for pattern recognition, and the automatic identification of larvae images is successfully achieved with satisfactory results.

KSC-04PD-1995

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. Two students at Ronald E. McNair High School in Atlanta proudly display the banner identifying McNair as a NASA Explorer School. The students enjoyed a presentation earlier by KSC Deputy Director Dr. Woodrow Whitlow Jr., astronaut Leland Melvin and Dr. Julian Earls, director of NASA Glenn Research Center. 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.

New design environment for defect detection in web inspection systems

NASA Astrophysics Data System (ADS)

Hajimowlana, S. Hossain; Muscedere, Roberto; Jullien, Graham A.; Roberts, James W.

1997-09-01

One of the aims of industrial machine vision is to develop computer and electronic systems destined to replace human vision in the process of quality control of industrial production. In this paper we discuss the development of a new design environment developed for real-time defect detection using reconfigurable FPGA and DSP processor mounted inside a DALSA programmable CCD camera. The FPGA is directly connected to the video data-stream and outputs data to a low bandwidth output bus. The system is targeted for web inspection but has the potential for broader application areas. We describe and show test results of the prototype system board, mounted inside a DALSA camera and discuss some of the algorithms currently simulated and implemented for web inspection applications.

Theory research of seam recognition and welding torch pose control based on machine vision

NASA Astrophysics Data System (ADS)

Long, Qiang; Zhai, Peng; Liu, Miao; He, Kai; Wang, Chunyang

2017-03-01

At present, the automation requirement of the welding become higher, so a method of the welding information extraction by vision sensor is proposed in this paper, and the simulation with the MATLAB has been conducted. Besides, in order to improve the quality of robot automatic welding, an information retrieval method for welding torch pose control by visual sensor is attempted. Considering the demands of welding technology and engineering habits, the relative coordinate systems and variables are strictly defined, and established the mathematical model of the welding pose, and verified its feasibility by using the MATLAB simulation in the paper, these works lay a foundation for the development of welding off-line programming system with high precision and quality.

Smartphone measurement engineering - Innovative challenges for science & education, instrumentation & training

NASA Astrophysics Data System (ADS)

Hofmann, D.; Dittrich, P.-G.; Duentsch, E.

2010-07-01

Smartphones have an enormous conceptual and structural influence on measurement science & education, instrumentation & training. Smartphones are matured. They became convenient, reliable and affordable. In 2009 worldwide 174 million Smartphones has been delivered. Measurement with Smartphones is ready for the future. In only 10 years the German vision industry tripled its global sales volume to one Billion Euro/Year. Machine vision is used for mobile object identification, contactless industrial quality control, personalized health care, remote facility and transport management, safety critical surveillance and all tasks which are too complex for the human eye or too monotonous for the human brain. Aim of the paper is to describe selected success stories for the application of Smartphones for measurement engineering in science and education, instrumentation and training.

Vision based speed breaker detection for autonomous vehicle

NASA Astrophysics Data System (ADS)

C. S., Arvind; Mishra, Ritesh; Vishal, Kumar; Gundimeda, Venugopal

2018-04-01

In this paper, we are presenting a robust and real-time, vision-based approach to detect speed breaker in urban environments for autonomous vehicle. Our method is designed to detect the speed breaker using visual inputs obtained from a camera mounted on top of a vehicle. The method performs inverse perspective mapping to generate top view of the road and segment out region of interest based on difference of Gaussian and median filter images. Furthermore, the algorithm performs RANSAC line fitting to identify the possible speed breaker candidate region. This initial guessed region via RANSAC, is validated using support vector machine. Our algorithm can detect different categories of speed breakers on cement, asphalt and interlock roads at various conditions and have achieved a recall of 0.98.

Computational Fluid Dynamic Simulation of Flow in Abrasive Water Jet Machining

NASA Astrophysics Data System (ADS)

Venugopal, S.; Sathish, S.; Jothi Prakash, V. M.; Gopalakrishnan, T.

2017-03-01

Abrasive water jet cutting is one of the most recently developed non-traditional manufacturing technologies. In this machining, the abrasives are mixed with suspended liquid to form semi liquid mixture. The general nature of flow through the machining, results in fleeting wear of the nozzle which decrease the cutting performance. The inlet pressure of the abrasive water suspension has main effect on the major destruction characteristics of the inner surface of the nozzle. The aim of the project is to analyze the effect of inlet pressure on wall shear and exit kinetic energy. The analysis could be carried out by changing the taper angle of the nozzle, so as to obtain optimized process parameters for minimum nozzle wear. The two phase flow analysis would be carried by using computational fluid dynamics tool CFX. It is also used to analyze the flow characteristics of abrasive water jet machining on the inner surface of the nozzle. The availability of optimized process parameters of abrasive water jet machining (AWJM) is limited to water and experimental test can be cost prohibitive. In this case, Computational fluid dynamics analysis would provide better results.

Effects of tear film dynamics on quality of vision.

