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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.
- Segmentação de imagem em três dimensõesPublication . Rodrigues, J. M. F.; du Buf, J. M. H.
- Improved line/edge detection and visual reconstructionPublication . Rodrigues, J. M. F.; du Buf, J. M. H.Lines and edges provide important information for object categorization and recognition. In addition, one brightness model is based on a symbolic interpretation of the cortical multi-scale line/edge representation. In this paper we present an improved scheme for line/edge extraction from simple and complex cells and we illustrate the multi-scale representation. This representation can be used for visual reconstruction, but also for nonphotorealistic rendering. Together with keypoints and a new model of disparity estimation, a 3D wireframe representation of e.g. faces can be obtained in the future.
- Fast segmentation of 3D data using an octreePublication . Rodrigues, J. M. F.; Loke, R. E.; du Buf, J. M. H.The algorithm developed uses an octree pyramid in which noise is reduced at the expense of the spatial resolution. At a certain level an unsupervised clustering without spatial connectivity constraints is applied. After the classification, isolated voxels and insignificant regions are removed by assigning them to their neighbours. The spatial resolution is then increased by the downprojection of the regions, level by level. At each level the uncertainty of the boundary voxels is minimised by a dynamic selection and classification of these, using an adaptive 3D filtering. The algorithm is tested using different data sets, including NMR data.
- 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.
- An integrated framework for combining gist vision with object segregation categorisation and recognitionPublication . Rodrigues, J. M. F.; Almeida, D.; Martins, Jaime; Lam, RobertoThere 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.
- Sistema de reconhecimento de matrículasPublication . Santos, Joaquim; Rodrigues, J. M. F.Introdução: os sistemas de visão para o reconhecimento de matrículas são, actualmente, utilizados em inúmeros casos, onde é necessário fazer a monitorização e controlo de tráfego automóvel [3, 4, 5], tais como controlo de parques de estacionamento, identificação de carros roubados, pagamentos automáticos e gestão de trânsito (entre outros). A difusão deste tipo de sistemas deve-se à facilidade de utilização, fiabilidade e aos diminutos recursos humanos necessários para o seu funcionamento. Este artigo descreve o funcionamento de um sistema que permite sem nenhuma intervenção humana accionar um semáforo (ou uma cancela) de um parque automóvel, utilizando para tal o reconhecimento por visão da matrícula do veículo à entrada do parque e a sua comparação com as matrículas já existentes numa base de dados. O trabalho foi realizado dentro do âmbito da disciplina de projecto da licenciatura bietápica em Engenharia Eléctrica e Electrónica. O sistema baseia-se na aquisição da imagem da parte frontal do carro, onde deve estar incluída a matrícula. Esta aquisição pode ser feita por uma vulgar WebCam, ou através de um conjunto de câmara e frame grabber (placa de aquisição e tratamento de imagem). Neste caso particular utilizou-se uma WebCam. Pela análise dessa imagem, o sistema, localiza a área da matrícula retirando-a da imagem global (segmenta- a). De seguida, analisa a referida área, detectando as zonas onde se encontram os caracteres, derivando assim uma série de novas imagens que são alvo de uma busca e reconhecimento de caracteres, tendo como resultado uma sequência de caracteres que dão origem à matrícula.
- Object segregation and local gist vision using low-level geometryPublication . Martins, J. A.; 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 (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.
- Multi-scale keypoints in V1 and face detectionPublication . Rodrigues, J. M. F.; du Buf, J. M. H.End-stopped cells in cortical area V1, which combine out- puts of complex cells tuned to different orientations, serve to detect line and edge crossings (junctions) and points with a large curvature. In this paper we study the importance of the multi-scale keypoint representa- tion, i.e. retinotopic keypoint maps which are tuned to different spatial frequencies (scale or Level-of-Detail). We show that this representation provides important information for Focus-of-Attention (FoA) and object detection. In particular, we show that hierarchically-structured saliency maps for FoA can be obtained, and that combinations over scales in conjunction with spatial symmetries can lead to face detection through grouping operators that deal with keypoints at the eyes, nose and mouth, especially when non-classical receptive field inhibition is employed. Al- though a face detector can be based on feedforward and feedback loops within area V1, such an operator must be embedded into dorsal and ventral data streams to and from higher areas for obtaining translation-, rotation- and scale-invariant face (object) detection.
- Integrated multi-scale architecture of the cortex with application to computer visionPublication . Rodrigues, J. M. F.; du Buf, J. M. H.The main goal of this thesis is to try to understand the functioning of the visual cortex through the development of computational models. In the input layer V1 of the visual cortex there are simple, complex and endstopped cells. These provide a multi-scale representation of objects and scene in terms of lines, edges and keypoints. In this thesis we combine recent progress concerning the development of computational models of these and other cells with processes in higher cortical areas V2 and V4 etc. Three pertinent challenges are discussed: (i) object recognition embedded in a cortical architecture; (ii) brightness perception, and (iii) painterly rendering based on human vision. Specific aspects are Focusof- Attention by means of keypoint-based saliency maps, the dynamic routing of features from V1 through higher cortical areas in order to obtain translation, rotation and size invariance, and the construction of normalized object templates with canonical views in visual memory. Our simulations show that the multi-scale representations can be integrated into a cortical architecture in order to model subsequent processing steps: from segregation, via different categorization levels, until final object recognition is obtained. As for real cortical processing, the system starts with coarse-scale information, refines categorization by using mediumscale information, and employs all scales in recognition. We also show that a 2D brightness model can be based on the multi-scale symbolic representation of lines and edges, with an additional low-pass channel and nonlinear amplitude transfer functions, such that object recognition and brightness perception are combined processes based on the same information. The brightness model can predict many different effects such as Mach bands, grating induction, the Craik-O’Brien-Cornsweet illusion and brightness induction, i.e. the opposite effects of assimilation (White effect) and simultaneous brightness contrast. Finally, a novel application is introduced: painterly rendering has been linked to computer vision, but we propose to link it to human vision because perception and painting are two processes which are strongly interwoven.