Download or read book Active 3D Object Recognition Using Geometric Invariants written by Sven Vinther and published by . This book was released on 1994 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Feature Extraction and 2D 3D Object Recognition Using Geometric Invariants written by Yonggen Zhu and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Recognizing 3D Object Using Photometric Invariant written by and published by . This book was released on 1995 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we describe a new efficient algorithm for recognizing 3D objects by combining photometric and geometric invariants. Some photometric properties are derived, that are invariant to the changes of illumination and to relative object motion with respect to the camera and/or the lighting source in 3D space. We argue that conventional color constancy algorithms can not be used in the recognition of 3D objects. Further we show recognition does not require a full constancy of colors, rather, it only needs something that remains unchanged under the varying light conditions and poses of the objects. Combining the derived color invariants and the spatial constraints on the object surfaces, we identify corresponding positions in the model and the data space coordinates, using centroid invariance of corresponding groups of feature positions. Tests are given to show the stabillty and efficiency of our approach to 3D object recognition.
Download or read book Geometric Invariants in Object Recognition written by and published by . This book was released on 1995 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: The project has involved the use of invariants in computer vision and has shown that invariants can greatly improve the efficiency of object recognition. As part of the project, two kinds of invariants have been studied. (1) Geometric invariants, which assume that the geometry of the object is given. The objective here is to use descriptors of the object that are independent of the geometric transformations that complicate object recognition, such as change of the viewpoint from which the object is seen, or small deformations. (2) Physical invariants, which take into account the physical process by which the image is obtained such as irradiation by visible light, infra-red, radar, sonar etc. We borrow methods from physics to apply invariants similar to energy and momentum of the physical process. In these processes there are usually more unknowns than equations. The invariants help in putting additional constraints on the underdetermined set of equations.
Download or read book Computer Vision ECCV 94 written by Jan-Olof Eklundh and published by Springer Science & Business Media. This book was released on 1994-04-20 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision - ECCV'94. -- v. 1
Download or read book Geometric Invariants and Object Recognition written by Isaac Weiss and published by . This book was released on 1992 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geometric invariants are shape descriptors, computed from the geometry of the shape, that remain unchanged under geometric transformations such as changing the viewpoint. Thus they can be matched without search. Deformations of objects are another important class of changes for which invariance is useful."
Download or read book Representations and Techniques for 3D Object Recognition and Scene Interpretation written by Derek Hoiem and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Representations and Techniques for 3D Object Recognition and Scene Interpretation written by Derek Hoiem and published by Morgan & Claypool Publishers. This book was released on 2011-09-09 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Model based 3D Object Recognition Using Invariants written by Manjit Ray and published by . This book was released on 2000 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Feature based Single view 3D Object Recognition in Optical Images Using Invariants written by Manjit Ray and published by . This book was released on 2000 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work seeks to demonstrate how invariants can be employed in the recognition of 3D objects in single optical images. Invariants are functions of object feature configurations that remain unchanged under the action of different transformation groups. Each object is constrained to be one of a pre-defined set of models and the invariant functions are allowed to be non-separable, i.e., involving both model and image features. Even though no true invariant functions exist for the 3D-to-2D projection, it is demonstrated how using non-separable invariant relations allows us to circumvent this restriction. It is also shown how recognition tasks can be reduced to determining intersections between subspaces in a suitably defined canonical frame. In the scenarios considered, image point features (corners) are mapped to lines in a 3D canonical frame that ideally intersect the 3D invariant points generated from the corresponding model points.
Download or read book Integrating 3D and 2D Representations for View Invariant Object Recognition written by Wenze Hu and published by . This book was released on 2012 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents representations and corresponding algorithms which learn models to recognize objects in the full continuous view space. Particularly, we propose to integrate the 3D object-centered representations with 2D viewer-centered representations, which fills in the representation gap between the sparse and simple 3D shapes and their view variant appearances observed as image pixels. Towards this goal, this thesis studies the following models and corresponding algorithms: 1. A mixed model and a pursuit algorithm that integrates 3D object primitives and 2D image primitives according to their information contributions measured as information gains. This proposed measure is consistently used in subsequent models, and also provides a numerical answer to the debates over object-centered representation and viewer-centered representation. 2. A 2D compositional image model and a sum-max data structure which groups the 2D image primitives to represent middle level image structures, such as line segments, curves and corners. This middle level image model can be used to find sparse representations of natural images, and connects the low level 2D image representations to 3D object representations. 3. A 3D hierarchical compositional object model and an AND-OR tree structure which represents a huge number of possible 3D object templates using a limited number of nodes. This AND-OR tree hierarchically quantizes the infinite and continuous space of object geometry and appearance, and decomposes the 3D object representation into 3D panels, whose appearance on images are further decomposed into active curves and the 2D primitives. Though with multiple hierarchies, learning and inference can be done efficiently by dynamic programming, which is essentially composed of layers of sum and max operations.
Download or read book Application of Geometric and Physical Invariants to Object Recognition written by and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The project was a continuation of a previous one, entitled Geometric Invariants in Object Recognition. We have developed and applied new ideas within the framework of the concepts used in the earlier grant. We list here some of the publications that have resulted from this continuation grant along with representative abstracts.
Download or read book Active 3D Modeling Using Perception based Differential geometric Primitives written by Liangyin Yu and published by . This book was released on 1999 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Invariant Object Recognition Based on Elastic Graph Matching written by Raymond S. T. Lee and published by . This book was released on 2003 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Invariant Methods in Discrete and Computational Geometry written by Neil L. White and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Invariant, or coordinate-free methods provide a natural framework for many geometric questions. Invariant Methods in Discrete and Computational Geometry provides a basic introduction to several aspects of invariant theory, including the supersymmetric algebra, the Grassmann-Cayler algebra, and Chow forms. It also presents a number of current research papers on invariant theory and its applications to problems in geometry, such as automated theorem proving and computer vision. Audience: Researchers studying mathematics, computers and robotics.
Download or read book Feature less Single view 3D Object Recognition in Range Images Using Invariants written by Manjit Ray and published by . This book was released on 2000 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work seeks to demonstrate how invariants can be employed in the recognition of objrcts with articulated components in single range images. Invariants are functions of object feature configurations that remain unchanged under the action of different transformation groups. Each object is constrained to be one of a pre-defined set of models. An individual model is represented by an invariant surface in a high-dimensional canonical frame with the surface being compactly described using wavelets. The framework employed does not require the detection of discrete features from the range image and uses scale-space invariants to achieve some degree of resistance to noise and occlusion.
Download or read book Applications of Invariance in Computer Vision written by Joseph L. Mundy and published by Springer Science & Business Media. This book was released on 1994-07-20 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the proceedings of the Second Joint European-US Workshop on Applications of Invariance to Computer Vision, held at Ponta Delgada, Azores, Portugal in October 1993. The book contains 25 carefully refereed papers by distinguished researchers. The papers cover all relevant foundational aspects of geometric and algebraic invariance as well as applications to computer vision, particularly to recovery and reconstruction, object recognition, scene analysis, robotic navigation, and statistical analysis. In total, the collection of papers, together with an introductory survey by the editors, impressively documents that geometry, in its different variants, is the most successful and ubiquitous tool in computer vision.
