Authors:
Francisco J. Diaz-Pernas
;
Mario Martínez-Zarzuela
;
Miriam Anton-Rodriguez
and
David González-Ortega
Affiliation:
University of Valladolid, Spain
Keyword(s):
Computer Vision, Contour Learning, Boundary Detection, Neural Networks, Colour Image Processing, Bio-Inspired Models.
Related
Ontology
Subjects/Areas/Topics:
Image and Video Processing, Compression and Segmentation
;
Multimedia
;
Multimedia Signal Processing
;
Neural Networks, Spiking Systems, Genetic Algorithms and Fuzzy Logic
;
Telecommunications
Abstract:
This work proposes a bio-inspired neural architecture called L-PREEN (Learning and Perceptual boundaRy rEcurrent dEtection neural architecture). L-PREEN has three different feedback interactions that fuse the bottom-up and top-down contour information of visual areas V1-V2-V4-Infero Temporal. This recurrent model uses colour, texture, and diffusive features to generate surface perception and contour learning and recognition processes. We compare the L-PREEN model against other boundary detection methods using the Berkeley Segmentation Dataset and Benchmark (Martin et al., 2001). The results obtained show better performance of L-PREEN using quantitative measures.