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We present an approach to automatically generating verbal commentaries for tennis games. We introduce a novel application that requires a combination of techniques from computer vision, natural language processing and machine learning. A video sequence is first analysed using state-of-the-art computer vision methods to track the ball, fit the detected edges to the court model, track the players, and...
This paper is concerned with the detection of dim targets in cluttered image sequences. It is an extension of our previous work [7] in which we viewed target detection as an outlier detection problem. In that work the background was modelled by a uni-modal Gaussian. In this paper a Gaussian mixture-model is used to describe the background in which the the number of components is automatically selected...
Motivated by the widespread adoption of social networks and the abundant availability of user-generated multimedia content, our purpose in this work is to investigate how the known principles of active learning for image classification fit in this newly developed context. The process of active learning can be fully automated in this social context by replacing the human oracle with the user tagged...
In this paper, we present a grid structured morphological pattern spectrum based approach for off-line signature verification. The proposed approach has three major phases: preprocessing, feature extraction and verification. In the feature extraction phase, the signature image is partitioned into eight equally sized vertical grids and grid structured morphological pattern spectra for each grid is...
The problem of tracking people using multiple cameras is of much current interest as a means of providing cues for audiovisual blind source separation in dynamic environments. Here we investigate the use of one of the current state-of-the-art techniques in object recognition combined with one of the most popular methods of modelling object motion, particle filters, for tracking people. The dictionary...
We investigate the application of structured output learning (SOL) in automatic annotation of court games. We formulate the problem of event classification in court games as one of learning a mapping from features to structured labels, and employ structured SVM to achieve a max-margin solution. We compare closely the more popular generative approach based on the hidden Markov model (HMM) with our...
An increasing body of evidence suggests that cohort-based score normalization can improve the performance of biometric authentication. This approach relies on the use of cohort biometric templates, which can be computationally expensive. We contribute to the advancement of cohort score normalization in two ways. First, we show both theoretically and empirically that the most similar and the most...
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