How neurons learn to associate 2 D - views in invariant object recognition
نویسنده
چکیده
A local learning rule is shown to be able to account for the association of images together on the basis of temporal order rather than spatial con guration, as described in single cell recording results published by Miyashita (1988). Possible reasons for requiring such learning are then given in the context of invariant object recognition This work was supported by a Fellowship from the Max-Plank Gesellschaftschaft This document is available as /pub/mpi-memos/TR-37.ps via anonymous ftp from ftp.mpik-tueb.mpg.de or from the World Wide Web, http://www.mpik-tueb.mpg.de/projects/TechReport/list.html.
منابع مشابه
Invariant Object Recognition in the Visual System with Novel Views of 3D Objects
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 time under natural viewing conditions. This can be achieved in neuronal network models of this process by using an associative learning rule with a short-term temporal memory trace. It is postulated that within a vi...
متن کاملFast Learning VIEWNET Architectures for Recognizing 3-D Objects from Multiple 2-D Views
The recognition of 3-D objects from sequences of their 2-D views is modeled by a family of self-organizing neural architectures, called VIEWNET, that use View Information Encoded With NETworks. VIEWNET incorporates a preprocessor that generates a compressed but 2-D invariant representation of an image, a supervised incremental learning system that classifies the preprocessed representations int...
متن کاملUsing spatiotemporal correlations to learn topographic maps for invariant object recognition.
The retinal image of visual objects can vary drastically with changes of viewing angle. Nevertheless, our visual system is capable of recognizing objects fairly invariant of viewing angle. Under natural viewing conditions, different views of the same object tend to occur in temporal proximity, thereby generating temporal correlations in the sequence of retinal images. Such spatial and temporal ...
متن کاملHow Lateral Connections and Spiking Dynamics May Separate Multiple Objects Moving Together
Over successive stages, the ventral visual system of the primate brain develops neurons that respond selectively to particular objects or faces with translation, size and view invariance. The powerful neural representations found in Inferotemporal cortex form a remarkably rapid and robust basis for object recognition which belies the difficulties faced by the system when learning in natural vis...
متن کاملSpatiotemporal information during unsupervised learning enhances viewpoint invariant object recognition.
Recognizing objects is difficult because it requires both linking views of an object that can be different and distinguishing objects with similar appearance. Interestingly, people can learn to recognize objects across views in an unsupervised way, without feedback, just from the natural viewing statistics. However, there is intense debate regarding what information during unsupervised learning...
متن کامل