PubMed

Koh, Shizuka; Tung, Cynthia I; Inoue, Yasushi; Jhanji, Vishal

2018-06-15

The precorneal tear film is maintained by blinking and exhibits different phases in the tear cycle. The tear film serves as the most anterior surface of the eye and plays an important role as a first refractive component of the eye. Alterations in tear film dynamics may cause both vision-related and ocular surface-related symptoms. Although the optical quality associated with the tear film dynamics previously received little attention, objective measurements of optical quality using wavefront sensors have enabled us to quantify optical aberrations induced by the tear film. This has provided an objective method for assessing reduced optical quality in dry eye; thus, visual disturbances were included in the definition of dry eye disease in the 2007 Dry Eye Workshop report. In addition, sequential measurements of wavefront aberrations have provided us with valuable insights into the dynamic optical changes associated with tear film dynamics. This review will focus on the current knowledge of the mechanisms of wavefront variations that are caused by different aspects of tear film dynamics: specifically, quality, quantity and properties of the tear film, demonstrating the respective effects of dry eye, epiphora and instillation of eye drops on the quality of vision. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Parameter identification and optimization of slide guide joint of CNC machine tools

NASA Astrophysics Data System (ADS)

Zhou, S.; Sun, B. B.

2017-11-01

The joint surface has an important influence on the performance of CNC machine tools. In order to identify the dynamic parameters of slide guide joint, the parametric finite element model of the joint is established and optimum design method is used based on the finite element simulation and modal test. Then the mode that has the most influence on the dynamics of slip joint is found through harmonic response analysis. Take the frequency of this mode as objective, the sensitivity analysis of the stiffness of each joint surface is carried out using Latin Hypercube Sampling and Monte Carlo Simulation. The result shows that the vertical stiffness of slip joint surface constituted by the bed and the slide plate has the most obvious influence on the structure. Therefore, this stiffness is taken as the optimization variable and the optimal value is obtained through studying the relationship between structural dynamic performance and stiffness. Take the stiffness values before and after optimization into the FEM of machine tool, and it is found that the dynamic performance of the machine tool is improved.

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

PubMed Central

Wang, Shuihua; Yang, Ming; Du, Sidan; Yang, Jiquan; Liu, Bin; Gorriz, Juan M.; Ramírez, Javier; Yuan, Ti-Fei; Zhang, Yudong

2016-01-01

Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss. PMID:27807415

Massively parallel neural circuits for stereoscopic color vision: encoding, decoding and identification.

PubMed

Lazar, Aurel A; Slutskiy, Yevgeniy B; Zhou, Yiyin

2015-03-01

Past work demonstrated how monochromatic visual stimuli could be faithfully encoded and decoded under Nyquist-type rate conditions. Color visual stimuli were then traditionally encoded and decoded in multiple separate monochromatic channels. The brain, however, appears to mix information about color channels at the earliest stages of the visual system, including the retina itself. If information about color is mixed and encoded by a common pool of neurons, how can colors be demixed and perceived? We present Color Video Time Encoding Machines (Color Video TEMs) for encoding color visual stimuli that take into account a variety of color representations within a single neural circuit. We then derive a Color Video Time Decoding Machine (Color Video TDM) algorithm for color demixing and reconstruction of color visual scenes from spikes produced by a population of visual neurons. In addition, we formulate Color Video Channel Identification Machines (Color Video CIMs) for functionally identifying color visual processing performed by a spiking neural circuit. Furthermore, we derive a duality between TDMs and CIMs that unifies the two and leads to a general theory of neural information representation for stereoscopic color vision. We provide examples demonstrating that a massively parallel color visual neural circuit can be first identified with arbitrary precision and its spike trains can be subsequently used to reconstruct the encoded stimuli. We argue that evaluation of the functional identification methodology can be effectively and intuitively performed in the stimulus space. In this space, a signal reconstructed from spike trains generated by the identified neural circuit can be compared to the original stimulus. Copyright © 2014 Elsevier Ltd. All rights reserved.

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning.

PubMed

Wang, Shuihua; Yang, Ming; Du, Sidan; Yang, Jiquan; Liu, Bin; Gorriz, Juan M; Ramírez, Javier; Yuan, Ti-Fei; Zhang, Yudong

2016-01-01

Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.

Modern trends in industrial technology of production of optical polymeric components for night vision devices

NASA Astrophysics Data System (ADS)

Goev, A. I.; Knyazeva, N. A.; Potelov, V. V.; Senik, B. N.

2005-06-01

The present paper represents in detail the complex approach to creating industrial technology of production of polymeric optical components: information has been given on optical polymeric materials, automatic machines for injection moulding, the possibilities of the Moldflow system (the AB "Universal" company) used for mathematical simulation of the technological process of injection moulding and making the moulds.

When America Makes, America Works: A Successful Public Private 3D Printing (Postprint)

DTIC Science & Technology

2016-09-01

National Additive Manufacturing Innovation Institute, a public-private partnership led by the National Center for Defense Manufacturing and Machining...NCDMM), a not-for-profit 501(c)3 organization. The vision for America Makes is to accelerate additive manufacturing (AM) innovation to enable...60 members are small businesses. 15. SUBJECT TERMS America Makes; National Additive Manufacturing Innovation Institute (NAMII); National Center

The 1991 Goddard Conference on Space Applications of Artificial Intelligence

NASA Technical Reports Server (NTRS)

Rash, James L. (Editor)

1991-01-01

The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in this proceeding fall into the following areas: Planning and scheduling, fault monitoring/diagnosis/recovery, machine vision, robotics, system development, information management, knowledge acquisition and representation, distributed systems, tools, neural networks, and miscellaneous applications.