Excerpt from Scalable Parallel Geometric Hashing for Hypercube Simd Architectures We begin with an extremely brief summary of the geometric hashing idea. We give a high-level de scription of geometric hashing, then give an example for the case of recognition of dot patterns which may be subjected to translation and obscuration in the scene, but not rotation nor scale transforma tion, and finally present the algorithm for dot patterns subjected to rigid transformation or similarity transformation. Features are extracted ...
Read More
Excerpt from Scalable Parallel Geometric Hashing for Hypercube Simd Architectures We begin with an extremely brief summary of the geometric hashing idea. We give a high-level de scription of geometric hashing, then give an example for the case of recognition of dot patterns which may be subjected to translation and obscuration in the scene, but not rotation nor scale transforma tion, and finally present the algorithm for dot patterns subjected to rigid transformation or similarity transformation. Features are extracted from the image, and a subset is designated as a basis set. The feature values are measured relative (in some sense) to the basis set. The values of the features are used as indices into a hash table, where records are kept of model features that map to the same location with respect to some basis set chosen from the model. Each such hashed feature votes for the set of possible models and basis sets stored at that location in the hash table. An important aspect of the geometric hashing paradigm is that an object is multiply encoded, using many different basis set selections. In this way, as long as a reasonable basis set is chosen in the recognition process, an identification will be made, since the basis set will be one of the many sets used to encode the object. Conceptually, the method can be seen as a sequence of measurements and maps, where each map projects to locations determined from the previous map by a process of fixed links and measured values. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at ... 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.
Read Less
Add this copy of Scalable Parallel Geometric Hashing for Hypercube Simd to cart. $14.12, new condition, Sold by Paperbackshop rated 4.0 out of 5 stars, ships from Bensenville, IL, UNITED STATES, published 2018 by Forgotten Books.