Real-time binocular smooth pursuit
International Journal of Computer Vision
Learning, anisotropic diffusion, nonlinear filtering and space-variant vision
Learning, anisotropic diffusion, nonlinear filtering and space-variant vision
A review of biologically motivated space-variant data reduction models for robotic vision
Computer Vision and Image Understanding
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Grid Coverage for Surveillance and Target Location in Distributed Sensor Networks
IEEE Transactions on Computers
Object-based visual attention for computer vision
Artificial Intelligence
Sensor deployment and target localization in distributed sensor networks
ACM Transactions on Embedded Computing Systems (TECS)
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
A Detection Technique for Degraded Face Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Stochastic Local Search for Omnidirectional Catadioptric Stereovision Design
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Computer Vision and Image Understanding
Optimizing parameters of a motion detection system by means of a distributed genetic algorithm
Image and Vision Computing
Estimating gaze direction from low-resolution faces in video
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
A review of log-polar imaging for visual perception in robotics
Robotics and Autonomous Systems
A spatial variant approach for vergence control in complex scenes
Image and Vision Computing
A quantitative comparison of speed and reliability for log-polar mapping techniques
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
Pattern Recognition Letters
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The best parameters defining the geometry of a visual sensor generally depend on the particular visual task the sensor is intended to be used in. However, translating task requirements directly into low-level geometric parameters may be difficult, since deep knowledge of the sensor design is usually required, but end users of a sensor need not necessarily be its designers. A framework is suggested to facilitate this translation by including an intermediate layer, the design criteria, between task requirements and sensor parameters. The proposed framework is illustrated with a log-polar space-variant vision model. The motivation behind using this particular sensor is the observation that, in the literature, little attention has been paid to the proper choice of the sensor parameters, or the lack of justification of the chosen configuration. Sets of general-purpose design criteria and task specifications are provided and discussed. The process of finding the best geometric parameters for a given set of (many and/or mutually conflicting) design criteria is automated with a multi-objective genetic algorithm. Some examples are given demonstrating the feasibility and potential of the approach.