title: Proto-object categorisation and local gist vision using low-level spatial features.
abstract: Object categorisation is a research area with significant challenges, especially in conditions with bad lighting, occlusions, different poses and similar objects. This makes systems that rely on precise information unable to perform efficiently, like a robotic arm that needs to know which objects it can reach. We propose a biologically inspired object detection and categorisation framework that relies on robust low-level object shape. Using only edge conspicuity and disparity features for scene figure-ground segregation and object categorisation, a trained neural network classifier can quickly categorise broad object families and consequently bootstrap a low-level scene gist system. We argue that similar processing is possibly located in the parietal pathway leading to the LIP cortex and, via areas V5/MT and MST, providing useful information to the superior colliculus for eye and head control.
authors.affiliation: Vision Laboratory (FCT), LARSyS, Portugal; University of the Algarve, Portugal. Electronic address: jamartins@ualg.pt.
authors.forename: Jaime A
authors.initials: JA
authors.lastname: Martins
authors.affiliation: Vision Laboratory (ISE), LARSyS, Portugal; University of the Algarve, Portugal.
authors.forename: J M F
authors.initials: JM
authors.lastname: Rodrigues
authors.affiliation: Vision Laboratory (FCT), LARSyS, Portugal; University of the Algarve, Portugal.
authors.forename: J M H
authors.initials: JM
authors.lastname: du Buf
mesh_terms: D016208:Databases, Factual; D006801:Humans; D008029:Lighting; D000069550:Machine Learning; D008954:Models, Biological; D016571:Neural Networks, Computer; D012372:ROC Curve; D049490:Systems Biology; D014796:Visual Perception
keywords: 3D; Biological model; Categorisation; Colour; Disparity; Figure-ground; Learning; Neural network; Object; Population coding; Segregation; Stereo vision; Verification; Visual cortex