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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Visual object classification by robots, using on-line, self-supervised learning

Pejman Iravani; Peter Hall; Daniel Beale; Cyril Charron; Yulia Hicks 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011

The challenge addressed in this paper is the classification of visual objects by robots. Visual classification is an active field within Computer Vision, with excellent results achieved recently. However, not all of the advances transfer into the study of robots in free environments; two differences stand out. One is that Computer Vision algorithms often rely on batch learning over a ...


Subjective contours are useful for extracting contours with very weak contrasts

M. Teranishi; N. Ohnishi; N. Sugie Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 1993

Certain figures cause one to perceive clearly visible contours (subjective contours) in the region where there are no physical contrasts in brightness or color. An algorithm is proposed for generating subjective contours automatically from given figures. The algorithm is based on the following idea: 'L' or 'I' type vertices are produced on physical contours because they are occluded by a ...


Real-time plane extraction from depth images with the Randomized Hough Transform

Daniel Dube; Andreas Zell 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011

Depth cameras, like the Microsoft Kinect system, are valuable sensors for mobile robotics since their data enables a highly detailed perception of the environmental structure. Certainly, their amount of data is often too high to be processed in real-time by the limited resources of mobile robots. One way of using these sensors is to reduce the amount of data by ...


Matrix Factorization Approach for Feature Deduction and Design of Intrusion Detection Systems

Vaclav Snasel; Jan Platos; Pavel Kromer; Ajith Abraham 2008 The Fourth International Conference on Information Assurance and Security, 2008

Current Intrusion Detection Systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this research is to identify important input features in building an IDS that is computationally efficient and effective. This paper propose a novel matrix factorization approach ...


A fuzzy algorithm for navigation of mobile robots in unknown environments

Tsong-Li Lee; Li-Chun Lai; Chia-Ju Wu 2005 IEEE International Symposium on Circuits and Systems, 2005

A fuzzy algorithm is proposed to navigate a mobile robot in a completely unknown environment. The mobile robot is equipped with an electronic compass and two optical encoders for dead-reckoning, and two ultrasonic modules for self-localization and environment recognition. From the readings of sensors at every sampling instant, the proposed fuzzy algorithm determines the priorities of thirteen possible heading directions. ...


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Educational Resources on Computer Vision

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eLearning

Visual object classification by robots, using on-line, self-supervised learning

Pejman Iravani; Peter Hall; Daniel Beale; Cyril Charron; Yulia Hicks 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011

The challenge addressed in this paper is the classification of visual objects by robots. Visual classification is an active field within Computer Vision, with excellent results achieved recently. However, not all of the advances transfer into the study of robots in free environments; two differences stand out. One is that Computer Vision algorithms often rely on batch learning over a ...


Subjective contours are useful for extracting contours with very weak contrasts

M. Teranishi; N. Ohnishi; N. Sugie Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 1993

Certain figures cause one to perceive clearly visible contours (subjective contours) in the region where there are no physical contrasts in brightness or color. An algorithm is proposed for generating subjective contours automatically from given figures. The algorithm is based on the following idea: 'L' or 'I' type vertices are produced on physical contours because they are occluded by a ...


Real-time plane extraction from depth images with the Randomized Hough Transform

Daniel Dube; Andreas Zell 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011

Depth cameras, like the Microsoft Kinect system, are valuable sensors for mobile robotics since their data enables a highly detailed perception of the environmental structure. Certainly, their amount of data is often too high to be processed in real-time by the limited resources of mobile robots. One way of using these sensors is to reduce the amount of data by ...


Matrix Factorization Approach for Feature Deduction and Design of Intrusion Detection Systems

Vaclav Snasel; Jan Platos; Pavel Kromer; Ajith Abraham 2008 The Fourth International Conference on Information Assurance and Security, 2008

Current Intrusion Detection Systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this research is to identify important input features in building an IDS that is computationally efficient and effective. This paper propose a novel matrix factorization approach ...


