Local gist vision of man-made objects

Attention is usually modelled by sequential fixation of peaks in saliency maps. Those maps code local conspicuity: complexity, colour and texture. Such features have no relation to entire objects, unless also disparity and optical flow are considered, which often segregate entire objects from their background. Recently we developed a model of local gist vision: which types of objects are about where in a scene. This model addresses man-made objects which are dominated by a small shape repertoire: squares, rectangles, trapeziums, triangles, circles and ellipses. Only exploiting local colour contrast, the model can detect these shapes by a small hierarchy of cell layers devoted to low- and mid-level geometry. The model has been tested successfully on video sequences containing traffic signs and other scenes, and partial occlusions were not problematic.

Metal speciation dynamics in colloidal ligand dispersions. Part 3: Lability features of steady-state systems

A lability criterion is developed for dynamic metal binding by colloidal ligands with convective diffusion as the dominant mode of mass transport. Scanned stripping chronopotentiometric measurements of Pb(II) and Cd(II) binding by carboxylated latex core-shell particles were in good agreement with the predicted values. The dynamic features of metal ion binding by these particles illustrate that the conventional approach of assuming a smeared-out homogeneous ligand distribution overestimates the lability of a colloidal ligand system. Due to the nature of the spatial distribution of the binding sites, the change in lability of a metal species with changing ligand concentration depends on whether the ligand concentration is varied via manipulation of the pH (degree of protonation) or via the particle concentration. In the former case the local ligand density varies, whereas in the latter case it is constant. This feature provides a useful diagnostic tool for the presence of geometrically constrained binding sites.

Object segregation and local gist vision using low-level geometry

Multi-scale representations of lines, edges and keypoints on the basis of simple, complex and end-stopped cells can be used for object categorisation and recognition (Rodrigues and du Buf, 2009 BioSystems 95 206-226). These representations are complemented by saliency maps of colour, texture, disparity and motion information, which also serve to model extremely fast gist vision in parallel with object segregation. We present a low-level geometry model based on a single type of self-adjusting grouping cell, with a circular array of dendrites connected to edge cells located at several angles.