Conferences related to Computer Vision

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2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE)

Control Systems & ApplicationsPower ElectronicsSignal Processing & Computational IntelligenceRobotics & MechatronicsSensors, Actuators & System IntegrationElectrical Machines & DrivesFactory Automation & Industrial InformaticsEmerging Technologies

  • 2012 IEEE 21st International Symposium on Industrial Electronics (ISIE)

    IEEE-ISIE is the largest summer conference of the IEEE Industrial Electronics Society, which is an international forum for presentation and discussion of the state of art in Industrial Electronics and related areas.

  • 2011 IEEE 20th International Symposium on Industrial Electronics (ISIE)

    Industrial electronics, power electronics, power converters, electrical machines and drives, signal processing, computational intelligence, mechatronics, robotics, telecommuniction, power systems, renewable energy, factory automation, industrial informatics.

  • 2010 IEEE International Symposium on Industrial Electronics (ISIE 2010)

    Application of electronics and electrical sciences for the enhancement of industrial and manufacturing processes. Latest developments in intelligent and computer control systems, robotics, factory communications and automation, flexible manufacturing, data acquisition and signal processing, vision systems, and power electronics.

  • 2009 IEEE International Symposium on Industrial Electronics (ISIE 2009)

    The purpose of the IEEE international conference is to provide a forum for presentation and discussion of the state-of art of Industrial Electronics and related areas.


2014 IEEE Winter Conference on Applications of Computer Vision (WACV)

Conference Scope: Computer Vision has become increasingly important in real world systems forcommercial, industrial and military applications. Computer Vision related technologies have migrated fromacademic institutions to industrial laboratories, and onward into deployable systems. The goal of thisworkshop is to bring together an international cadre of academic, industrial, and government researchers,along with companies applying vision techniques.

  • 2013 IEEE Workshop on Applications of Computer Vision (WACV)

    Computer Vision has become increasingly important in real world systems for commercial, industrial and military applications. Computer Vision related technologies have migrated from academic institutions to industrial laboratories, and onward into deployable systems. The goal of this workshop is to bring together an international cadre of academic, industrial, and government researchers, along with companies applying vision techniques.

  • 2011 IEEE Workshop on Applications of Computer Vision (WACV)

    Computer Vision has become increasingly important in real world systems for commercial, industrial and military applications. Computer Vision related technologies have started migrating from academic institutions to industrial laboratories, and onward into deployable systems. The goal of this workshop is to bring together an international cadre of academic, industrial, and government researchers, and companies applying vision techniques


IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

Applications of power electronics, artificial intelligence, robotics, and nanotechnology in electrification of automotive, military, biomedical, and utility industries.

  • IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

    Industrial and manufacturing theory and applications of electronics, controls, communications, instrumentation and computational intelligence.

  • IECON 2012 - 38th Annual Conference of IEEE Industrial Electronics

    The conference will be focusing on industrial and manufacturing theory and applications of electronics,power, sustainable development, controls, communications, instrumentation and computational intelligence.

  • IECON 2011 - 37th Annual Conference of IEEE Industrial Electronics

    industrial applications of electronics, control, robotics, signal processing, computational and artificial intelligence, sensors and actuators, instrumentation electronics, computer networks, internet and multimedia technologies.

  • IECON 2010 - 36th Annual Conference of IEEE Industrial Electronics

    IECON is an international conference on industrial applications of electronics, control, robotics, signal processing, computational and artificial intelligence, sensors and actuators, instrumentation electronics, computer networks, internet and multimedia technologies. The objectives of the conference are to provide high quality research and professional interactions for the advancement of science, technology, and fellowship.

  • IECON 2009 - 35th Annual Conference of IEEE Industrial Electronics

    Applications of electronics, instrumentation, control and computational intelligence to industrial and manufacturing systems and process. Major themes include power electronics, drives, sensors, actuators, signal processing, motion control, robotics, mechatronics, factory and building automation, and informatics. Emerging technologies and applications such as renewable energy, electronics reuse, and education.


2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

AVSS focuses on video and signal based surveillance. Topics include: 1) Sensors and data fusion, 2) Processing, detection & recognition, 3) Analytics, behavior & biometrics, 4) Data management and human-computer interfaces, 5) Applications and 6) Privacy Issues


2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI)

The 11th International Content Based Multimedia Indexing Workshop is to bring together the various communities involved in all aspects of content-based multimedia indexing, retrieval, browsing and presentation.The conference will host invited keynote talks and regular, special and demo sessions with contributed research papers.


