[PDF] A Hierarchical Modular Vision System For Object Recognition eBook

Author : Jens Spehr
Publisher : Springer
Page : 210 pages
File Size : 30,54 MB
Release : 2014-11-13
Category : Technology & Engineering
ISBN : 3319113259

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In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.

Author : Jean-Michel Jolion
Publisher : Springer Science & Business Media
Page : 231 pages
File Size : 17,7 MB
Release : 2012-12-06
Category : Computers
ISBN : 1461527929

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Biological visual systems employ massively parallel processing to perform real-world visual tasks in real time. A key to this remarkable performance seems to be that biological systems construct representations of their visual image data at multiple scales. A Pyramid Framework for Early Vision describes a multiscale, or `pyramid', approach to vision, including its theoretical foundations, a set of pyramid-based modules for image processing, object detection, texture discrimination, contour detection and processing, feature detection and description, and motion detection and tracking. It also shows how these modules can be implemented very efficiently on hypercube-connected processor networks. A Pyramid Framework for Early Vision is intended for both students of vision and vision system designers; it provides a general approach to vision systems design as well as a set of robust, efficient vision modules.

Author : Anil K Jain
Publisher :
Page : 488 pages
File Size : 12,1 MB
Release : 1993-05-05
Category : Computers
ISBN :

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The design and construction of three-dimensional [3-D] object recognition systems has long occupied the attention of many computer vision researchers. The variety of systems that have been developed for this task is evidence both of its strong appeal to researchers and its applicability to modern manufacturing, industrial, military, and consumer environments. 3-D object recognition is of interest to scientists and engineers in several different disciplines due to both a desire to endow computers with robust visual capabilities, and the wide applications which would benefit from mature and robust vision systems. However, 3-D object recognition is a very complex problem, and few systems have been developed for actual production use; most existing systems have been developed for experimental use by researchers only. This edited collection of papers summarizes the state of the art in 3-D object recognition using examples of existing 3-D systems developed by leading researchers in the field. While most chapters describe a complete object recognition system, chapters on biological vision, sensing, and early processing are also included. The volume will serve as a valuable reference source for readers who are involved in implementing model-based object recognition systems, stimulating the cross-fertilisation of ideas in the various domains. The variety of topics on Image Communication is so broad that no one can be a specialist in all the topics, and the whole area is beyond the scope of a single volume, while the requirement of up to date information is ever increasing. This new closed-end book series is intended both as a comprehensive reference for those already active in the area of Image Communication, as well as providing newcomers with a foothold for commencing research. Each volume will comprise a state of the art work on the editor's/author's area of expertise, containing information until now scattered in many journals and proceedings.

Author : Sunhyoung Han
Publisher :
Page : 206 pages
File Size : 24,82 MB
Release : 2011
Category :
ISBN : 9781124906089

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Human visual perception mechanism is known to be effective and fast for object recognition problems and has inspired recognition algorithms. In this thesis we propose Hierarchical Discriminant Saliency Network (HDSN) mimicking hierarchical architecture of the primary visual cortex (V1). HDSN has feedforward hierarchical architecture tuned to goal-driven (top-down) recognition problem. First, we show a discriminant formulation of top-down visual saliency, intrinsically connected to the recognition problem. The formulation is shown to be closely related to a number of classical principles for the organization of perceptual systems, including infomax, inference by detection of suspicious coincidences, classification with minimal uncertainty, and classification with minimum probability of error. The resulting top-down saliency performs effectively as a focus of attention mechanism for the selection of interest points according to their relevance for visual recognition. Experimental results show that state-of-the-art computer vision algorithms works better when top-down saliency is used as preprocessor by pruning interest points. Then, stand alone discriminant saliency network based on discriminant saliency principle is presented. The biological plausibility of building blocks in the network, statistical inference and learning, tuned to the statistics of natural images, is investigated. It is shown that a rich family of statistical decision rules, confidence measures, and risk estimates, can be implemented with the computations attributed to the standard neurophysiological model of V1. In particular, different statistical quantities can be computed through simple rearrangement of lateral divisive connections, non-linearities, and pooling. It is then shown that a number of proposals for the measurement of visual saliency can be implemented in a biologically plausible manner, through such rearrangements. This enables the implementation of biologically plausible feedforward object recognition networks that include explicit saliency models. The potential of combined attention and recognition is illustrated by replacing the first layer of the HMAX architecture with a saliency network. Various saliency measures are compared, to investigate whether 1) saliency can substantially benefit visual recognition, and 2) the benefits depend on the specific saliency mechanisms implemented. Experimental evaluation shows that saliency does indeed enhance recognition, but the gains are not independent of the saliency mechanisms. Best results are obtained with top-down mechanisms that equate saliency to classification confidence. Finally, a novel biologically plausible hierarchical saliency network for visual recognition is proposed. Both of the layers are an optimal top-down saliency module, for the detection of a visual class of interest. The relationships between the proposed saliency network and existing solutions are discussed, for both convolutional network models, and more generic computer vision methods. This leads to some interesting insights, such as a mapping of popular computer vision algorithms to network form into building blocks, which highlights important discrepancies on the evaluation of the two types of approaches and gives a way of evaluating various algorithms in its component level. An extensive experimental evaluation shows that the proposed saliency network outperforms all existing network models, and all computer vision models of comparable parameters for both object localization and classification tasks. We also demonstrate that discriminant saliency network is suitable for amorphous object detection where the object is specified with no defined shape or distinctive edge configurations and automatic detection of region-of-interest for image compression with additional EM type saliency validation process.