A fuzzy algorithm for navigation of mobile robots in unknown environments

Tsong-Li Lee; Li-Chun Lai; Chia-Ju Wu 2005 IEEE International Symposium on Circuits and Systems, 2005

A fuzzy algorithm is proposed to navigate a mobile robot in a completely unknown environment. The mobile robot is equipped with an electronic compass and two optical encoders for dead-reckoning, and two ultrasonic modules for self-localization and environment recognition. From the readings of sensors at every sampling instant, the proposed fuzzy algorithm determines the priorities of thirteen possible heading directions. ...


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IEEE-USA E-Books

  • The Computational Hoverfly; a Study in Computational Neuroethology

    Studies in computer vision have only recently realised the advantage of adding a behavioural component to vision systems, enabling them to make programmed 'eye movements'. Such an animate vision capability allows the system to employ a nonuniform or foveal sampling strategy, with gaze-control mechanisms repositioning the limited high-resolution area of the visual field. The hoverfly Syritta pipiens is an insect that exhibits foveal animate vision behaviour highly similar to the corresponding activity in humans. This paper discusses a simulation model of Syritta created for studying the neural processes underlying such visually guided behaviour. The approach differs from standard "neural network" modeling techniques in that the simulated Syritta exists within a elosed simulated environment, i.e. there is no need for human intervention: such an approach is an example of computational neuroethology.

  • No title

    <p>Because circular objects are projected to ellipses in images, ellipse fitting is a first step for 3-D analysis of circular objects in computer vision applications. For this reason, the study of ellipse fitting began as soon as computers came into use for image analysis in the 1970s, but it is only recently that optimal computation techniques based on the statistical properties of noise were established. These include renormalization (1993), which was then improved as FNS (2000) and HEIV (2000). Later, further improvements, called hyperaccurate correction (2006), HyperLS (2009), and hyper-renormalization (2012), were presented. Today, these are regarded as the most accurate fitting methods among all known techniques. This book describes these algorithms as well implementation details and applications to 3-D scene analysis. </p><p> We also present general mathematical theories of statistical optimization underlying all ellipse fitting algorithms, including rig rous covariance and bias analyses and the theoretical accuracy limit. The results can be directly applied to other computer vision tasks including computing fundamental matrices and homographies between images. </p><p> This book can serve not simply as a reference of ellipse fitting algorithms for researchers, but also as learning material for beginners who want to start computer vision research. The sample program codes are downloadable from the website: https://sites.google.com/a/morganclaypool.com/ellipse-fitting-for-computer- vision-implementation-and-applications.</p>

  • No title

    Malignant tumors due to breast cancer and masses due to benign disease appear in mammograms with different shape characteristics: the former usually have rough, spiculated, or microlobulated contours, whereas the latter commonly have smooth, round, oval, or macrolobulated contours. Features that characterize shape roughness and complexity can assist in distinguishing between malignant tumors and benign masses. In spite of the established importance of shape factors in the analysis of breast tumors and masses, difficulties exist in obtaining accurate and artifact-free boundaries of the related regions from mammograms. Whereas manually drawn contours could contain artifacts related to hand tremor and are subject to intra-observer and inter- observer variations, automatically detected contours could contain noise and inaccuracies due to limitations or errors in the procedures for the detection and segmentation of the related regions. Modeling procedures are desired to eliminate the artifa ts in a given contour, while preserving the important and significant details present in the contour. This book presents polygonal modeling methods that reduce the influence of noise and artifacts while preserving the diagnostically relevant features, in particular the spicules and lobulations in the given contours. In order to facilitate the derivation of features that capture the characteristics of shape roughness of contours of breast masses, methods to derive a signature based on the turning angle function obtained from the polygonal model are described. Methods are also described to derive an index of spiculation, an index characterizing the presence of convex regions, an index characterizing the presence of concave regions, an index of convexity, and a measure of fractal dimension from the turning angle function. Results of testing the methods with a set of 111 contours of 65 benign masses and 46 malignant tumors are presented and discussed. It is shown that shape modeling and a alysis can lead to classification accuracy in discriminating between benign masses and malignant tumors, in terms of the area under the receiver operating characteristic curve, of up to 0.94. The methods have applications in modeling and analysis of the shape of various types of regions or objects in images, computer vision, computer graphics, and analysis of biomedical images, with particular significance in computer-aided diagnosis of breast cancer. Table of Contents: Analysis of Shape / Polygonal Modeling of Contours / Shape Factors for Pattern Classification / Classification of Breast Masses

