Elsevier

Vision Research

Volume 39, Issue 11, June 1999, Pages 1963-1989
Vision Research

An optimal estimation approach to visual perception and learning

https://doi.org/10.1016/S0042-6989(98)00279-XGet rights and content
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Abstract

How does the visual system learn an internal model of the external environment? How is this internal model used during visual perception? How are occlusions and background clutter so effortlessly discounted for when recognizing a familiar object? How is a particular object of interest attended to and recognized in the presence of other objects in the field of view? In this paper, we attempt to address these questions from the perspective of Bayesian optimal estimation theory. Using the concept of generative models and the statistical theory of Kalman filtering, we show how static and dynamic events occurring in the visual environment may be learned and recognized given only the input images. We also describe an extension of the Kalman filter model that can handle multiple objects in the field of view. The resulting robust Kalman filter model demonstrates how certain forms of attention can be viewed as an emergent property of the interaction between top–down expectations and bottom–up signals. Experimental results are provided to help demonstrate the ability of such a model to perform robust segmentation and recognition of objects and image sequences in the presence of occlusions and clutter.

Keywords

Visual recognition
Perceptual learning
Attention
Segmentation
Prediction
Kalman filtering

Cited by (0)

This work was performed at the Department of Computer Science, University of Rochester, Rochester, NY 14627, USA.