Abstract: Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks.

Abstract: This chapter addresses the recognition of basic facial expressions. It has three main contributions. First, the authors introduce a view- and texture independent schemes that exploits facial action parameters estimated by an appearance-based 3D face tracker. they represent the learned facial actions associated with different facial expressions by time series. Two dynamic recognition schemes are proposed: (1) the first is based on conditional predictive models and on an analysis-synthesis scheme, and (2) the second is based on examples allowing straightforward use of machine learning approaches. Second, the authors propose an efficient recognition scheme based on the detection of keyframes in videos. Third, the authors compare the dynamic scheme with a static one based on analyzing individual snapshots and show that in general the former performs better than the latter. The authors then provide evaluations of performance using Linear Discriminant Analysis (LDA), Non parametric Discriminant Analysis (NDA), and Support Vector Machines (SVM).

Abstract: An exportable and robust system for turn control using only camera images is proposed for path execution in robot navigation. Robot motion information is extracted in the form of optical flow from SURF robust descriptors of consecutive frames in the image sequence. This information is used to compute the instantaneous rotation angle. Finally, control loop is closed correcting robot displacements when it is requested for a turn command. The proposed system has been successfully tested on the four-legged Sony Aibo robot.

Abstract: 36.307
Many everyday behavioural uses of color vision depend on color naming ability, which is neither measured nor predicted by most standardized tests of color vision, for either normal or anomalous color vision. Here we demonstrate a new method to quantify color naming ability by deriving a compact computational description of individual 3D color spaces. Methods: Individual observers underwent standardized color vision diagnostic tests (including anomaloscope testing) and a series of custom-made color naming tasks using 500 distinct color samples, either CRT stimuli (“light”-based) or Munsell chips (“surface”-based), with both forced- and free-choice color naming paradigms. For each subject, we defined his/her color solid as the set of 3D convex hulls computed for each basic color category from the relevant collection of categorised points in perceptually uniform CIELAB space. From the parameters of the convex hulls, we derived several indices to characterise the 3D structure of the color solid and its inter-individual variations. Using a reference group of 25 normal trichromats (NT), we defined the degree of normality for the shape, location and overlap of each color region, and the extent of “light”-“surface” agreement. Results: Certain features of color perception emerge from analysis of the average NT color solid, e.g.: (1) the white category is slightly shifted towards blue; and (2) the variability in category border location across NT subjects is asymmetric across color space, with least variability in the blue/green region. Comparisons between individual and average NT indices reveal specific naming “deficits”, e.g.: (1) Category volumes for white, green, brown and grey are expanded for anomalous trichromats and dichromats; and (2) the focal structure of color space is disrupted more in protanopia than other forms of anomalous color vision. The indices both capture the structure of subjective color spaces and allow us to quantify inter-individual differences in color naming ability.

Abstract: The amount of multimodal devices that surround us is growing everyday. In this context, human interaction and communication have become a focus of attention and a hot topic of research. A crucial element in human relations is the evaluation of individuals with respect to facial traits, what is called a first impression. Studies based on appearance have suggested that personality can be expressed by appearance and the observer may use such information to form judgments. In the context of rapid facial evaluation, certain personality traits seem to have a more pronounced effect on the relations and perceptions inside groups. The perception of dominance has been shown to be an active part of social roles at different stages of life, and even play a part in mate selection. The aim of this paper is to study to what extent this information is learnable from the point of view of computer science. Specifically we intend to determine if judgments of dominance can be learned by machine learning techniques. We implement two different descriptors in order to assess this. The first is the histogram of oriented gradients (HOG), and the second is a probabilistic appearance descriptor based on the frequencies of grouped binary tests. State of the art classification rules validate the performance of both descriptors, with respect to the prediction task. Experimental results show that machine learning techniques can predict judgments of dominance rather accurately (accuracies up to 90%) and that the HOG descriptor may characterize appropriately the information necessary for such task.