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9780243279500: Automatic Model Builder for Object Recognition (Classic Reprint)

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Excerpt from Automatic Model Builder for Object Recognition

Extracts suitable information from observations. Any observations that yield reliable 3d coordinates can be used as the basis for an automatic model builder. Among the many features that may be used are lines significant to object shape, feature points such as vertices, or identifiable surface patches. Extracted features have three dimensional coordinates, but since the object can be moved arbitrarily between observations a particular feature will have different coordinates in each set of observations.

Uses features to find corresponding regions in pairs of views. Data collection may be structured so it is known which views of an object contain common regions, and views can be matched immediately. Often, it is necessary to determine views which contain overlapping regions. Features, e. G. The perimeter of a paisley-shaped area of different reflectivity, which may be easily identified in several views, should be exploited. Alternatively, relations between features, such as angles and distances between line segments, might be used to create a list of views ordered in probability of overlap, and also information concerning which features of one view are likely to correspond to features in another.

Matches overlapping views successively. A single model is constructed by sequentially matching and transforming the coordinates of many views. For exam ple, suppose that views are made of an American football rotated about its axis 20 or 30 degrees between each observation. (the exact amount of rotation is immaterial, since the model-builder will discover the proper transformation between all sets of observations it uses; similarly the axis need not be constant.) View B may be found to have regions of overlap with views A, C, and D. A good matching order would be B, C, D, A, where the coordinate transformation to bring C into the frame of reference of B would be found, C would be transformed using this, then the transformation needed to bring D into this frame of reference would be found, and so on. The model builder should distinguish between this order and less favorable orders such as B, A, C, D that would be possible if the amount of rotation between observations were small.

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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 Automatic Model Builder for Object Recognition

Extracts suitable information from observations. Any observations that yield reliable 3d coordinates can be used as the basis for an automatic model builder. Among the many features that may be used are lines significant to object shape, feature points such as vertices, or identifiable surface patches. Extracted features have three dimensional coordinates, but since the object can be moved arbitrarily between observations a particular feature will have different coordinates in each set of observations.

Uses features to find corresponding regions in pairs of views. Data collection may be structured so it is known which views of an object contain common regions, and views can be matched immediately. Often, it is necessary to determine views which contain overlapping regions. Features, e. G. The perimeter of a paisley-shaped area of different reflectivity, which may be easily identified in several views, should be exploited. Alternatively, relations between features, such as angles and distances between line segments, might be used to create a list of views ordered in probability of overlap, and also information concerning which features of one view are likely to correspond to features in another.

Matches overlapping views successively. A single model is constructed by sequentially matching and transforming the coordinates of many views. For exam ple, suppose that views are made of an American football rotated about its axis 20 or 30 degrees between each observation. (the exact amount of rotation is immaterial, since the model-builder will discover the proper transformation between all sets of observations it uses; similarly the axis need not be constant.) View B may be found to have regions of overlap with views A, C, and D. A good matching order would be B, C, D, A, where the coordinate transformation to bring C into the frame of reference of B would be found, C would be transformed using this, then the transformation needed to bring D into this frame of reference would be found, and so on. The model builder should distinguish between this order and less favorable orders such as B, A, C, D that would be possible if the amount of rotation between observations were small.

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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9780656331956: Automatic Model Builder for Object Recognition (Classic Reprint)

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ISBN 10:  065633195X ISBN 13:  9780656331956
Verlag: Forgotten Books, 2019
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Paperback. Zustand: New. Print on Demand. This book details the work of the author, a computer scientist, on a revolutionary model builder that can generate a complete three-dimensional model of any given object using only data provided by a sensor and the laws of physics. The model builder does not require any prior knowledge of the object or the relations between multiple views of it, such knowledge being a necessity in previous attempts to create object-recognition systems. The author describes the painstaking work of assembling hardware and software to demonstrate the feasibility of automatic model building using a decorated flowerpot as the subject. The resulting system was able to generate a highly detailed model of the object after examining it from multiple angles, and the entire process from data acquisition to generation of the model took only one minute on a computer from the 1980s, demonstrating the great promise of the technique in the field of robotic vision systems. 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. Bestandsnummer des Verkäufers 9780243279500_0

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