Publisher Summary This chapter focuses on learning and recognizing 3D objects from
multiple views in a neural system. In this approach of designing a 3D object recognition …

Within the frameworks of hierarchical neural feed-forward architectures for performing real-
world 3D invariant object recognition a technique is proposed that shares components like …

In a method and a system for the implementation of multi-layered network object recognition
in multi-dimensional space, the structure of a neural recognition network is dynamically …

The recognition of three-dimensional (3-D) objects from sequences of their two-dimensional
(2-D) views is modeled by a family of self-organizing neural architectures, called VIEWNET …

The recognition of 3-D objects from sequences of their 2-D views is modeled by a neural
architecture, called VIEWNET, that uses View Information Encoded With NETworks …

Payne sional models. Transforms are used to map features of an object to an image seen
from a vantage point and to map features of images best seen from the Vantage points to …

The task of visual object recognition is performed efficiently and effortlessly by humans,
including young children. Simpler animals, such as the pigeon, also exhibit a remarkable …

To form view-invariant representations of objects, neurons in the inferior temporal cortex
may associate together different views of an object, which tend to occur close together in …

This article describes an architecture for the recognition of three-dimensional objects on the
basis of viewer centred representations and temporal associations. Considering evidence …

This report describes the main features of a view-based model of object recognition. The
model does not attempt to account for specific cortical structures; it tries to capture general …