The Best New Object Recognition Books To Read In 2025

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.

Excerpt from Robotics Research Technical Report: Object Recognition by Affine Invariant Matching. Recent results which are not listed in the above mentioned surveys and are relevant to this paper are [9 While [9-11] concentrate on recognition utilizing points and lines, the approach of [12] is based on affine invariant boundary curvematching. 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.

Anthropomorphic prosthetic hands must resemble human hands concerning their kinematic abilities and significant features such as object recognition on grasp. Implementation of object recognition by prosthetic hands require prior information on the extrinsic structural properties of objects as well as the hand's position and orientation. Object recognition approaches are primarily of two categories: vision and tactile. Vision-based learning methods have dominated the realm of object recognition for robotic and prosthetic hands in the past decades.

Relying only on vision is not sufficient for the perceptual requirements of a prosthetic hand. The human hand is a complex structure with multiple degrees of freedom (DoF), leading to various movements and grasp formations. Acquiring the full range of motion in the fingers and the wrist during prosthetic hand development is critical. Creating such dexterity involves an intense investigation to extract knowledge of motion and joint constraints in the phalanges and wrist bones. The increase of digital information in the current age has elevated the demands of semantically rich annotations for applications shared over the internet. In recent years, the popularity of philosophical knowledge representation methods like ontology to under- stand and utilize relevant domain concepts for problem specifications has escalated. Ontologies are significant in providing a meaningful schema by linking unstructured data. Object recognition during a grasp is an essential attribute in a prosthetic hand, which takes its development closer to its natural counterpart.