We present a prediction-and-verification segmentation scheme using attention images from multiple fixations. A major advantage of this scheme is that it can handle a large number of different deformable objects presented in complex backgrounds. The scheme is also relatively efficient. The system was tested to segment hands in sequences of intensity images, where each sequence represents a hand sign in American Sign Language. The experimental result showed a 95 percent correct segmentation rate with a 3 percent false rejection rate.

We propose to actively apply content based operation to video data acquisition. Our goal is to develop an automated cameraman to replace the human operator who acquires data in a content sensitive (smart) manner. This auto cameramen is capable of (1) constantly monitoring a global surrounding; (2) automatically keeping track of important visual events; (3) dynamically, based on the detected visual events, determining the video acquisition strategy; and (4) actively generating continuous video clips that are visual events coherent.

In this paper, we present a new approach to extract characters on a license plate of a moving vehicle given a sequence of perspective distortion corrected license plate images. We model the extraction of characters as a Markov random field (MRF). With the MRF modeling, the extraction of characters is formulated as the problem of maximizing the a posteriori probability based on given prior and observations. A genetic algorithm with local greedy mutation operator is employed to optimize the objective function. Experiments and comparison study were conducted. It is shown that our approach provides better performance than other single frame methods.

We present a new approach to extract the license from an image sequence of moving vehicles. The approach includes the following components: 1) license plate localization; 2) feature extraction and tracking; 3) perspective distortion correction; 4) binarization. We model the binarization of characters as a Markov random field (MRF), where the randomness is used to describe the uncertainty in pixel label assignment. With the MRF modeling, the extraction of characters is formulated as the problem of maximizing the a posteriori probability based on given prior and observations. A genetic algorithm with local greedy mutation operator is employed to optimize the objective function based on MRF modeling. In the experiments, we compared our results with other two methods that were evaluated. Our method has demonstrated better performance.

A transitory image sequence is one in which no scene element is visible through the entire sequence. This article deals with some major theoretical and algorithmic issues associated with the task of estimating structure and motion from transitory image sequences. It is shown that integration with a transitory sequence has properties that are very different from those with a nontransitory one. Two representations, world-centered (WC) and camera-centered (CC), behave very differently with a transitory sequence. The asymptotic error rates derived in this article indicate that one representation is significantly superior to the other, depending on whether one needs camera-centered or world-centered estimates. We introduce an efficient "cross-frame" estimation technique for the CC representation. For the WC representation, our analysis indicates that a good technique should be based on camera global pose instead of interframe motions. Rigorous experiments were conducted with real-image sequences taken by a fully calibrated camera system.

This paper presents a prediction-and-verification segmentation scheme wing attention images from multiple fixations. A major advantage of this scheme is that it can handle a large number of different deformable objects presented in complex backgrounds. The scheme is also relatively efficient since the segmentation is guided by the past knowledge through a prediction-and-verification scheme. The system has been tested to segment hands in the sequences of intensity images, where each sequence represents a hand sign. The experimental result showed a 95% correct segmentation rate with a 3% false rejection rate.

In this paper, we have presented a new approach to recognize hand signs. In our approach, motion understanding (the hand movement) is tightly coupled with spatial recognition (hand shape). The system uses the multiclass, multidimensional discriminant analysis to automatically select the most discriminating features for gesture classification. A recursive partition tree approximator is proposed to do classification. This approach combined with our previous work on the hand segmentation forms a new framework which addresses three key aspects of the hand sign interpretation, that is the hand shape, the location, and the movement. The framework has been tested to recognize 28 different hand signs. The experimental results show that the system can achieve a 93.1% recognition rate for test sequences that have not been used in the training phase.

In this paper, we presents a three-stage framework to analyze time-varying image sequences. The focus of this paper is the second stage: segmentation. We propose a prediction-and-verification segmentation scheme which efficiently utilizes the attention images from the multiple fixations. The experimental results show 95% correct segmentation rate with 3% false rejection rate of 805 testing images. The recognition of hand sign based on the segmentation results has shown that the system has achieved a good performance for this very difficult vision task.

We present a self-organizing framework called the SHOSLIF-M for learning and recognizing spatiotemporal events (or patterns) from intensity image sequences. The proposed framework consists of a multiclass, multivariate discriminant analysis to automatically select the most discriminating features (MDF), a space partition tree to achieve a logarithmic retrieval time complexity for a database of n items, and a general interpolation scheme to do view inference and generalization in the MDF space based on a small number of training samples. The system is tested to recognize 28 different hand signs. The experimental results show that the learned system can achieve a 96% recognition rate for test sequences that have not been used in the training phase.>

A transitory image sequence is one in which no scene element is visible through the entire sequence. This article deals with some major theoretical and algorithmic issues associated with the task of estimating structure and motion from transitory image sequences. Two representations, world-centered (WC) and camera-centered (CC), behave very differently with a transitory sequence. The asymptotical error properties derived in this article indicate that one representation is significantly superior to the other, depending on whether one uses camera-centered or world-centered estimates. Rigorous experiments were conducted with real-image sequences taken by a fully calibrated camera system. The comparison demonstrated that a good accuracy can be obtained from transitory image sequences.>