[PDF] Region Based Image Matching For 3d Object Recognition eBook
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3D object recognition is a very difficult and important problem in computer vision, arising in a wide range of applications. Typically in 3D object recognition, interest points are extracted from images and then matched. A shortcoming of this approach is that points only carry local visual information. Therefore, there could be many similar interest points, and these similar points could confuse the matching algorithm. In this thesis, we focus on region-based image matching for 3D object recognition. The main steps that we take are: 1) Extract regions from the image segmentations 2) Represent the objects with the aspect graph of regions 3) Match the given images based on region properties and special layout of regions. Our experiments on a challenging dataset show that we are able to match regions of the objects under 3D transforms. Our main contribution is that while traditional approaches concentrate on matching points, we focus on matching regions of objects. We are the first to show that matching segments of these challenging images is possible under 3D transforms.
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.
One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions
One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions
Rapid development of computer hardware has enabled usage of automatic object recognition in an increasing number of applications, ranging from industrial image processing to medical applications, as well as tasks triggered by the widespread use of the internet. Each area of application has its specific requirements, and consequently these cannot all be tackled appropriately by a single, general-purpose algorithm. This easy-to-read text/reference provides a comprehensive introduction to the field of object recognition (OR). The book presents an overview of the diverse applications for OR and highlights important algorithm classes, presenting representative example algorithms for each class. The presentation of each algorithm describes the basic algorithm flow in detail, complete with graphical illustrations. Pseudocode implementations are also included for many of the methods, and definitions are supplied for terms which may be unfamiliar to the novice reader. Supporting a clear and intuitive tutorial style, the usage of mathematics is kept to a minimum. Topics and features: presents example algorithms covering global approaches, transformation-search-based methods, geometrical model driven methods, 3D object recognition schemes, flexible contour fitting algorithms, and descriptor-based methods; explores each method in its entirety, rather than focusing on individual steps in isolation, with a detailed description of the flow of each algorithm, including graphical illustrations; explains the important concepts at length in a simple-to-understand style, with a minimum usage of mathematics; discusses a broad spectrum of applications, including some examples from commercial products; contains appendices discussing topics related to OR and widely used in the algorithms, (but not at the core of the methods described in the chapters). Practitioners of industrial image processing will find this simple introduction and overview to OR a valuable reference, as will graduate students in computer vision courses. Marco Treiber is a software developer at Siemens Electronics Assembly Systems, Munich, Germany, where he is Technical Lead in Image Processing for the Vision System of SiPlace placement machines, used in SMT assembly.
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.
What is Object Detection The field of computer technology known as object detection is closely associated with computer vision and image processing. Its primary objective is to identify instances of semantic objects belonging to a specific class inside digital images and videos. In the field of object detection, face detection and pedestrian detection are two areas that have received extensive attention. Object detection is useful in a wide variety of computer vision applications, including image retrieval and video surveillance, among others. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Object detection Chapter 2: Computer vision Chapter 3: Image segmentation Chapter 4: Template matching Chapter 5: Optical braille recognition Chapter 6: Deep learning Chapter 7: Convolutional neural network Chapter 8: DeepDream Chapter 9: Saliency map Chapter 10: Small object detection (II) Answering the public top questions about object detection. (III) Real world examples for the usage of object detection in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Object Detection.
The authors survey a recent technique in computer vision called Interactive Co-segmentation, which is the task of simultaneously extracting common foreground objects from multiple related images. They survey several of the algorithms, present underlying common ideas, and give an overview of applications of object co-segmentation.
AbstractImage matching is a central research topic in computer vision which has been mainly focused on optical aspects. The aim of the work presented herein consists in the direct use of geometry to complement optical information in the tasks of 2D matching. First, we focus on global methods based on the calculus of variations. In such methods occlusions and sharp features raise difficult challenges. In these scenarios only the contribution of the regularizer accounts for results. Based on a geometric characterization of this behaviour, we formulate a variational matching method that steers grid lines away from problematic regions. While variational methods provide well behaved results, local methods based on match propagation provide results that adapt closely to varying 3D structures although choppy in nature. Therefore, we present a novel method to propagate matches using local information about surface regularity correcting 3D positions along with corresponding 2D matchings.