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- Multi-scale lines and edges in V1 and beyond: brightness, object categorization and recognition, and consciousnessPublication . Rodrigues, J. M. F.; du Buf, J. M. H.In this paper we present an improved model for line and edge detection in cortical area V1. This model is based on responses of simple and complex cells, and it is multi-scale with no free parameters. We illustrate the use of the multi-scale line/edge representation in different processes: visual reconstruction or brightness perception, automatic scale selection and object segregation. A two-level object categorization scenario is tested in which pre-categorization is based on coarse scales only and final categorization on coarse plus fine scales. We also present a multi-scale object and face recognition model. Processing schemes are discussed in the framework of a complete cortical architecture. The fact that brightness perception and object recognition may be based on the same symbolic image representation is an indication that the entire (visual) cortex is involved in consciousness.
- 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 cortical framework for invariant object categorization and recognitionPublication . Rodrigues, J. M. F.; du Buf, J. M. H.In this paper we present a new model for invariant object categorization and recognition. It is based on explicit multi-scale features: lines, edges and keypoints are extracted from responses of simple, complex and endstopped cells in cortical area V1, and keypoints are used to construct saliency maps for Focus-of-Attention. The model is a functional but dichotomous one, because keypoints are employed to model the “where” data stream, with dynamic routing of features from V1 to higher areas to obtain translation, rotation and size invariance, whereas lines and edges are employed in the “what” stream for object categorization and recognition. Furthermore, both the “where” and “what” pathways are dynamic in that information at coarse scales is employed first, after which information at progressively finer scales is added in order to refine the processes, i.e., both the dynamic feature routing and the categorization level. The construction of group and object templates, which are thought to be available in the prefrontal cortex with “what” and “where” components in PF46d and PF46v, is also illustrated. The model was tested in the framework of an integrated and biologically plausible architecture.
- Multi-scale keypoint annotation: a biological approachPublication . Farrajota, Miguel; Rodrigues, J. M. F.; du Buf, J. M. H.The primary visual cortex employs simple, complex and end-stopped cells to create a scale space of 1D singularities (lines and edges) and of 2D singularities (line and edge junctions and crossings called keypoints). In this paper we show first results of a biological model which attributes information of the local image structure to keypoints at all scales, ie junction type (L, T, +) and main line/edge orientations. Keypoint annotation in combination with coarse to fine scale processing facilitates various processes, such as image matching (stereo and optical flow), object segregation and object tracking.
- 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.
- Image morphology: from perception to renderingPublication . Rodrigues, J. M. F.; du Buf, J. M. H.Complete image ontology can be obtained by formalising a top-down meta-language wich must address all possibilities, from global message and composition to objects and local surface properties.
- Correlates of facial expressions in the primary visual cortexPublication . Sousa, R.; Rodrigues, J. M. F.; du Buf, J. M. H.Face detection and recognition should be complemented by recognition of facial expression, for example for social robots which must react to human emotions. Our framework is based on two multi-scale representations in cortical area V1: keypoints at eyes, nose and mouth are grouped for face detection [1]; lines and edges provide information for face recognition [2].