Representations and Techniques for 3D Object Recognition and Scene Interpretation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Representations and Techniques for 3D Object Recognition and Scene Interpretation write by Derek Hoiem. This book was released on 2011. Representations and Techniques for 3D Object Recognition and Scene Interpretation available in PDF, EPUB and Kindle. 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
Representations and Techniques for 3D Object Recognition and Scene Interpretation
Representations and Techniques for 3D Object Recognition and Scene Interpretation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Representations and Techniques for 3D Object Recognition and Scene Interpretation write by Derek Santhanam. This book was released on 2022-05-31. Representations and Techniques for 3D Object Recognition and Scene Interpretation available in PDF, EPUB and Kindle. 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
From Shape-based Object Recognition and Discovery to 3D Scene Interpretation
From Shape-based Object Recognition and Discovery to 3D Scene Interpretation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook From Shape-based Object Recognition and Discovery to 3D Scene Interpretation write by Nadia Payet. This book was released on 2011. From Shape-based Object Recognition and Discovery to 3D Scene Interpretation available in PDF, EPUB and Kindle. This dissertation addresses a number of inter-related and fundamental problems in computer vision. Specifically, we address object discovery, recognition, segmentation, and 3D pose estimation in images, as well as 3D scene reconstruction and scene interpretation. The key ideas behind our approaches include using shape as a basic object feature, and using structured prediction modeling paradigms for representing objects and scenes. In this work, we make a number of new contributions both in computer vision and machine learning. We address the vision problems of shape matching, shape-based mining of objects in arbitrary image collections, context-aware object recognition, monocular estimation of 3D object poses, and monocular 3D scene reconstruction using shape from texture. Our work on shape-based object discovery is the first to show that meaningful objects can be extracted from a collection of arbitrary images, without any human supervision, by shape matching. We also show that a spatial repetition of objects in images (e.g., windows on a building facade, or cars lined up along a street) can be used for 3D scene reconstruction from a single image. The aforementioned topics have never been addressed in the literature. The dissertation also presents new algorithms and object representations for the aforementioned vision problems. We fuse two traditionally different modeling paradigms Conditional Random Fields (CRF) and Random Forests (RF) into a unified framework, referred to as (RF)^2. We also derive theoretical error bounds of estimating distribution ratios by a two-class RF, which is then used to derive the theoretical performance bounds of a two-class (RF)^2. Thorough experimental evaluation of individual aspects of all our approaches is presented. In general, the experiments demonstrate that we outperform the state of the art on the benchmark datasets, without increasing complexity and supervision in training.
3D Shape Analysis
3D Shape Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook 3D Shape Analysis write by Hamid Laga. This book was released on 2018-12-14. 3D Shape Analysis available in PDF, EPUB and Kindle. An in-depth description of the state-of-the-art of 3D shape analysis techniques and their applications This book discusses the different topics that come under the title of "3D shape analysis". It covers the theoretical foundations and the major solutions that have been presented in the literature. It also establishes links between solutions proposed by different communities that studied 3D shape, such as mathematics and statistics, medical imaging, computer vision, and computer graphics. The first part of 3D Shape Analysis: Fundamentals, Theory, and Applications provides a review of the background concepts such as methods for the acquisition and representation of 3D geometries, and the fundamentals of geometry and topology. It specifically covers stereo matching, structured light, and intrinsic vs. extrinsic properties of shape. Parts 2 and 3 present a range of mathematical and algorithmic tools (which are used for e.g., global descriptors, keypoint detectors, local feature descriptors, and algorithms) that are commonly used for the detection, registration, recognition, classification, and retrieval of 3D objects. Both also place strong emphasis on recent techniques motivated by the spread of commodity devices for 3D acquisition. Part 4 demonstrates the use of these techniques in a selection of 3D shape analysis applications. It covers 3D face recognition, object recognition in 3D scenes, and 3D shape retrieval. It also discusses examples of semantic applications and cross domain 3D retrieval, i.e. how to retrieve 3D models using various types of modalities, e.g. sketches and/or images. The book concludes with a summary of the main ideas and discussions of the future trends. 3D Shape Analysis: Fundamentals, Theory, and Applications is an excellent reference for graduate students, researchers, and professionals in different fields of mathematics, computer science, and engineering. It is also ideal for courses in computer vision and computer graphics, as well as for those seeking 3D industrial/commercial solutions.
Analyzing 3D Objects in 2D Images
Analyzing 3D Objects in 2D Images - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Analyzing 3D Objects in 2D Images write by Mohsen Hejratin. This book was released on 2015. Analyzing 3D Objects in 2D Images available in PDF, EPUB and Kindle. Robots are mechanically capable of doing many tasks, carrying loads, precisely manipulating objects, picking and packing or collaborating with humans. However, they require accurate 3D perception of objects and surrounding environment to do these tasks autonomously. Traditional methods build 3D representation of the scene using structure from motion techniques or depth sensors, while more recent approaches use statistical models to learn geometry and appearance of 3D objects and scenes. This thesis investigates approaches to represent, learn and analyze 3D objects in natural images. We first propose two new methods for 3D object recognition and pose estimation in single 2D images. Second, we study various geometric representations for the novel task of primitive 3D shape categorization. We propose two novel approaches for recognizing 3D objects: (1) Aligning a 3D model to detected 2D landmarks, where we propose a novel method based on deformable-part models to propose candidate detections and 2D estimates of shape, then these estimates are refined by using an explicit 3D model of shape and viewpoint. (2) An analysis by synthesis approach where a forward synthesis model constructs possible geometric interpretations of the world, and then selects the interpretation that best agrees with the measured visual evidence. We show state of the art performance for detection and pose estimation on two challenging 3D object recognition datasets of cars and cuboids. 3D object recognition methods focus on modeling 3D shape of the objects, however, many objects may have similar 3D shape (washing machines, cabinets and microwave are all cuboidal), thus recognizing them require reasoning about appearance and geometry at the same time. The natural approach for recognition might extract pose-normalized appearance features. Though such approaches are extraordinarily common in the literature, in this thesis we demonstrate that they are {\em not optimal}. Instead, we introduce methods based on pose-synthesis, a somewhat simple approach of augmenting training data with geometrically perturbed training samples. We demonstrate that synthesis is a surprisingly simple but effective strategy that allows for state-of-the-art categorization and automatic 3D alignment.
