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Alan Chauvin

Publications and source records attributed to Alan Chauvin.

6 recordsLinked to original sources

Accurate statistical tests for smooth classification images.

Despite an obvious demand for a variety of statistical tests adapted to classification images, few have been proposed. We argue that two statistical tests based on random field theory (RFT) satisfy this need for smooth classification images. We illustrate these tests on classification images representative of the literature from F. Gosselin and P. G. Schyns (2001) and from A. B. Sekuler, C. M. Gaspar, J. M. Gold, and P. J. Bennett (2004). The necessary computations are performed using the Stat4Ci Matlab toolbox.

Classification↗

The coarse-to-fine hypothesis revisited: evidence from neuro-computational modeling.

The human perceptual system seems to be driven by a coarse-to-fine integration of visual information. Different results have shown a faster integration of low-spatial frequency compared with high-spatial frequency (HSF) information, starting at early retinal processes. The difference in spatial scale decomposition remains throughout the lateral geniculate nucleus (Hubel & Wiesel, 1977) and V1 (Tootell, Silverman, & De Valois, 1981). During the last decade, a debate has emerged concerning the origin of the coarse-to-fine integration. Is it a constant, perceptually driven integration (Parker et al., 1992 and Parker et al., 1996)? Instead, the flexible use hypothesis suggests that different spatial frequency channels could be enhanced depending on the requirement of the task for high-level cognitive processes like categorization (Oliva and Schyns, 1997 and Schyns and Oliva, 1999). In two connectionist simulations, we have shown that global categorization performance could actually be better performed with HSF information when the amount of information is normalized across the different spatial frequency channels. Those results suggest that high-level requirement alone could not explain the coarse-to-fine bias toward LSF information. A hypothesis is proposed concerning the possible implication of the amount of data provided by different spatial frequency channel that might provide the perceptual bias toward LSF information.

Algorithms↗

Improving generalisation skills in a neural network on the basis of neurophysiological data.

The distribution of striate cortex cells exhibits a maximum number of cells tuned to vertical and horizontal orientations (Mansfield, 1974). This was interpreted as an adaptation of the visual system to the presence in the visual environment of greater amounts of vertical and horizontal information compared to information from other orientations (Keil & Cristobal, 2000). The present research confirms that vertical and horizontal orientations are, indeed, present in greater number in natural scenes. After normalization of the amount of information across all orientations, vertical information appeared to be better for bottom-up categorization. We demonstrate this using a connectionist autoassociator model of categorization used elsewhere in simulations of early infant categorization.

Neural Networks, Computer↗

Image phase or amplitude? Rapid scene categorization is an amplitude-based process.

Models of the visual cortex are based on image decomposition according to the Fourier spectrum (amplitude and phase). On one hand, it is commonly believed that phase information is necessary to identify a scene. On the other hand, it is known that complex cells of the visual cortex, the most numerous ones, code only the amplitude spectrum. This raises the question of knowing if these cells carry sufficient information to allow visual scene categorization. In this work, using the same experiments in computer simulation and in psychophysics, we provide arguments to show that the amplitude spectrum alone is sufficient for categorization task.

Fourier Analysis↗

Hemispheric specialization for spatial frequency processing in the analysis of natural scenes.

Experimental data coming from visual cognitive sciences suggest that visual analysis starts with a parallel extraction of different visual attributes at different scales/frequencies. Neuropsychological and functional imagery data have suggested that each hemisphere (at the level of temporo-parietal junctions-TPJ) could play a key role in spatial frequency processing: The right TPJ should predominantly be involved in low spatial frequency (LFs) analysis and the left TPJ in high spatial frequency (HFs) analysis. Nevertheless, this functional hypothesis had been inferred from data obtained when using the hierarchical form paradigm, without any explicit spatial frequency manipulation per se. The aims of this research are (i) to investigate, in healthy subjects, the hemispheric asymmetry hypothesis with an explicit manipulation of spatial frequencies of natural scenes and (ii) to examine whether the 'precedence effect' (the relative rapidity of LFs and HFs processing) depends on the visual field of scene presentation or not. For this purpose, participants were to identify either non-filtered or LFs and HFs filtered target scene displayed either in the left, central, or right visual field. Results showed a hemispheric specialization for spatial frequency processing and different 'precedence effects' depending on the visual field of presentation.

Cognition↗