2014/07/02 by Giacomo Cocci, Cocci, Giacomo, Davide Barbieri +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Neuroscience · Psychology · #Advanced Vision and Imaging #Artificial intelligence #Cluster analysis #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Dimensionality reduction #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Neuroscience #Object (grammar) #Pattern recognition (psychology) #Perception #Psychology #Segmentation #Visual perception and processing mechanisms #Visual processing #Visual space #cs.CV #cs.NE #q-bio.NC #stat.ML
paper · pdf · open access · doi:10.48550/arxiv.1407.0733
published in LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (LA Referencia)
openalex publication_date 2014/07/02 · arxiv created 2014/10/03 · arxiv updated 2014/10/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The visual systems of many mammals, including humans, is able to integrate the geometric information of visual stimuli and to perform cognitive tasks already at the first stages of the cortical processing. This is thought to be the result of a combination of mechanisms, which include feature extraction at single cell level and geometric processing by means of cells connectivity. We present a geometric model of such connectivities in the space of detected features associated to spatio-temporal visual stimuli, and show how they can be used to obtain low-level object segmentation. The main idea is that of defining a spectral clustering procedure with anisotropic affinities over datasets consisting of embeddings of the visual stimuli into higher dimensional spaces. Neural plausibility of the proposed arguments will be discussed.