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Periodicals related to Computer Vision

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Information Technology in Biomedicine, IEEE Transactions on

Telemedicine, teleradiology, telepathology, telemonitoring, telediagnostics, 3D animations in health care, health information networks, clinical information systems, virtual reality applications in medicine, broadband technologies, and global information infrastructure design for health care.


Multimedia, IEEE

IEEE Multimedia Magazine covers a broad range of issues in multimedia systems and applications. Articles, product reviews, new product descriptions, book reviews, and announcements of conferences and workshops cover topics that include hardware and software for media compression, coding and processing; media representations and standards for storage, editing, interchange, transmission and presentation; hardware platforms supporting multimedia applications; operating systems suitable ...


Pattern Analysis and Machine Intelligence, IEEE Transactions on

Statistical and structural pattern recognition; image analysis; computational models of vision; computer vision systems; enhancement, restoration, segmentation, feature extraction, shape and texture analysis; applications of pattern analysis in medicine, industry, government, and the arts and sciences; artificial intelligence, knowledge representation, logical and probabilistic inference, learning, speech recognition, character and text recognition, syntactic and semantic processing, understanding natural language, expert systems, ...


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Most published Xplore authors for Computer Vision

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Xplore Articles related to Computer Vision

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Principles Emerging from the Design of Visual Search Algorithms for Practical Inspection Tasks

E. R. Davies 2008 International Machine Vision and Image Processing Conference, 2008

This paper is targeted at the design of inspection algorithms involving fast visual search for features, products, and the artefacts that accompany them. The work is based on a number of cereal grain inspection tasks that have presented important problems relating to robustness, sensitivity, accuracy and speed of operation. Techniques covered include multi-level thresholding, morphological operators, mask design, multi-stage processing, ...


Real-time recognition of moving direction of a target object based on historical trajectories

Chen-Chien Hsu; Wei-Chen Liu; Chin-Ming Hong Proceedings 2011 International Conference on System Science and Engineering, 2011

This paper presents an effective algorithm to identify moving direction of an object in images based on historical trajectories of the moving object. By continuously acquiring images, temporal differencing is performed to detect the moving object followed by median filtering to reduce noises in the images. After that, opening operation is adopted to remove small objects lacking momentum in the ...


Interactive Image Segmentation Based on Region Merging Using Hierarchical Match Mechanism

Chongbo Zhou; Chuancai Liu 2012 International Conference on Computer Science and Service System, 2012

Image segmentation is to segment an image into disjoint parts. It is a fundamental and challenging task in computer vision. We present an interactive image segmentation method based on region merging. An image is first over- segmented into many super-pixels using bottom-up methods. Then some strokes are manually labeled on the over-segmented image. The strokes give information about object classes ...


High-arity interactions, polyhedral relaxations, and cutting plane algorithm for soft constraint optimisation (MAP-MRF)

Tomas Werner 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008

LP relaxation approach to soft constraint optimisation (i.e. MAP-MRF) has been mostly considered only for binary problems. We present its generalisation to n-ary problems, including a simple algorithm to optimise the LP bound, n-ary max-sum diffusion. As applications, we show that a hierarchy of gradually tighter polyhedral relaxations of MAP-MRF is obtained by adding zero interactions. We propose a cutting ...


Parameterized surface fitting via MAP estimation for binocular stereo

M. J. Weisman; A. L. Yuille; J. J. Clark Proceedings of 1995 IEEE International Conference on Robotics and Automation, 1995

We present a novel method for reconstructing three dimensional surfaces from stereo intensity data. We employ a set of competing surface hypotheses based on parameterized models. We use maximum a posteriori (MAP) estimation and demonstrate a connection to the Hough transform. Experimental results are given showing the effectiveness of the algorithm


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Educational Resources on Computer Vision

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eLearning

Principles Emerging from the Design of Visual Search Algorithms for Practical Inspection Tasks

E. R. Davies 2008 International Machine Vision and Image Processing Conference, 2008

This paper is targeted at the design of inspection algorithms involving fast visual search for features, products, and the artefacts that accompany them. The work is based on a number of cereal grain inspection tasks that have presented important problems relating to robustness, sensitivity, accuracy and speed of operation. Techniques covered include multi-level thresholding, morphological operators, mask design, multi-stage processing, ...


