Browsing by Subject "Object recognition"
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Item Covariance based point cloud descriptors for object detection and classification(2013-08) Fehr, Duc AlexandreProcessing 3D point data is of primary interest in many areas of computer vision, including object grasping, robot navigation, and 3D object recognition. The recent introduction of cheap range sensors like the Microsoft Kinect has created a great interest in the computer vision community towards developing efficient algorithms for point cloud processing. Previously, in order to capture a point cloud, expensive specialized sensors, such as lasers or dedicated range imagers, were needed; now, range data is readily available from low-cost sensors which provide easily extractable point clouds from a depth map. From here, an interesting challenge is to find different objects in the point cloud. Various descriptors have been introduced to match features in a point cloud. Cheaper sensors are not necessarily designed to produce precise measurements, which entails that the data is not as accurate as a point cloud provided from a laser or a dedicated range finder. There have been feature descriptors that have been shown to be successful in recognizing objects from point clouds. The aim of this thesis is to introduce techniques from other domains, such as image processing, into the field of 3D point cloud processing in order to improve their rendering, recognition, and classification. Covariances have been proven to be very successful in image processing but other domains as well. This work is a first demonstration of the application of covariances in conjunction with 3D point cloud data.Item Global self-similarity and saliency measures based on sparse representations for classification of objects and spatio-temporal sequences(2012-12) Somasundaram, GuruprasadExtracting the truly salient regions in images is critical for many computer vision applications. Salient regions are considered the most informative regions of an image. Traditionally these salient regions have always been considered as local phenomena in which the salient regions stand out as local extrema with respect to their immediate neighbors. We introduce a novel global saliency metric based on sparse representation in which the regions that are most dissimilar with respect to the entire image are deemed salient. We examine our definition of saliency from the theoretical stand point of sparse representation and minimum description length. Encouraged by the efficacy of our method in modeling foreground objects, we propose two classification methods for recognizing objects in images. First, we introduce two novel global self-similarity descriptors for object representation which can directly be used in any classification framework. Next, we use our salient feature detection approach with conventional region descriptors in a bag-of-features framework. Experimentally we show that our feature detection method enhances the bag-of-features framework. Finally, we extend our salient bag-of-features approach to the spatio-temporal domain for use with three-dimensional dense descriptors. We apply this method successfully to video sequences involving human actions. We obtain state-of-the-art recognition rates in three distinct datasets involving sports and movie actions.