Face segregation and recognition by cortical multi-scale line and edge coding

Models of visual perception are based on image representations in cortical area V1 and higher areas which contain many cell layers for feature extraction. Basic simple, complex and end-stopped cells provide input for line, edge and keypoint detection. In this paper we present an improved method for multi-scale line/edge detection based on simple and complex cells. We illustrate the line/edge representation for object reconstruction, and we present models for multi-scale face (object) segregation and recognition that can be embedded into feedforward dorsal and ventral data streams (the “what” and “where” subsystems) with feedback streams from higher areas for obtaining translation, rotation and scale invariance.

An integrated framework for combining gist vision with object segregation categorisation and recognition

There 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.

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.

Region segregation and saliency using colour information

Saliency maps determine the likelihood that we focus on interesting areas of scenes or images. These maps can be built using several low-level image features, one of which having a particular relevance: colour. In this paper we present a new computational model, based only on colour features, which provides a sound basis for saliency maps for static images and video, plus region segregation and cues for local gist vision.

Absorption coefficient of particulate matter on the south-west coast of Europe: a contribution to the validation of the MERIS-Medium Resolution Imaging Spectrometer sensor

Dissolved oxygen (DO) is one of the most important environmental variables of water quality, especially for marine life. Consequently, oxygen is one of the Chemical Quality Elements required for the implementation of European Union Water Framework Directive. This study uses the example of the Ria Formosa, a meso-tidal lagoon on the south coast of Portugal to demonstrate how monitoring of water quality for coastal waters must be well designed to identify symptoms of episodic hypoxia. New data from the western end of the Ria Formosa were compared to values in a database of historical data and in the published literature to identify long-term trends. The dissolved oxygen concentration values in the database and in the literature were generally higher than those found in this study, where episodic hypoxia was observed during the summer. Analysis of the database showed that the discrepancy was probably related with the time and the sites where the samples had been collected, rather than a long-term trend. The most problematic situations were within the inner lagoon near the city of Faro, where episodic hypoxia (<2 mg dm3 DO) occurred regularly in the early morning. These results emphasise the need for a balanced sampling strategy for oxygen monitoring which includes all periods of the day and night, as well as a representative range of sites throughout the lagoon. Such a strategy would provide adequate data to apply management measures to reduce the risk of more persistent hypoxia that would impact on the ecological, important natural resource. economic and leisure uses of this important natural resource.

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.