Object categorisations using templates constructed from multi-scale line and edge representations

Object categorisation is linked to detection, segregation and recognition. In the visual system, these processes are achieved in the ventral \what"and dorsal \where"pathways [3], with bottom-up feature extractions in areas V1, V2, V4 and IT (what) in parallel with top-down attention from PP via MT to V2 and V1 (where). The latter is steered by object templates in memory, i.e. in prefrontal cortex with a what component in PF46v and a where component in PF46d.

Multi-scale cortical keypoints for realtime hand tracking and gesture recognition

Human-robot interaction is an interdisciplinary research area which aims at integrating human factors, cognitive psychology and robot technology. The ultimate goal is the development of social robots. These robots are expected to work in human environments, and to understand behavior of persons through gestures and body movements. In this paper we present a biological and realtime framework for detecting and tracking hands. This framework is based on keypoints extracted from cortical V1 end-stopped cells. Detected keypoints and the cells’ responses are used to classify the junction type. By combining annotated keypoints in a hierarchical, multi-scale tree structure, moving and deformable hands can be segregated, their movements can be obtained, and they can be tracked over time. By using hand templates with keypoints at only two scales, a hand’s gestures can be recognized.

An integrated framework for combining gist vision with object segregation categorisation and recognition

There are roughly two processing systems: (1) very fast gist vision of entire scenes, completely bottom-up and data driven, and (2) Focus-of-Attention (FoA) with sequential screening of specific image regions and objects. The latter system has to be sequential because unnormalised input objects must be matched against normalised templates of canonical object views stored in memory, which involves dynamic routing of features in the visual pathways.

Invariant multi-scale object categorisation and recognition

Object recognition requires that templates with canonical views are stored in memory. Such templates must somehow be normalised. In this paper we present a novel method for obtaining 2D translation, rotation and size invariance. Cortical simple, complex and end-stopped cells provide multi-scale maps of lines, edges and keypoints. These maps are combined such that objects are characterised. Dynamic routing in neighbouring neural layers allows feature maps of input objects and stored templates to converge. We illustrate the construction of group templates and the invariance method for object categorisation and recognition in the context of a cortical architecture, which can be applied in computer vision.

Integrated multi-scale architecture of the cortex with application to computer vision

Tese de dout., Engenharia Electrónica e de Computadores, Faculdade de Ciência e Tecnologia, Universidade do Algarve, 2007

An elegant way to increase acidity in SAPOs: use of methylamine as co-template during synthesis

SAPO-34 and SAPO-11 with an increased number of acid sites were synthesized by adding methylamine (MA) as co-template to tetraethylammonium hydroxide (TEAOH) or dipropylamine (DPA) respectively used as true templates.