[PDF] Image Segmentation And Shape Matching For Object Recognition eBook

Author : Yasushi Yagi
Publisher : Springer
Page : 988 pages
File Size : 41,17 MB
Release : 2007-11-14
Category : Computers
ISBN : 3540763864

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This title is part of a two volume set that constitutes the refereed proceedings of the 8th Asian Conference on Computer Vision, ACCV 2007. Coverage in this volume includes shape and texture, face and gesture, camera networks, face/gesture/action detection and recognition, learning, motion and tracking, human pose estimation, matching, face/gesture/action detection and recognition, low level vision and phtometory, motion and tracking, human detection, and segmentation.

Author : David A. Forsyth
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 40,19 MB
Release : 1999-11-03
Category : Computers
ISBN : 3540667229

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Computer vision has been successful in several important applications recently. Vision techniques can now be used to build very good models of buildings from pictures quickly and easily, to overlay operation planning data on a neuros- geon’s view of a patient, and to recognise some of the gestures a user makes to a computer. Object recognition remains a very di cult problem, however. The key questions to understand in recognition seem to be: (1) how objects should be represented and (2) how to manage the line of reasoning that stretches from image data to object identity. An important part of the process of recognition { perhaps, almost all of it { involves assembling bits of image information into helpful groups. There is a wide variety of possible criteria by which these groups could be established { a set of edge points that has a symmetry could be one useful group; others might be a collection of pixels shaded in a particular way, or a set of pixels with coherent colour or texture. Discussing this process of grouping requires a detailed understanding of the relationship between what is seen in the image and what is actually out there in the world.

Author : Valliappa Lakshmanan
Publisher : "O'Reilly Media, Inc."
Page : 481 pages
File Size : 50,93 MB
Release : 2021-07-21
Category : Computers
ISBN : 1098102339

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This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

Author : Kristen Grauman
Publisher : Morgan & Claypool Publishers
Page : 184 pages
File Size : 30,64 MB
Release : 2011
Category : Computers
ISBN : 1598299689

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The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions

Author : Jaechul Kim
Publisher :
Page : 268 pages
File Size : 28,30 MB
Release : 2013
Category :
ISBN :