Author : Herbert Freeman
Publisher : Elsevier
Page : 329 pages
File Size : 43,99 MB
Release : 2012-12-02
Category : Technology & Engineering
ISBN : 0323155723

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Machine Vision: Algorithms, Architectures, and Systems contains the proceedings of the workshop ""Machine Vision: Where Are We and Where Are We Going?"" sponsored by the Center for Computer Aids for Industrial Productivity (CAIP) at Rutgers University and held in April 1987 in New Brunswick, New Jersey. The papers review the state of the art of machine vision and sets directions for future research. Topics covered include ""smart sensing"" in machine vision, computer architectures for machine vision, and range image segmentation. Comprised of 14 chapters, this book opens with an overview of ""smart sensing"" strategies in machine vision and illustrates how smart sensing may fit into a general purpose vision system by implementing a flexible, modular system called Pipeline Pyramid Machine. The discussion then turns to a hierarchy of local autonomy for processor arrays, focusing on the progression from pure SIMD to complete MIMD as well as the hardware penalties that arise when autonomy is increased. The following chapters explore schemes for integrating vision modules on fine-grained machines; computer architectures for real-time machine vision systems; the application of machine vision to industrial inspection; and characteristics of technologies and social processes that are inhibiting the development and/or evolution of machine vision. Machine vision research at General Motors is also considered. The final chapter assesses future prospects for machine vision and highlights directions for research. This monograph will be a useful resource for practitioners in the fields of computer science and applied mathematics.

Author : Virginio Cantoni
Publisher : Springer Science & Business Media
Page : 348 pages
File Size : 38,55 MB
Release : 2012-12-06
Category : Computers
ISBN : 146152413X

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Computer vision deals with the problem of manipulating information contained in large quantities of sensory data, where raw data emerge from the transducing 6 7 sensors at rates between 10 to 10 pixels per second. Conventional general purpose computers are unable to achieve the computation rates required to op erate in real time or even in near real time, so massively parallel systems have been used since their conception in this important practical application area. The development of massively parallel computers was initially character ized by efforts to reach a speedup factor equal to the number of processing elements (linear scaling assumption). This behavior pattern can nearly be achieved only when there is a perfect match between the computational struc ture or data structure and the system architecture. The theory of hierarchical modular systems (HMSs) has shown that even a small number of hierarchical levels can sizably increase the effectiveness of very large systems. In fact, in the last decade several hierarchical architectures that support capabilities which can overcome performances gained with the assumption of linear scaling have been proposed. Of these architectures, the most commonly considered in com puter vision is the one based on a very large number of processing elements (PEs) embedded in a pyramidal structure. Pyramidal architectures supply the same image at different resolution lev els, thus ensuring the use of the most appropriate resolution for the operation, task, and image at hand.

Author : Simone Frintrop
Publisher : Springer
Page : 219 pages
File Size : 40,53 MB
Release : 2006-03-28
Category : Computers
ISBN : 3540327606

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This monograph presents a complete computational system for visual attention and object detection. VOCUS (Visual Object detection with a Computational attention System) represents a major step forward on integrating data-driven and model-driven information into a single framework. Additionally, the volume contains an extensive review of the literature on visual attention, detailed evaluations of VOCUS in different settings, and applications of the system.

Author : Arun K. Sood
Publisher : Springer Science & Business Media
Page : 747 pages
File Size : 18,66 MB
Release : 2012-12-06
Category : Computers
ISBN : 3642772250

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Intelligent robotics has become the focus of extensive research activity. This effort has been motivated by the wide variety of applications that can benefit from the developments. These applications often involve mobile robots, multiple robots working and interacting in the same work area, and operations in hazardous environments like nuclear power plants. Applications in the consumer and service sectors are also attracting interest. These applications have highlighted the importance of performance, safety, reliability, and fault tolerance. This volume is a selection of papers from a NATO Advanced Study Institute held in July 1989 with a focus on active perception and robot vision. The papers deal with such issues as motion understanding, 3-D data analysis, error minimization, object and environment modeling, object detection and recognition, parallel and real-time vision, and data fusion. The paradigm underlying the papers is that robotic systems require repeated and hierarchical application of the perception-planning-action cycle. The primary focus of the papers is the perception part of the cycle. Issues related to complete implementations are also discussed.