Multi-resolution texture classification based on local image orientation

The aim of this paper is to evaluate quantitatively the discriminative power of the image orientation in the texture classification process. In this regard, we have evaluated the performance of two texture classification schemes where the image orientation is extracted using the partial derivatives of the Gaussian function. Since the texture descriptors are dependent on the observation scale, in this study the main emphasis is placed on the implementation of multi-resolution texture analysis schemes. The experimental results were obtained when the analysed texture descriptors were applied to standard texture databases

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CLASSIFICATION OF IMAGES: ICA FILTERS VS HUMAN PERCEPTION

In this paper we compare a machine based semantic organisation of natural images with the one provided by human perception. On one hand, we have conducted a psychophysical experiment to determine a human perception space in which we have identified semantic categories. These categories and the distances between images are emphasised by analysing the human response similarities with a multidimensional scaling technique called Curvilinear Component Analysis (CCA). On the other hand, we try to perform the same scene categorisation with a computational model based on an ICA filter description. 1

Abstract Representation of images for classification with independent features

In this study, Independent Component Analysis (ICA) is used to compute features extracted from natural images. The use of ICA is justified in the context of classification of natural images for two reasons. On the one hand the model of image suggests that the underlying statistical principles may be the same as those that determine the structure of the visual cortex. As a consequence, the filters that ICA produces are adapted to the statistics of natural images. On the other hand, we adopt a non parametric approach that require density estimation in many dimensions, and independence between features appears as a solution to overthrow the “curse of dimensionality”. Hence we introduce several signatures of natural images that use these feature, and we define some similarity measures that correspond to these signatures. These signatures appear as more and more accurate estimations of densities, and the associated distances as estimations of the Kullback-Leibler divergence between the densities. Efficiency of the couple signature/distance is estimated by a K-nearest neighbour classifier, with a “leave-oneout” procedure for all the signatures we define, and a “bootstrap ” based one for the best results

fMRI Retinotopic Mapping—Step by Step

fMRI retinotopic mapping provides detailed information about the correspondence between the visual field and its cortical representation in the individual subject. Besides providing for the possibility of unambiguously localizing functional imaging data with respect to the functional architecture of the visual system, it is a powerful tool for the investigation of retinotopic properties of visual areas in the healthy and impaired brain. fMRI retinotopic mapping differs conceptually from a more traditional volume-based, block-type, or event-related analysis, in terms of both the surface-based analysis of the data and the phaseencoded paradigm. Several methodological works related to fMRI retinotopic mapping have been published. However, a detailed description of all the methods involved, discussing the steps from stimulus design to the processing of phase data on the surface, is still missing. We describe here step by step our methodology for the complete processing chain. Besides reusing methods proposed by other researchers in the field, we introduce original ones: improved stimuli for the mapping of polar angle retinotopy, a method of assigning volume-based functional data to the surface, and a way of weighting phase information optimally to account for the SNR obtained locally. To assess the robustness of these methods we present a study performed on three subjects, demonstrating the reproducibility of the delineation of low order visual areas. © 2002 Elsevier Science (USA) Key Words: human cortex; vision; visual areas; surface maps; retinotopy; functional magnetic resonance imaging