With the aim of combining the main advantages of the virtual laboratories and the collaborative learning environments, a new on-line collaborative environment is proposed in this paper. This environment includes, in a...
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ArTbitrariness is presented here as an initiative of upgrading the esthetical judgment through interactive evolutionary computation techniques and other population based techniques. Computational creativity will be ap...
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This paper proposes a concept of panoramic appearance map to perform reidentification of a people who leave the scene and reappear after some time. The map is a compact signature of appearance information of a person ...
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We propose a new approach to the hand pose estimation problem using only volume information. We describe a thermal and color image-based approach to generate silhouettes of the hand with which voxel images are produce...
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Recently, many global stereo methods have achieved good results by modeling a disparity surface as a Markov random field (MRF) and by solving an optimization problem with various techniques. However, most global metho...
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This paper presents an approach for the registration of multimodal imagery for pedestrian detection when the significant depth differences of objects in the scene precludes a global alignment assumption. Using maximiz...
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In this paper, we propose a fast method for separating reflection components using a single color image. We first propose a specular-free two-band image that is a specularity-invariant color image representation. Refl...
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In this paper, we propose a fast method for separating reflection components using a single color image. We first propose a specular-free two-band image that is a specularity-invariant color image representation. Reflection components separation is achieved by comparing local ratios at each pixel and making those ratios equal in an iterative framework. The proposed method is very fast and shows reasonable results for textured indoor/outdoor images.
We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will u...
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We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal.
With the aim of combining the main advantages of the virtual laboratories and the collaborative learning environments, a new on-line collaborative environment is proposed in this paper. This environment includes, in a...
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With the aim of combining the main advantages of the virtual laboratories and the collaborative learning environments, a new on-line collaborative environment is proposed in this paper. This environment includes, in addition to other common tools, simulation applets in the user interface. To make possible the collaborative learning, the simulations work in a synchronized way for the users of a same group. The main features of the first prototype of the environment and its architecture are described. This prototype has been developed as a module for the popular CMS Moodle.
Driver assistance systems have both the potential to alert the driver to critical situations and distract or annoy the driver if the driver is already aware of the situation. As systems attempt to preemptively warn dr...
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Driver assistance systems have both the potential to alert the driver to critical situations and distract or annoy the driver if the driver is already aware of the situation. As systems attempt to preemptively warn drivers more and more in advance, this problem becomes exacerbated. We present a predictive braking assistance system that identifies not only the need for braking action, but also whether or not a braking action is being planned by the driver. Our system uses a Bayesian framework to determine the criticality of the situation by assessing (1) the probability that braking should be performed given observations of the vehicle and surround and (2) the probability that the driver intends to perform a braking action. We train and evaluate our system using over 22 hours of data collected from real driving scenarios with 28 different drivers
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