Selecting salient objects in real scenes: An oscillatory correlation model

Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real gray-level and color images and the simulation results show the effectiveness of the system. (C) 2010 Elsevier Ltd. All rights reserved.

A network of integrate and fire neurons for visual selection

Biological systems have facility to capture salient object(s) in a given scene, but it is still a difficult task to be accomplished by artificial vision systems. In this paper a visual selection mechanism based on the integrate and fire neural network is proposed. The model not only can discriminate objects in a given visual scene, but also can deliver focus of attention to the salient object. Moreover, it processes a combination of relevant features of an input scene, such as intensity, color, orientation, and the contrast of them. In comparison to other visual selection approaches, this model presents several interesting features. It is able to capture attention of objects in complex forms, including those linearly nonseparable. Moreover, computer simulations show that the model produces results similar to those observed in natural vision systems.

Novel OTOF mutations in Brazilian patients with auditory neuropathy

The OTOF gene encoding otoferlin is associated with auditory neuropathy (AN), a type of non-syndromic deafness. We investigated the contribution of OTOF mutations to AN and to non-syndromic recessive deafness in Brazil. A test for the Q829X mutation was carried out on a sample of 342 unrelated individuals with non-syndromic hearing loss, but none presented this mutation. We selected 48 cases suggestive of autosomal recessive inheritance, plus four familial and seven isolated cases of AN, for genotyping of five microsatellite markers linked to the OTOF gene. The haplotype analysis showed compatibility with linkage in 11 families (including the four families with AN). Samples of the 11 probands from these families and from seven isolated cases of AN were selected for an exon-by-exon screening for mutations in the OTOF gene. Ten different pathogenic variants were detected, among which six are novel. Among the 52 pedigrees with autosomal recessive inheritance (including four familial cases of AN), mutations were identified in 4 (7.7%). Among the 11 probands with AN, seven had at least one pathogenic mutation in the OTOF gene. Mutations in the OTOF gene are frequent causes of AN in Brazil and our results confirm that they are spread worldwide. Journal of Human Genetics (2009) 54, 382-385; doi: 10.1038/jhg.2009.45; published online 22 May 2009

Chaotic synchronization in general network topology for scene segmentation

Chaotic synchronization has been discovered to be an important property of neural activities, which in turn has encouraged many researchers to develop chaotic neural networks for scene and data analysis. In this paper, we study the synchronization role of coupled chaotic oscillators in networks of general topology. Specifically, a rigorous proof is presented to show that a large number of oscillators with arbitrary geometrical connections can be synchronized by providing a sufficiently strong coupling strength. Moreover, the results presented in this paper not only are valid to a wide class of chaotic oscillators, but also cover the parameter mismatch case. Finally, we show how the obtained result can be applied to construct an oscillatory network for scene segmentation.

Chaotic synchronization in 2D lattice for scene segmentation

Synchronization and chaos play important roles in neural activities and have been applied in oscillatory correlation modeling for scene and data analysis. Although it is an extensively studied topic, there are still few results regarding synchrony in locally coupled systems. In this paper we give a rigorous proof to show that large numbers of coupled chaotic oscillators with parameter mismatch in a 2D lattice can be synchronized by providing a sufficiently large coupling strength. We demonstrate how the obtained result can be applied to construct an oscillatory network for scene segmentation. (C) 2007 Elsevier B.V. All rights reserved.