Real-time recognition of moving direction of a target object based on historical trajectories

Chen-Chien Hsu; Wei-Chen Liu; Chin-Ming Hong Proceedings 2011 International Conference on System Science and Engineering, 2011

This paper presents an effective algorithm to identify moving direction of an object in images based on historical trajectories of the moving object. By continuously acquiring images, temporal differencing is performed to detect the moving object followed by median filtering to reduce noises in the images. After that, opening operation is adopted to remove small objects lacking momentum in the ...


Interactive Image Segmentation Based on Region Merging Using Hierarchical Match Mechanism

Chongbo Zhou; Chuancai Liu 2012 International Conference on Computer Science and Service System, 2012

Image segmentation is to segment an image into disjoint parts. It is a fundamental and challenging task in computer vision. We present an interactive image segmentation method based on region merging. An image is first over- segmented into many super-pixels using bottom-up methods. Then some strokes are manually labeled on the over-segmented image. The strokes give information about object classes ...


High-arity interactions, polyhedral relaxations, and cutting plane algorithm for soft constraint optimisation (MAP-MRF)

Tomas Werner 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008

LP relaxation approach to soft constraint optimisation (i.e. MAP-MRF) has been mostly considered only for binary problems. We present its generalisation to n-ary problems, including a simple algorithm to optimise the LP bound, n-ary max-sum diffusion. As applications, we show that a hierarchy of gradually tighter polyhedral relaxations of MAP-MRF is obtained by adding zero interactions. We propose a cutting ...


Parameterized surface fitting via MAP estimation for binocular stereo

M. J. Weisman; A. L. Yuille; J. J. Clark Proceedings of 1995 IEEE International Conference on Robotics and Automation, 1995

We present a novel method for reconstructing three dimensional surfaces from stereo intensity data. We employ a set of competing surface hypotheses based on parameterized models. We use maximum a posteriori (MAP) estimation and demonstrate a connection to the Hough transform. Experimental results are given showing the effectiveness of the algorithm


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IEEE-USA E-Books

  • Sensorimotor transformations in the worlds of frogs and robots

    The paper develops a multilevel approach to the design and analysis of systems with "action-oriented perception", situating various robot and animal "designs" in an evolutionary perspective. We present a set of biological design principles within a broader perspective that shows their relevance for robot design. We introduce schemas to provide a coarse-grain analysis of "cooperative computation" in the brains of animals and the "brains" of robots, starting with an analysis of approach, avoidance, detour behavior, and path planning in frogs. An explicit account of neural mechanism of avoidance behavior in the frog illustrates how schemas may be implemented in neural networks. The focus of the rest of the article is on the relation of instinctive to reflective behavior. We generalize an analysis of the interaction of perceptual schemas in the VISIONS system for computer vision to a view of the interaction of perceptual and motor schemas in distributed planning which, we argue, has great promise for integrating mechanisms for action and perception in both animal and robot. We conclude with general observations on the lessons on relating structure and function which can be carried from biology to technology.

  • On Forensic Use of Biometrics

    Forensic science largely concerns the analysis of crime. The science of biometrics has developed approaches that are used to automatically identify individuals by personal characteristics. Biometric techniques have primarily been used to assure identity. The main steps of a biometric recognition approach include: acquisition of the biometric data, localization and alignment of the data, feature extraction, and matching. This chapter concentrates on two case studies discussing the forensic possibilities of face and ear as biometrics. It introduces the manual and computer-aided forensic face recognition. The chapter discusses the disparities between the behaviour of the current automatic face recognition systems and that which is needed for forensic application, and outlines the current progress towards addressing the challenges existing in face recognition. An emerging biometric ear is examined. There is a rich variety of approaches for ear biometrics and these are steeped in pattern recognition and computer vision.

  • Contributors

    Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications.The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naïve methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks.

