Obstacle Avoidance Using Flow Field Divergence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Tracking Strategy for Monocular Depth Inference over Multiple Frames
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
Recursive Filters for Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
The computation of optical flow
ACM Computing Surveys (CSUR)
Real-time quantized optimal flow
Real-Time Imaging - Special issue on computer vision motion analysis
Computational and psychophysical mechanisms of visual coding
Computational and psychophysical mechanisms of visual coding
Determination of Optical Flow and its Discontinuities using Non-Linear Diffusion
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Vision based obstacle avoidance using biological models
Vision based obstacle avoidance using biological models
A mixture model for population codes of Gabor filters
IEEE Transactions on Neural Networks
Expansion segmentation for visual collision detection and estimation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A modified model for the Lobula Giant Movement Detector and its FPGA implementation
Computer Vision and Image Understanding
Adjustable linear models for optic flow based obstacle avoidance
Computer Vision and Image Understanding
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A population coded algorithm, built on established models of motion processing in the primate visual system, computes the time-to-collision of a mobile robot to real-world environmental objects from video imagery. A set of four transformations starts with motion energy, a spatiotemporal frequency based computation of motion features. The following processing stages extract image velocity features similar to, but distinct from, optic flow; "translation驴 features, which account for velocity errors including those resulting from the aperture problem; and finally, estimate the time-to-collision. Biologically motivated population coding distinguishes this approach from previous methods based on optic flow. A comparison of the population coded approach with the popular optic flow algorithm of Lucas and Kanade against three types of approaching objects shows that the proposed method produces more robust time-to-collision information from a real world input stimulus in the presence of the aperture problem and other noise sources. The improved performance comes with increased computational cost, which would ideally be mitigated by special purpose hardware architectures.