Toshev, Alexander

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Now showing 1 - 3 of 3
  • Publication
    An APRIORI-based Method for Frequent Composite Event Discovery in Videos
    (2006-01-01) Toshev, Alexander; Toshev, Alexander; Bremond, Francois; Thonnat, Monique
    We propose a method for discovery of composite events in videos. The algorithm processes a set of primitive events such as simple spatial relations between objects obtained from a tracking system and outputs frequent event patterns which can be interpreted as frequent composite events. We use the APRIORI algorithm from the field of data mining for efficient detection of frequent patterns. We adapt this algorithm to handle temporal uncertainty in the data without losing its computational effectiveness. It is formulated as a generic framework in which the context knowledge is clearly separated from the method in form of a similarity measure for comparison between two video activities and a library of primitive events serving as a basis for the composite events.
  • Publication
    Shape-based object recognition in videos using 3D synthetic object models
    (2009-06-20) Toshev, Alexander; Daniilidis, Kostas; Makadia, Ameesh; Daniilidis, Kostas
    In this paper we address the problem of recognizing moving objects in videos by utilizing synthetic 3D models. We use only the silhouette space of the synthetic models making thus our approach independent of appearance. To deal with the decrease in discriminability in the absence of appearance, we align sequences of object masks from video frames to paths in silhouette space. We extract object silhouettes from video by an integration of feature tracking, motion grouping of tracks, and co-segmentation of successive frames. Subsequently, the object masks from the video are matched to 3D model silhouettes in a robust matching and alignment phase. The result is a matching score for every 3D model to the video, along with a pose alignment of the model to the video. Promising experimental results indicate that a purely shape-based matching scheme driven by synthetic 3D models can be successfully applied for object recognition in videos.
  • Publication
    Shape Representations for Object Recognition
    (2011-05-16) Toshev, Alexander; Toshev, Alexander
    The problem of object recognition has been at the forefront of computer vision research in the last decade. The most successful approaches have used mainly edge- or texture-based representations. The shape of the object outline, albeit widely used for pre-segmented objects, has found limited applicability to the detection problem in real images. The fact that shape is a truly holistic global percept is challenging because background structure and interior object contours can easily clutter a global shape descriptor and render it unusable. Therefore, figure-ground organization, which segments the object of interest and removes the cluttering contours, is of paramount importance. However, purely bottom-up segmentation rarely provides a good object outline suitable for shape-based detection. In this thesis, we study a novel shape representation, called a chordiogram, which allows us to address the above challenges. The chordiogram is a holistic shape descriptor capturing global geometric relationships between object boundaries. Based on the chordiogram, we introduce a boundary structure segmentation model which efficiently integrates region and boundary grouping principles with shape-based matching. This method uses holistic shape for simultaneous object segmentation and detection in highly cluttered scenes. We apply it on established recognition benchmarks and achieve state-of-the art results. Further, we study the applicability of shape for object detection in videos. We show that shape-based representations can be used not only to robustly detect moving objects but also to provide a rough estimate of their pose. For this purpose, we utilize freely available large datasets of 3D synthetic models. Beyond linking shape matching with perceptual grouping, we study the interplay between feature matching and perceptual grouping. We introduce co-salient regions -- coherent, corresponding segments in two or more images -- and describe two algorithms for their detection. Co-salient regions are applied to two problems -- wide-baseline stereo and motion segmentation. In the former problem we show how to estimate correspondences between regions and improve feature matches, while in the latter segments representing same object parts are tracked across multiple frames in a video.