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In this thesis, I explore region detection and consider its impact on image matching for exemplar-based object recognition. Detecting regions is important to provide semantically meaningful spatial cues in images. Matching establishes similarity between visual entities, which is crucial for recognition. My thesis starts by detecting regions in both local and object level. Then, I leverage geometric cues of the detected regions to improve image matching for the ultimate goal of object recognition. More specifically, my thesis considers four key questions: 1) how can we extract distinctively-shaped local regions that also ensure repeatability for robust matching? 2) how can object-level shape inform bottom-up image segmentation? 3) how should the spatial layout imposed by segmented regions influence image matching for exemplar-based recognition? and 4) how can we exploit regions to improve the accuracy and speed of dense image matching? I propose novel algorithms to tackle these issues, addressing region-based visual perception from low-level local region extraction, to mid-level object segmentation, to high-level region-based matching and recognition. First, I propose a Boundary Preserving Local Region (BPLR) detector to extract local shapes. My approach defines a novel spanning-tree based image representation whose structure reflects shape cues combined from multiple segmentations, which in turn provide multiple initial hypotheses of the object boundaries. Unlike traditional local region detectors that rely on local cues like color and texture, BPLRs explicitly exploit the segmentation that encodes global object shape. Thus, they respect object boundaries more robustly and reduce noisy regions that straddle object boundaries. The resulting detector yields a dense set of local regions that are both distinctive in shape as well as repeatable for robust matching. Second, building on the strength of the BPLR regions, I develop an approach for object-level segmentation. The key insight of the approach is that objects shapes are (at least partially) shared among different object categories--for example, among different animals, among different vehicles, or even among seemingly different objects. This shape sharing phenomenon allows us to use partial shape matching via BPLR-detected regions to predict global object shape of possibly unfamiliar objects in new images. Unlike existing top-down methods, my approach requires no category-specific knowledge on the object to be segmented. In addition, because it relies on exemplar-based matching to generate shape hypotheses, my approach overcomes the viewpoint sensitivity of existing methods by allowing shape exemplars to span arbitrary poses and classes. For the ultimate goal of region-based recognition, not only is it important to detect good regions, but we must also be able to match them reliably. A matching establishes similarity between visual entities (images, objects or scenes), which is fundamental for visual recognition. Thus, in the third major component of this thesis, I explore how to leverage geometric cues of the segmented regions for accurate image matching. To this end, I propose a segmentation-guided local feature matching strategy, in which segmentation suggests spatial layout among the matched local features within each region. To encode such spatial structures, I devise a string representation whose 1D nature enables efficient computation to enforce geometric constraints. The method is applied for exemplar-based object classification to demonstrate the impact of my segmentation-driven matching approach. Finally, building on the idea of regions for geometric regularization in image matching, I consider how a hierarchy of nested image regions can be used to constrain dense image feature matches at multiple scales simultaneously. Moving beyond individual regions, the last part of my thesis studies how to exploit regions' inherent hierarchical structure to improve the image matching. To this end, I propose a deformable spatial pyramid graphical model for image matching. The proposed model considers multiple spatial extents at once--from an entire image to grid cells to every single pixel. The proposed pyramid model strikes a balance between robust regularization by larger spatial supports on the one hand and accurate localization by finer regions on the other. Further, the pyramid model is suitable for fast coarse-to-fine hierarchical optimization. I apply the method to pixel label transfer tasks for semantic image segmentation, improving upon the state-of-the-art in both accuracy and speed. Throughout, I provide extensive evaluations on challenging benchmark datasets, validating the effectiveness of my approach. In contrast to traditional texture-based object recognition, my region-based approach enables to use strong geometric cues such as shape and spatial layout that advance the state-of-the-art of object recognition. Also, I show that regions' inherent hierarchical structure allows fast image matching for scalable recognition. The outcome realizes the promising potential of region-based visual perception. In addition, all my codes for local shape detector, object segmentation, and image matching are publicly available, which I hope will serve as useful new additions for vision researchers' toolbox.

Author : Management Association, Information Resources
Publisher : IGI Global
Page : 1587 pages
File Size : 43,15 MB
Release : 2013-05-31
Category : Computers
ISBN : 1466639954

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Advancements in digital technology continue to expand the image science field through the tools and techniques utilized to process two-dimensional images and videos. Image Processing: Concepts, Methodologies, Tools, and Applications presents a collection of research on this multidisciplinary field and the operation of multi-dimensional signals with systems that range from simple digital circuits to computers. This reference source is essential for researchers, academics, and students in the computer science, computer vision, and electrical engineering fields.

Author : Ching Yee Suen
Publisher : World Scientific
Page : 463 pages
File Size : 10,99 MB
Release : 1989-04-01
Category : Computers
ISBN : 9814525138

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This is an up-to-date volume of selected and expanded papers originating from Vision Interface 88, a conference held in Edmonton, Canada. A broad range of topics are covered-from image processing to hardware design. They include robot vision, biomedical imaging, remote sensing and parallel processing, shape recognition and features, computational methods in vision, and three dimensional vision and application.

Author : Masoumeh Kalantari Khandani
Publisher :
Page : 190 pages
File Size : 40,22 MB
Release : 2010
Category : Cluster analysis
ISBN :

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In this thesis the problem of detecting a known model object in a scene or database of images is addressed. We present two major components of a complete solution for this problem: a data clustering technique for image segmentation and feature extraction, and a shape recognition method. The presented novel data clustering method (Force) relies on the laws of electrostatic fields to find clusters of datapoints in a multiple-dimension space. Application of Force to image segmentation in gray level and color images is described in the thesis. We also show that Force can be successfully used for feature extraction from object images. We present a statistical shape matching method based on Hidden Markov Models (HMM) and then combine its recognition results with the recognition outcome of the Force based algorithm. We show improvement made when Force based features are added to the HMM based approach.