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A key development in the design of visual object recognition systems is the combination of multiple features. In recent years, various popular optimization based feature combination methods have been proposed in the literatures. However, those methods obtain tiny performance improvement at the cost of enormous computation consumption. In this paper, we propose an improved averaging combination (IAC)...
Despite many years of research, pedestrian recognition is still a difficult, but very important task. We present a multi-modality approach, that combines features extracted from three type of images: intensity, depth and flow. For the feature extraction phase we use Kernel Descriptors, which are optimised independently on each type of image, and for the learning phase we use Support Vector Machines...
Recently, increasing interest has been brought to improve image categorization performances by combining multiple descriptors. However, very few approaches have been proposed for combining features based on complementary aspects, and evaluating the performances in realistic databases. In this paper, we tackle the problem of combining different feature types (edge and color), and evaluate the performance...
Winder et al. have recently shown the superiority of the DAISY descriptor in comparison to other widely extended descriptors such as SIFT and SURF. Motivated by those results, we present a novel algorithm that extracts viewpoint and illumination invariant keypoints and describes them with a particular implementation of a DAISY-like layout. We demonstrate how to efficiently compute the scale-space...
High-quality cameras are a standard feature of mobile platforms, but the computational capabilities of mobile processors limit the applications capable of exploiting them. Emerging mobile application domains, for example mobile augmented reality (MAR), rely heavily on techniques from computer vision, requiring sophisticated analyses of images followed by higher-level processing. An important class...
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