Visual Feature Array based Cognitive polygon recognition using the UFEX text categorizer
Barna Reskó, Domonkos Tikk, Hideki Hashimoto, Péter Bárányi · 2006
This paper presents a cognitive vision based approach to recognize polygons on a natural image. The approach is based on the Visual Feature Array (VFA), which is a cognitive computational model of the mammalian primary visual processing. VFA, as a multidimensional orthogonal data structure, contains data about the line segment and vertex features in the edge detected input image. Based on the features available in VFA, using the Universal Feature Extractor classifier (UFEX), the problem of polygon categorization and recognition is addressed. The results are compared to solutions by conventional neural networks, such as the Learning Vector Quantization network.