Optimal component analysis (OCA) provides a general sub-space formulation that has many applications. Within the framework of linear representations, OCA poses the problem of finding the optimal representations as an ...
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We present a novel 3D gesture recognition scheme that combines the 3D appearance of the hand and the motion dynamics of the gesture to classify manipulative and controlling gestures. Our method does not directly track...
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There are people that are so severely paralyzed that they only have the ability to control the muscles in their eyes. Communication is limited to the interpretation of eye movements. Currently available human-computer...
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Multimedia materials from classrooms and seminars are rich sources of information. Our Virtualized Classroom Project, which is a testbed for integrating novel multimedia software systems, addresses fundamental researc...
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Object detection with a learned classifier has been applied successfully to difficult tasks such as detecting faces and pedestrians. Systems using this approach usually learn the classifier offline with manually label...
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Object detection with a learned classifier has been applied successfully to difficult tasks such as detecting faces and pedestrians. Systems using this approach usually learn the classifier offline with manually labeled training data. We present a framework that learns the classifier online with automatically labeled data for the specific case of detecting moving objects from video. Motion information is used to automatically label training examples collected directly from the live detection task video. An online learner based on the Winnow algorithm incrementally trains a task-specific classifier with these examples. Since learning occurs online and without manual help, it can continue in parallel with detection and adapt the classifier over time. The framework is demonstrated on a person detection task for an office corridor scene. In this task, we use background subtraction to automatically label training examples. After the initial manual effort of implementing the labeling method, the framework runs by itself on the scene video stream to gradually train an accurate detector.
A new algorithm for segmenting a multi-modal grey-scale image is proposed. The image is described as a sample of a joint Gibbs random field of region labels and grey values. To initialize the model, a multi-modal mixe...
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We present a navel structure-enhancing adaptive filter guided by features derived from the Gradient Structure Tensor. We employ this filter to reduce noise in seismic data and to assist in generating seed points for i...
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We present a navel structure-enhancing adaptive filter guided by features derived from the Gradient Structure Tensor. We employ this filter to reduce noise in seismic data and to assist in generating seed points for initializing an automatic horizon picking algorithm. In addition, our algorithm takes seismic attributes into consideration to reduce the possibilities of false horizon generation and fault-crossing. Comparative experimental results are presented to highlight the potential of our approach.
In this paper, a generative model combined with stochastic framework is proposed and applied to the simultaneous correspondence estimation and object segmentation. The correspondence and segment fields are explicitly ...
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This paper addresses the problem of estimating human body pose in static images. This problem is challenging due to the high dimensional state space of body poses, the presence of pose ambiguity, and the need to segme...
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This paper addresses the problem of estimating human body pose in static images. This problem is challenging due to the high dimensional state space of body poses, the presence of pose ambiguity, and the need to segment the human body in an image. We use an image generative approach by modeling the human kinematics, the shape and the clothing probabilistically. These models are used for deriving a good likelihood measure to evaluate samples in the solution space. We adopt a data-driven MCMC framework for searching the solution space efficiently. Our observation data include the face, head-shoulders contour, skin color blobs, and ridges;and they provide evidences on the positions of the head, shoulders and limbs. To translate these inferences into pose hypotheses, we introduce the use of 'proposal maps', which is an efficient way of consolidating the evidence and generating 3D pose candidates during the MCMC search. As experimental results show, the proposed technique estimates the human 3D pose accurately on various test images.
The reliable extraction of characteristic gait features from image sequences and their recognition are two important issues in gait recognition. In this paper, we propose a novel 2-step, model-based approach to gait r...
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