Impairment in preattentive visual processing in patients with Parkinson's disease

We explored the possibility of whether preattentive visual processing is impaired in Parkinson's disease. With this aim, visual discrimination thresholds for orientation texture stimuli were determined in two separate measurement sessions in 16 patients with idiopathic Parkinson's disease. The results were compared with those of 16 control subjects age-matched and 16 young healthy volunteers. Discrimination thresholds were measured in a four-alternative spatial forced-choice paradigm, in which subjects judged the location of a target embedded in a background of distractors. Four different stimulus configurations were employed: (i) a group of vertical targets among horizontal distractors (`vertical line targets'); (ii) targets with varying levels of orientation difference on a background of spatially filtered vertically oriented noise (`Gaussian filtered noise'); (iii) one `L' among 43 `+' signs (`texton'), all of which assess preattentive visual processing; and (iv) control condition, of one `L' among 43 `T' distractors (`non-texton' search target), which reflects attentive visual processing. In two of the preattentive tasks (filtered noise and texton), patients with Parkinson's disease required significantly greater orientation differences and longer stimulus durations, respectively. In contrast, their performance in the vertical line target and non-texton search target was comparable to that of the matched control subjects. These differences were more pronounced in the first compared with the second session. Duration of illness and age within the patient group correlated significantly with test performance. In all conditions tested, the young control subjects performed significantly better than the more elderly control group, further indicating an effect of age on this form of visual processing. The results suggest that, in addition to the well documented impairment in retinal processing, idiopathic Parkinson's disease is associated with a deficit in preattentive cortical visual processing

Biologically Inspired Dynamic Textures for Probing Motion Perception

Perception is often described as a predictive process based on an optimal inference with respect to a generative model. We study here the principled construction of a generative model specifically crafted to probe motion perception. In that context, we first provide an axiomatic, biologically-driven derivation of the model. This model synthesizes random dynamic textures which are defined by stationary Gaussian distributions obtained by the random aggregation of warped patterns. Importantly, we show that this model can equivalently be described as a stochastic partial differential equation. Using this characterization of motion in images, it allows us to recast motion-energy models into a principled Bayesian inference framework. Finally, we apply these textures in order to psychophysically probe speed perception in humans. In this framework, while the likelihood is derived from the generative model, the prior is estimated from the observed results and accounts for the perceptual bias in a principled fashion.Comment: Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), Dec 2015, Montreal, Canad

Geodesics on the manifold of multivariate generalized Gaussian distributions with an application to multicomponent texture discrimination

We consider the Rao geodesic distance (GD) based on the Fisher information as a similarity measure on the manifold of zero-mean multivariate generalized Gaussian distributions (MGGD). The MGGD is shown to be an adequate model for the heavy-tailed wavelet statistics in multicomponent images, such as color or multispectral images. We discuss the estimation of MGGD parameters using various methods. We apply the GD between MGGDs to color texture discrimination in several classification experiments, taking into account the correlation structure between the spectral bands in the wavelet domain. We compare the performance, both in terms of texture discrimination capability and computational load, of the GD and the Kullback-Leibler divergence (KLD). Likewise, both uni- and multivariate generalized Gaussian models are evaluated, characterized by a fixed or a variable shape parameter. The modeling of the interband correlation significantly improves classification efficiency, while the GD is shown to consistently outperform the KLD as a similarity measure

Ambient Sound Provides Supervision for Visual Learning

The sound of crashing waves, the roar of fast-moving cars -- sound conveys important information about the objects in our surroundings. In this work, we show that ambient sounds can be used as a supervisory signal for learning visual models. To demonstrate this, we train a convolutional neural network to predict a statistical summary of the sound associated with a video frame. We show that, through this process, the network learns a representation that conveys information about objects and scenes. We evaluate this representation on several recognition tasks, finding that its performance is comparable to that of other state-of-the-art unsupervised learning methods. Finally, we show through visualizations that the network learns units that are selective to objects that are often associated with characteristic sounds.Comment: ECCV 201

A Compact Representation of Random Phase and Gaussian Textures

In this paper, we are interested in the mathematical analysis of the micro-textures that have the property to be perceptually invariant under the randomization of the phases of their Fourier Transform. We propose a compact representation of these textures by considering a special instance of them: the one that has identically null phases, and we call it ''texton''. We show that this texton has many interesting properties, and in particular it is concentrated around the spatial origin. It appears to be a simple and useful tool for texture analysis and texture synthesis, and its definition can be extended to the case of color micro-textures

Speckle Noise Reduction using Local Binary Pattern

AbstractA novel local binary pattern (LBP) based adaptive diffusion for speckle noise reduction is presented. The LBP operator unifies traditionally divergent statistical and structural models of region analysis. We use LBP textons to classify an image around a pixel into noisy, homogenous, corner and edge regions. According to different types of regions, a variable weight is assigned in to the diffusion equation, so that our algorithm can adaptively encourage strong diffusion in homogenous/noisy regions and less on the edge/corner regions. The diffusion preserves edges, local details while diffusing more on homogenous region. The experiments results are evaluated both in terms of objective metric and the visual quality