Luminance, colour, viewpoint and border enhanced disparity energy model

The visual cortex is able to extract disparity information through the use of binocular cells. This process is reflected by the Disparity Energy Model, which describes the role and functioning of simple and complex binocular neuron populations, and how they are able to extract disparity. This model uses explicit cell parameters to mathematically determine preferred cell disparities, like spatial frequencies, orientations, binocular phases and receptive field positions. However, the brain cannot access such explicit cell parameters; it must rely on cell responses. In this article, we implemented a trained binocular neuronal population, which encodes disparity information implicitly. This allows the population to learn how to decode disparities, in a similar way to how our visual system could have developed this ability during evolution. At the same time, responses of monocular simple and complex cells can also encode line and edge information, which is useful for refining disparities at object borders. The brain should then be able, starting from a low-level disparity draft, to integrate all information, including colour and viewpoint perspective, in order to propagate better estimates to higher cortical areas.Portuguese Foundation for Science and Technology (FCT); LARSyS FCT [UID/EEA/50009/2013]; EU project NeuroDynamics [FP7-ICT-2009-6, PN: 270247]; FCT project SparseCoding [EXPL/EEI-SII/1982/2013]; FCT PhD grant [SFRH-BD-44941-2008

Ramp edges, Mach bands, and the functional significance of the simple cell assembly

The responses of "complex" simple cells to sharp and blurred ramp edges were studied. These responses are quite similar to those in the case of lines, which implies that phase information cannot be used to discriminate between ramp edges and lines. Furthermore, if the maximum of the modulus is used as a position estimate, a systematic bias toward the ramp side results, and this bias increases with edge blur. In contrast, a local extremum in the real part of the cell responses provides a precise position estimate, even for strongly blurred edges. Possible multiscale detection strategies are discussed in the context of a syntactical visual reconstruction. This is illustrated by an explanation of Mach bands as perceived at trapezoidal edges, including Ratliffs Mach-band cancellation stimulus, and criteria for local probability summation in the prediction of Mach-band detection thresholds are presented. © 1994 Springer-Verlag

Responses of simple cells: events, interferences, and ambiguities

The responses of "complex" simple cells, which are an abstraction of pairs of simple cells having real receptive fields with phases in quadrature, to ideal line and edge patterns (events) are studied. These responses are generalized for events which are convolved with a Gaussian blur function. Normal and abnormal scalings lead to a unified description of the responses of cells with scaled receptive fields to ideal and blurred events. Scale tuning requirements and orientation estimation accuracy are derived. The responses in ambiguous neighbourhoods, where there are two events in the cells' receptive fields, are analyzed, considering both parallel and crossing event combinations. © 1993 Springer-Verlag

Automatic texture segmentation for content-based image retrieval application

In this article, a brief review on texture segmentation is presented, before a novel automatic texture segmentation algorithm is developed. The algorithm is based on a modified discrete wavelet frames and the mean shift algorithm. The proposed technique is tested on a range of textured images including composite texture images, synthetic texture images, real scene images as well as our main source of images, the museum images of various kinds. An extension to the automatic texture segmentation, a texture identifier is also introduced for integration into a retrieval system, providing an excellent approach to content-based image retrieval using texture features