IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks and the bias/variance dilemma
Neural Computation
Design principles of columnar organization in visual cortex
Neural Computation
Learning the Gestalt rule of collinearity from object motion
Neural Computation
Neural Computation
IEEE Transactions on Signal Processing
Editorial: ECOVISION: Challenges in Early-Cognitive Vision
International Journal of Computer Vision
A compact harmonic code for early vision based on anisotropic frequency channels
Computer Vision and Image Understanding
A two-level real-time vision machine combining coarse- and fine-grained parallelism
Journal of Real-Time Image Processing
Nature-inspired framework for measuring visual image resemblance: A near rough set approach
Theoretical Computer Science
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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The goal of this review is to discuss different strategies employed by the visual system to limit data-flow and to focus data processing. These strategies can be hard-wired, like the eccentricity-dependent visual resolution or they can be dynamically changing like mechanisms of visual attention. We will ask to what degree such strategies are also useful in a computer vision context. Specifically we will discuss, how to adapt them to technical systems where the substrate for the computations is vastly different from that in the brain. It will become clear that most algorithmic principles, which are employed by natural visual systems, need to be reformulated to better fit to modern computer architectures. In addition, we will try to show that it is possible to employ multiple strategies in parallel to arrive at a flexible and robust computer vision system based on recurrent feedback loops and using information derived from the statistics of natural images.