Loading...
6 results
Search Results
Now showing 1 - 6 of 6
- Object segregation and local gist vision using low-level geometryPublication . Martins, J. C.; Rodrigues, J. M. F.; du Buf, J. M. H.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. 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. Different angles between active edge cells allow the grouping cell to detect geometric primitives like corners, bars and blobs. Such primitives forming different configurations can then be grouped to identify more complex geometry, like object shapes, without much additional effort. The speed of the model permits it to be used for fast gist vision, assuming that edge cells respond to transients in colour, texture, disparity and motion. The big advantage of combining this information at a low level is that local (object) gist can be extracted first, ie, which types of objects are about where in a scene, after which global (scene) gist can be processed at a semantic level.
- A disparity energy model improved by line, edge and keypoint correspondencesPublication . Martins, J. C.; Farrajota, Miguel; Lam, Roberto; Rodrigues, J. M. F.; Terzic, Kasim; du Buf, J. M. H.Disparity energy models (DEMs) estimate local depth information on the basis ofVl complex cells. Our recent DEM (Martins et al, 2011 ISSPlT261-266) employs a population code. Once the population's cells have been trained with randorn-dot stereograms, it is applied at all retinotopic positions in the visual field. Despite producing good results in textured regions, the model needs to be made more precise, especially at depth transitions.
- Disparity energy model with keypoint disparity validationPublication . Farrajota, Miguel; Martins, J. C.; Rodrigues, J. M. F.; du Buf, J. M. H.A biological disparity energy model can estimate local depth information by using a population of V1 complex cells. Instead of applying an analytical model which explicitly involves cell parameters like spatial frequency, orientation, binocular phase and position difference, we developed a model which only involves the cells’ responses, such that disparity can be extracted from a population code, using only a set of previously trained cells with random-dot stereograms of uniform disparity. Despite good results in smooth regions, the model needs complementary processing, notably at depth transitions. We therefore introduce a new model to extract disparity at keypoints such as edge junctions, line endings and points with large curvature. Responses of end-stopped cells serve to detect keypoints, and those of simple cells are used to detect orientations of their underlying line and edge structures. Annotated keypoints are then used in the leftright matching process, with a hierarchical, multi-scale tree structure and a saliency map to segregate disparity. By combining both models we can (re)define depth transitions and regions where the disparity energy model is less accurate.
- Local object gist: meaningful shapes and spatial layout at a very early stage of visual processingPublication . Martins, J. C.; Rodrigues, J. M. F.; du Buf, J. M. H.In his introduction, Pinna (2010) quoted one of Wertheimer’s observations: “I stand at the window and see a house, trees, sky. Theoretically I might say there were 327 brightnesses and nuances of color. Do I have ‘327’? No. I have sky, house, and trees.” This seems quite remarkable, for Max Wertheimer, together with Kurt Koffka and Wolfgang Koehler, was a pioneer of Gestalt Theory: perceptual organisation was tackled considering grouping rules of line and edge elements in relation to figure-ground segregation, i.e., a meaningful object (the figure) as perceived against a complex background (the ground). At the lowest level – line and edge elements – Wertheimer (1923) himself formulated grouping principles on the basis of proximity, good continuation, convexity, symmetry and, often forgotten, past experience of the observer. Rubin (1921) formulated rules for figure-ground segregation using surroundedness, size and orientation, but also convexity and symmetry. Almost a century of research into Gestalt later, Pinna and Reeves (2006) introduced the notion of figurality, meant to represent the integrated set of properties of visual objects, from the principles of grouping and figure-ground to the colour and volume of objects with shading. Pinna, in 2010, went one important step further and studied perceptual meaning, i.e., the interpretation of complex figures on the basis of past experience of the observer. Re-establishing a link to Wertheimer’s rule about past experience, he formulated five propositions, three definitions and seven properties on the basis of observations made on graphically manipulated patterns. For example, he introduced the illusion of meaning by comics-like elements suggesting wind, therefore inducing a learned interpretation. His last figure shows a regular array of squares but with irregular positions on the right side. This pile of (ir)regular squares can be interpreted as the result of an earthquake which destroyed part of an apartment block. This is much more intuitive, direct and economic than describing the complexity of the array of squares.
- Local gist vision of man-made objectsPublication . Martins, J. C.; Rodrigues, J. M. F.; du Buf, J. M. H.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.
- Focus of attention and region segregation by low-level geometryPublication . Martins, J. C.; Rodrigues, J. M. F.; du Buf, J. M. H.Research has shown that regions with conspicuous colours are very effective in attracting attention, and that regions with different textures also play an important role. We present a biologically plausible model to obtain a saliency map for Focus-of-Attention (FoA), based on colour and texture boundaries. By applying grouping cells which are devoted to low-level geometry, boundary information can be completed such that segregated regions are obtained. Furthermore, we show that low-level geometry, in addition to rendering filled regions, provides important local cues like corners, bars and blobs for region categorisation. The integration of FoA,region segregation and categorisation is important for developing fast gist vision, i.e., which types of objects are about where in a scene.