  • No title

    Video context analysis is an active and vibrant research area, which provides means for extracting, analyzing and understanding behavior of a single target and multiple targets. Over the last few decades, computer vision researchers have been working to improve the accuracy and robustness of algorithms to analyse the context of a video automatically. In general, the research work in this area can be categorized into three major topics: 1) counting number of people in the scene 2) tracking individuals in a crowd and 3) understanding behavior of a single target or multiple targets in the scene. This book focusses on tracking individual targets and detecting abnormal behavior of a crowd in a complex scene. Firstly, this book surveys the state-of-the-art methods for tracking multiple targets in a complex scene and describes the authors' approach for tracking multiple targets. The proposed approach is to formulate the problem of multi-target tracking as an optimization problem of finding ynamic optima (pedestrians) where these optima interact frequently. A novel particle swarm optimization (PSO) algorithm that uses a set of multiple swarms is presented. Through particles and swarms diversification, motion prediction is introduced into the standard PSO, constraining swarm members to the most likely region in the search space. The social interaction among swarm and the output from pedestrians-detector are also incorporated into the velocity-updating equation. This allows the proposed approach to track multiple targets in a crowded scene with severe occlusion and heavy interactions among targets. The second part of this book discusses the problem of detecting and localising abnormal activities in crowded scenes. We present a spatio-temporal Laplacian Eigenmap method for extracting different crowd activities from videos. This method learns the spatial and temporal variations of local motions in an embedded space and employs representatives of different activities to const uct the model which characterises the regular behavior of a crowd. This model of regular crowd behavior allows for the detection of abnormal crowd activities both in local and global context and the localization of regions which show abnormal behavior.

  • Applications: Learning

    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.

  • No title

    The recognition of humans and their activities from video sequences is currently a very active area of research because of its applications in video surveillance, design of realistic entertainment systems, multimedia communications, and medical diagnosis. In this lecture, we discuss the use of face and gait signatures for human identification and recognition of human activities from video sequences. We survey existing work and describe some of the more well-known methods in these areas. We also describe our own research and outline future possibilities. In the area of face recognition, we start with the traditional methods for image-based analysis and then describe some of the more recent developments related to the use of video sequences, 3D models, and techniques for representing variations of illumination. We note that the main challenge facing researchers in this area is the development of recognition strategies that are robust to changes due to pose, illumination, disguise, and a ing. Gait recognition is a more recent area of research in video understanding, although it has been studied for a long time in psychophysics and kinesiology. The goal for video scientists working in this area is to automatically extract the parameters for representation of human gait. We describe some of the techniques that have been developed for this purpose, most of which are appearance based. We also highlight the challenges involved in dealing with changes in viewpoint and propose methods based on image synthesis, visual hull, and 3D models. In the domain of human activity recognition, we present an extensive survey of various methods that have been developed in different disciplines like artificial intelligence, image processing, pattern recognition, and computer vision. We then outline our method for modeling complex activities using 2D and 3D deformable shape theory. The wide application of automatic human identification and activity recognition methods will require the fusio of different modalities like face and gait, dealing with the problems of pose and illumination variations, and accurate computation of 3D models. The last chapter of this lecture deals with these areas of future research.

  • Efficient Structure Learning of Markov Networks using L1-Regularization

    Markov networks are commonly used in a wide variety of applications, ranging from computer vision, to natural language, to computational biology. In most current applications, even those that rely heavily on learned models, the structure of the Markov network is constructed by hand, due to the lack of effective algorithms for learning Markov network structure from data. In this paper, we provide a computationally efficient method for learning Markov network structure from data. Our method is based on the use of L1 regularization on the weights of the log-linear model, which has the effect of biasing the model towards solutions wheremany of the parameters are zero. This formulation converts theMarkov network learning problem into a convex optimization problem in a continuous space, which can be solved using efficient gradient methods. A key issue in this setting is the (unavoidable) use of approximate inference, which can lead to errors in the gradient computation when the network structure is dense. Thus, we explore the use of different feature introduction schemes and compare their performance. We provide results for our method on synthetic data, and on two real world data sets: pixel values in the MNIST data, and genetic sequence variations in the human HapMap data. We show that our L1-based method achieves considerably higher generalization performance than the more standard L2-based method (a Gaussian parameter prior) or pure maximum-likelihood learning. We also show that we can learn MRF network structure at a computational cost that is not much greater than learning parameters alone, demonstrating the existence of a feasible method for this important problem.

