Face recognition by cortical multi-scale line and edge representations

Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. Models of visual perception are based on image representations in cortical area V1 and beyond, which contain many cell layers for feature extraction. Simple, complex and end-stopped cells provide input for line, edge and keypoint detection. Detected events provide a rich, multi-scale object representation, and this representation can be stored in memory in order to identify objects. In this paper, the above context is applied to face recognition. The multi-scale line/edge representation is explored in conjunction with keypoint-based saliency maps for Focus-of-Attention. Recognition rates of up to 96% were achieved by combining frontal and 3/4 views, and recognition was quite robust against partial occlusions.

Face normalization using multi-scale cortical keypoints

Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. Models of visual perception are based on image representations in cortical area V1 and beyond, which contain many cell layers for feature extractions. Simple, complex and end-stopped cells tuned to different spatial frequencies (scales) and/or orientations provide input for line, edge and keypoint detection. This yields a rich, multi-scale object representation that can be stored in memory in order to identify objects. The multi-scale, keypoint-based saliency maps for Focus-of-Attention can be explored to obtain face detection and normalization, after which face recognition can be achieved using the line/edge representation. In this paper, we focus only on face normalization, showing that multi-scale keypoints can be used to construct canonical representations of faces in memory.

Cortical object segregation and categorization by multi-scale line and edge coding

In this paper we present an improved scheme for line and edge detection in cortical area V1, based on responses of simple and complex cells, truly multi-scale with no free parameters. We illustrate the multi-scale representation for visual reconstruction, and show how object segregation can be achieved with coarse-to-finescale groupings. 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. Processing schemes are discussed in the framework of a complete cortical architecture.

Multi-scale keypoint hierarchy for Focus-of-Attention and object detection

Hypercolumns in area V1 contain frequency- and orientation-selective simple and complex cells for line (bar) and edge coding, plus end-stopped cells for key- point (vertex) detection. A single-scale (single-frequency) mathematical model of single and double end-stopped cells on the basis of Gabor filter responses was developed by Heitger et al. (1992 Vision Research 32 963-981). We developed an improved model by stabilising keypoint detection over neighbouring micro- scales.

Looking through the eyes of the painter: from visual perception to non-photorealistic rendering

In this paper we present a brief overview of the processing in the primary visual cortex, the multi-scale line/edge and keypoint representations, and a model of brightness perception. This model, which is being extended from 1D to 2D, is based on a symbolic line and edge interpretation: lines are represented by scaled Gaussians and edges by scaled, Gaussian-windowed error functions. We show that this model, in combination with standard techniques from graphics, provides a very fertile basis for non-photorealistic image rendering.

Building the what and where systems: multi-scale lines, edges and keypoints

Computer vision for realtime applications requires tremendous computational power because all images must be processed from the first to the last pixel. Ac tive vision by probing specific objects on the basis of already acquired context may lead to a significant reduction of processing. This idea is based on a few concepts from our visual cortex (Rensink, Visual Cogn. 7, 17-42, 2000): (1) our physical surround can be seen as memory, i.e. there is no need to construct detailed and complete maps, (2) the bandwidth of the what and where systems is limited, i.e. only one object can be probed at any time, and (3) bottom-up, low-level feature extraction is complemented by top-down hypothesis testing, i.e. there is a rapid convergence of activities in dendritic/axonal connections.

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.

Artistic rendering of the visual cortex

In this paper we explain the processing in the first layers of the visual cortex by simple, complex and endstopped cells, plus grouping cells for line, edge, keypoint and saliency detection. Three visualisations are presented: (a) an integrated scheme that shows activities of simple, complex and end-stopped cells, (b) artistic combinations of selected activity maps that give an impression of global image structure and/or local detail, and (c) NPR on the basis of a 2D brightness model. The cortical image representations offer many possibilities for non-photorealistic rendering.

Visual cortex frontend: integrating lines, edges, keypoints and disparaty

We present a 3D representation that is based on the pro- cessing in the visual cortex by simple, complex and end-stopped cells. We improved multiscale methods for line/edge and keypoint detection, including a method for obtaining vertex structure (i.e. T, L, K etc). We also describe a new disparity model. The latter allows to attribute depth to detected lines, edges and keypoints, i.e., the integration results in a 3D \wire-frame" representation suitable for object recognition.

Fine arts edutainment: the amateur painter

A new scheme for painterly rendering (NPR) has been developed. This scheme is based on visual perception, in particular themulti-scale line/edge representation in the visual cortex. The Amateur Painter (TAP) is the user interface on top of the rendering scheme. It allows to (semi)automatically create paintings from photographs, with different types of brush strokes and colour manipulations. In contrast to similar painting tools, TAP has a set of menus that reflects the procedure followed by a normal painter. In addition, menus and options have been designed such that they are very intuitive, avoiding a jungle of sub-menus with options from image processing that children and laymen do not understand. Our goal is to create a tool that is extremely easy to use, with the possibility that the user becomes interested in painting techniques, styles, and fine arts in general.

A vision fronted with a new disparity model

We are developing a frontend that is based on the image representation in the visual cortex and plausible processing schemes. This frontend consists of multiscale line/edge and keypoint (vertex) detection, using models of simple, complex and end-stopped cells. This frontend is being extended by a new disparity model. Assuming that there is no neural inverse tangent operator, we do not exploit Gabor phase information. Instead, we directly use simple cell (Gabor) responses at positions where lines and edges are detected.

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

Fast and accurate multi-scale keypoints based on end-stopped cells

Increasingly more applications in computer vision employ interest points. Algorithms like SIFT and SURF are all based on partial derivatives of images smoothed with Gaussian filter kemels. These algorithrns are fast and therefore very popular.

A disparity energy model improved by line, edge and keypoint correspondences

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.

A tomada de decisão e o envolvimento no jogo em doentes com Parkinson

Os processos de tomada de decisão na doença de Parkinson (DP) têm sido cada vez mais investigados nos últimos anos e têm estado associados à presença de perturbações de controlo de impulsos, nomeadamente o jogo patológico. De acordo com a literatura estas alterações comportamentais têm estado relacionadas a uma desregulação dopaminérgica nos circuitos ventromediais do córtex pré-frontal. O objetivo deste trabalho consistiu em compreender se os DP têm défices na tomada de decisão comparativamente ao grupo de controlo, estando associado a um risco acrescido destes doentes poderem tornar-se jogadores patológicos. Foram comparados 20 sujeitos com DP e sem demência e 20 indivíduos saudáveis sem doença neurológica, em tarefas de tomada de decisão (Iowa Gambling Task), risco de jogo patológico (South Oak Gambling Screen) e níveis de impulsividade (Barratt Impulsiveness Scale-11). Os resultados revelaram uma diminuição do desempenho na prova de tomada de decisão por parte dos DP e níveis de impulsividade ligeiramente superiores ao do grupo de controlo, particularmente da impulsividade não-planeada. Contudo não foram encontradas diferenças significativas entre grupos quanto ao risco de jogo patológico. De um modo geral, os DP têm dificuldade na tomada de decisão que poderá ser causada por uma disfunção no processamento do feedback emocional da recompensa e/ou punição. No entanto, não demonstraram um risco adicional em adotar condutas aditivas pelo jogo.