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The design of novel robust image descriptors is still a formidable problem. Different features, with different capabilities, are introduced every year. However, to explore how to combine them is also a fundamental task. This paper proposes two novel strategies for aggregating different featurebased image partitions to tackle the challenging problem of discovering objects in unlabeled image collections...
Most recent category-level object and activity recognition systems work with visual words, i.e., vector-quantized local descriptors. These visual vocabularies are usually built by using a local feature, such as SIFT, and a single clustering algorithm, such as -means. However, very different clusterings algorithms are at our disposal, each of them discovering different structures in the data. In this...
This paper proposes a novel approach to recognize object categories in point clouds. By quantizing 3D SURF local descriptors, computed on partial 3D shapes extracted from the point clouds, a vocabulary of 3D visual words is generated. Using this codebook, we build a Bag-of-Words representation in 3D, which is used in conjunction with a SVM classification machinery. We also introduce the 3D Spatial...
Deformable Part Models (DPMs) as introduced by Felzenszwalb et al. have shown remarkably good results for category-level object detection. In this paper, we explore whether they are also well suited for the related problem of category-level object pose estimation. To this end, we extend the original DPM so as to improve its accuracy in object category pose estimation and design novel and more effective...
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