  • 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.

  • No title

    This book introduces zero-effort technologies (ZETs), an emerging class of technology that requires little or no effort from the people who use it. ZETs use advanced techniques, such as computer vision, sensor fusion, decision- making and planning, and machine learning to autonomously operate through the collection, analysis, and application of data about the user and his/her context. This book gives an overview of ZETs, presents concepts in the development of pervasive intelligent technologies and environments for health and rehabilitation, along with an in-depth discussion of the design principles that this approach entails. The book concludes with a discussion of specific ZETs that have applied these design principles with the goal of ensuring the safety and well-being of the people who use them, such as older adults with dementia and provides thoughts regarding future directions of the field. Table of Contents: Lecture Overview / Introduction to Zero Effort Technologies / Designing ZETs / Building and Evaluating ZETs / Examples of ZETs / Conclusions and Future Directions

  • No title

    Current vision systems are designed to perform in normal weather condition. However, no one can escape from severe weather conditions. Bad weather reduces scene contrast and visibility, which results in degradation in the performance of various computer vision algorithms such as object tracking, segmentation and recognition. Thus, current vision systems must include some mechanisms that enable them to perform up to the mark in bad weather conditions such as rain and fog. Rain causes the spatial and temporal intensity variations in images or video frames. These intensity changes are due to the random distribution and high velocities of the raindrops. Fog causes low contrast and whiteness in the image and leads to a shift in the color. This book has studied rain and fog from the perspective of vision. The book has two main goals: 1) removal of rain from videos captured by a moving and static camera, 2) removal of the fog from images and videos captured by a moving single uncalibrated ca era system. The book begins with a literature survey. Pros and cons of the selected prior art algorithms are described, and a general framework for the development of an efficient rain removal algorithm is explored. Temporal and spatiotemporal properties of rain pixels are analyzed and using these properties, two rain removal algorithms for the videos captured by a static camera are developed. For the removal of rain, temporal and spatiotemporal algorithms require fewer numbers of consecutive frames which reduces buffer size and delay. These algorithms do not assume the shape, size and velocity of raindrops which make it robust to different rain conditions (i.e., heavy rain, light rain and moderate rain). In a practical situation, there is no ground truth available for rain video. Thus, no reference quality metric is very useful in measuring the efficacy of the rain removal algorithms. Temporal variance and spatiotemporal variance are presented in this book as no reference quality met ics. An efficient rain removal algorithm using meteorological properties of rain is developed. The relation among the orientation of the raindrops, wind velocity and terminal velocity is established. This relation is used in the estimation of shape- based features of the raindrop. Meteorological property-based features helped to discriminate the rain and non-rain pixels. Most of the prior art algorithms are designed for the videos captured by a static camera. The use of global motion compensation with all rain removal algorithms designed for videos captured by static camera results in better accuracy for videos captured by moving camera. Qualitative and quantitative results confirm that probabilistic temporal, spatiotemporal and meteorological algorithms outperformed other prior art algorithms in terms of the perceptual quality, buffer size, execution delay and system cost. The work presented in this book can find wide application in entertainment industries, transportation, tracking a d consumer electronics. Table of Contents: Acknowledgments / Introduction / Analysis of Rain / Dataset and Performance Metrics / Important Rain Detection Algorithms / Probabilistic Approach for Detection and Removal of Rain / Impact of Camera Motion on Detection of Rain / Meteorological Approach for Detection and Removal of Rain from Videos / Conclusion and Scope of Future Work / Bibliography / Authors' Biographies



Standards related to Computer Vision

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No standards are currently tagged "Computer Vision"