Abstract
This paper presents a visual cortex inspired cognitive model for feature understanding, integrated into a DIND, which is the basic unit of the Intelligent Space. The model is strongly based on the receptive field characteristics of cortical neurons of the visual cortex. As a step forward compared to the previous version of the model, a new dimension has been added, which replaces the binary signals and operations by operations on real values. The resulting system yields a better approximation of the biological system, as well as provides stronger and more distinct contour lines and vertices. The contour detection and vertex extraction is performed by a vast network of simple units organized in a special structure, the Visual Feature Array (VFA). The goal of the model is to extract abstract information from an image, which in turn is used as input for other models understanding even more abstract visual objects, and thus allowing the Intelligent Space to acquire more abstract knowledge about its internal state.
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