Phase-based disparity measurement
CVGIP: Image Understanding
Artificial Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Promising directions in active vision
International Journal of Computer Vision
Robot Vision
Vertical and Horizontal Disparities from Phase
ECCV '90 Proceedings of the First European Conference on Computer Vision
A Computational Framework for Determining Stereo Correspondence from a Set of Linear Spatial Filters
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Real-time obstacle avoidance using central flow divergence and peripheral flow
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
The steerable pyramid: a flexible architecture for multi-scale derivative computation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Visual Routines for Autonomous Driving
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Modeling living systems for computer vision
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Motion and color analysis for animat perception
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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We develop a vision system for highly mobile autonomous agents that is capable of dynamic obstacle avoidance. We demonstrate the robust performance of the system in artificial animals with directable, foveated eyes, situated in physics-based virtual worlds. Through active perception, each agent controls its eyes and body by continuously analyzing photorealistic binocular retinal image streams. The vision system computes stereo disparity and segments looming targets in the low-resolution visual periphery while controlling eye movements to track an object fixated in the high-resolution fovea. It matches segmented targets against mental models of colored objects of interest in order to decide whether the segmented objects are harmless or represent dangerous obstacles. The latter are localized, enabling the artificial animal to exercise the sensorimotor control necessary to avoid collision.