  • Bibliography

    Two important subproblems of computer vision are the detection and recognition of 2D objects in gray-level images. This book discusses the construction and training of models, computational approaches to efficient implementation, and parallel implementations in biologically plausible neural network architectures. The approach is based on statistical modeling and estimation, with an emphasis on simplicity, transparency, and computational efficiency.The book describes a range of deformable template models, from coarse sparse models involving discrete, fast computations to more finely detailed models based on continuum formulations, involving intensive optimization. Each model is defined in terms of a subset of points on a reference grid (the template), a set of admissible instantiations of these points (deformations), and a statistical model for the data given a particular instantiation of the object present in the image. A recurring theme is a coarse to fine approach to the solution of vision problems. The book provides detailed descriptions of the algorithms used as well as the code, and the software and data sets are available on the Web.

  • No title

    While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL alg rithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph- based SSL methods have been successfully applied. Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index

  • Index

    Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications.The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naïve methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks.

  • Index

    Two important subproblems of computer vision are the detection and recognition of 2D objects in gray-level images. This book discusses the construction and training of models, computational approaches to efficient implementation, and parallel implementations in biologically plausible neural network architectures. The approach is based on statistical modeling and estimation, with an emphasis on simplicity, transparency, and computational efficiency.The book describes a range of deformable template models, from coarse sparse models involving discrete, fast computations to more finely detailed models based on continuum formulations, involving intensive optimization. Each model is defined in terms of a subset of points on a reference grid (the template), a set of admissible instantiations of these points (deformations), and a statistical model for the data given a particular instantiation of the object present in the image. A recurring theme is a coarse to fine approach to the solution of vision problems. The book provides detailed descriptions of the algorithms used as well as the code, and the software and data sets are available on the Web.

  • Single-Trial Analysis of EEG during Rapid Visual Discrimination: Enabling Cortically Coupled Computer Vision

    We describe our work using linear discrimination of multichannel electroencephalography for single-trial detection of neural signatures of visual recognition events. We demonstrate the approach as a methodology for relating neural variability to response variability, describing studies for response accuracy and response latency during visual target detection. We then show how the approach can be used to construct a novel type of brain-computer interface, which we term "cortically coupled computer vision." In this application, a large database of images is triaged using the detected neural signatures. We show how "cortical triaging" improves image search over a strictly behavioral response.

  • References

    If robots are to act intelligently in everyday environments, they must have a perception of motion and its consequences. This book describes experimental advances made in the interpretation of visual motion over the last few years that have moved researchers closer to emulating the way in which we recover information about the surrounding world. It describes algorithms that form a complete, implemented, and tested system developed by the authors to measure two-dimensional motion in an image sequence, then to compute three-dimensional structure and motion, and finally to recognize the moving objects.The authors develop algorithms to interpret visual motion around four principal constraints. The first and simplest allows the scene structure to be recovered on a pointwise basis. The second constrains the scene to a set of connected straight edges. The third makes the transition between edge and surface representations by demanding that the wireframe recovered is strictly polyhedral. And the final constraint assumes that the scene is comprised of planar surfaces, and recovers them directly.David W. Murray is University Lecturer in Engineering Science at the University of Oxford and Draper's Fellow in Robotics at St Anne's College, Oxford. Bernard F. Buxton is Senior Research Fellow at the General Electric Company's Hirst Research Centre, Wembley, UK, where he leads the Computer Vision Group in the Long Range Research Laboratory.Contents: Image, Scene, and Motion. Computing Image Motion. Structure from Motion of Points. The Structure and Motion of Edges. From Edges to Surfaces. Structure and Motion of Planes. Visual Motion Segmentation. Matching to Edge Models. Matching to Planar Surfaces.

  • Genetic Programming for Robot Vision

    Genetic Programming was used to create the vision subsystem of a reactive obstacle avoidance system for an autonomous mobile robot. The representation of algorithms was specifically chosen to capture the spirit of existing, hand written vision algorithms. Traditional computer vision operators such as Sobel gradient magnitude, median filters and the Moravec interest operator were combined arbitrarily. Images from an office hallway were used as training data. The evolved programs took a black and white camera image as input and estimated the location of the lowest non-ground pixel in a given column. The computed estimates were then given to a handwritten obstacle avoidance algorithm and used to control the robot in real time. Evolved programs successfully navigated in unstructured hallways, performing on par with hand- crafted systems



Standards related to Computer Vision

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No standards are currently tagged "Computer Vision"