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Sinopsis
Excerpt from The Viewpoint Consistency Constraint
In this paper we will examine one of the central constraints provided by prior three dimensional knowledge, which allows us to relate the three-dimensional structure of an object and its components to the two-dimensional spatial structure of its projection in an image. As in other areas of artificial intelligence, the effective application of such a strong constraint leads not only to increased robustness but also to a large reduction in the search space that must be explored during the process of interpretation. The particular constraint that we will be examining can be stated as follows: The viewpoint consistency constraint: The locations of all projected model features in an image must be consistent with projection from a single viewpoint.
The ease of stating this constraint is deceptive. The mathematical and practical problems of implementing it have been such that few model-based vision systems have made full use of the constraint. Some systems have ignored it altogether while others have used loose approximations that discard much of the inherent information content. However, the importance of this constraint for achieving robust recognition can hardly be overstated, and we will argue that it plays a central role in most instances of human visual recogntion. Since the appearance of a three-dimensional object can change completely as it is projected from different viewpoints, any attempt to recognize an object without application of the viewpoint consistency constraint will end up ignoring most of the constraining aspects of an object's spatial structure. Low-level vision has proved unsuccessful at generating stable, unambiguous features that in themselves provide reliable discrimination between object classes. However, low-level vision provides not only the identity of features, such as edges, but also accurate information regarding their location in the image. It is this large quantity of accurate spatial information that can be exploited through application of the viewpoint consistency constraint.
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Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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Excerpt from The Viewpoint Consistency Constraint
In this paper we will examine one of the central constraints provided by prior three dimensional knowledge, which allows us to relate the three-dimensional structure of an object and its components to the two-dimensional spatial structure of its projection in an image. As in other areas of artificial intelligence, the effective application of such a strong constraint leads not only to increased robustness but also to a large reduction in the search space that must be explored during the process of interpretation. The particular constraint that we will be examining can be stated as follows: The viewpoint consistency constraint: The locations of all projected model features in an image must be consistent with projection from a single viewpoint.
The ease of stating this constraint is deceptive. The mathematical and practical problems of implementing it have been such that few model-based vision systems have made full use of the constraint. Some systems have ignored it altogether while others have used loose approximations that discard much of the inherent information content. However, the importance of this constraint for achieving robust recognition can hardly be overstated, and we will argue that it plays a central role in most instances of human visual recogntion. Since the appearance of a three-dimensional object can change completely as it is projected from different viewpoints, any attempt to recognize an object without application of the viewpoint consistency constraint will end up ignoring most of the constraining aspects of an object's spatial structure. Low-level vision has proved unsuccessful at generating stable, unambiguous features that in themselves provide reliable discrimination between object classes. However, low-level vision provides not only the identity of features, such as edges, but also accurate information regarding their location in the image. It is this large quantity of accurate spatial information that can be exploited through application of the viewpoint consistency constraint.
About the Publisher
Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Reseña del editor
Excerpt from The Viewpoint Consistency Constraint
The viewpoint consistency constraint: The locations of all projected model features in an image must be consistent with projection from a single viewpoint.
The ease of stating this constraint is deceptive. The mathematical and practical problems of implementing it have been such that few model-based vision systems have made full use of the constraint. Some systems have ignored it altogether while others have used loose approximations that discard much of the inherent information content. However, the importance of this constraint for achieving robust recognition can hardly be overstated, and we will argue that it plays a central role in most instances of human visual recogntion. Since the appearance of a three-dimensional object can change completely as it is projected from different viewpoints, any attempt to recognize an object without application of the viewpoint consistency constraint will end up ignoring most of the constraining aspects of an object's spatial structure. Low-level vision has proved unsuccessful at generating stable, unambiguous features that in themselves provide reliable discrimination between object classes. However, low-level vision provides not only the identity of features, such as edges, but also accurate information regarding their location in the image. It is this large quantity of accurate spatial information that can be exploited through application of the viewpoint consistency constraint.
A second area of bottom-up image analysis has focussed upon region description, making use of properties such as color and texture. But once again, in themselves these region descriptions are likely to be of little use without a spatial mapping of the object model to the image that specifies which regions are in correspondence to specific surfaces of the object. Thus, spatial correspondence is often prerequisite to other forms of visual matching that are not explicitly spatial themselves.
One argument that is sometimes advanced against the use of precise spatial corre spondence is that many objects are non-rigid with internal degrees of freedom and variable dimensions. It is also clear that human vision has a remarkable capability for recognizingdistorted images and drawings. However, advances will be made on these important prob lems only by explicitly representing the possible degrees of freedom and distortions that are present in a situation. Our knowledge of the visual appearance of objects includes a large amount of information on internal degrees of freedom in their shape and visual properties, as well as potential transformations in the image domain itself. To simply discard all of the available spatial information because some of it is not fully constrained would result in the loss of a large portion of our most useful visual knowledge. It is true, for example, that human vision can identify a person from a highly non-veridical cartoon drawing, yet any amateur artist knows that this is an entirely different proposition from stating that recognition could occur after arbitrary spatial transformations of the image.
About the Publisher
Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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The Viewpoint Consistency Constraint (Classic Reprint)
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Paperback. Condición: New. Print on Demand. This book explores the concept of viewpoint consistency constraint in the field of computer vision. The viewpoint consistency constraint emphasizes the need for spatial correspondence between the 2D image and the 3D object model. Through the application of this constraint, the author argues that computational models can achieve robust 3D object recognition from single gray-scale images. The book delves into the history of the viewpoint consistency constraint, tracing its evolution from early attempts in the field of robotics to its current applications in model-based recognition. The author provides a detailed examination of various techniques for applying the constraint, including methods for solving viewpoint parameters and extending preliminary matches. This book is a valuable resource for computer vision researchers and practitioners interested in model-based recognition. It offers a comprehensive overview of the viewpoint consistency constraint, along with practical techniques for its implementation. The author's insights will help shape the future of computer vision research, enabling more accurate and robust object recognition. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item. Nº de ref. del artículo: 9781333492663_0
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The Viewpoint Consistency Constraint Classic Reprint
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