The Hidden Markov Model (HMM) is a popular statistical framework for modeling and analyzing stochastic signals. In this paper, a novel strategy is proposed that makes use of level-building algorithm with a chain of AdaBoost HMM classifiers to model long stochastic processes. AdaBoost HMM classifier belongs to the class of multiple-HMM classifier. It is specially trained to identify samples with erratic distributions. By connecting the AdaBoost HMM classifiers, processes of arbitrary length can be modeled. A probability trellis is created to store the accumulated probabilities, starting frames and indices of each reference model. By backtracking the trellis, a sequence of best-matched AdaBoost HMM classifiers can be decoded. The proposed method is applied to visual speech processing. A selected number of words and phrases are decomposed into sequences of visual speech units using both the proposed strategy and the conventional level-building on HMM method. Experimental results show that the proposed strategy is able to more accurately decompose words/phrases in visual speech than the conventional approach.

In this paper, a novel approach to speaker recognition is proposed. The approach makes use of adaptive boosting (AdaBoost) and classifiers such as Multilayer Perceptrons (MLP) and C4.5 Decision Trees for closed set, text-dependent speaker recognition. The performance of the systems is assessed using a subset of utterances drawn from the YOHO speaker verification corpus. Experiments show that significant improvement in accuracy can be achieved with the application of adaptive boosting techniques. Results also reveal that an accuracy of 98.8% for speaker identification may be achieved using the adaptively boosted C4.5 system.

In this paper, an automated computer-aided-detection scheme is proposed to identify and locate the suspicious masses in the abnormal breasts from the full mammograms. Mammograms are examined using a four-stage detection method including pre-processing, identification of local maxima, seeded region-growing, and false positive (FP) reduction. This method has been applied to the entire Mammographic Image Analysis Society (MIAS) database of 322 digitized mammograms containing 59 biopsy-proven masses in 56 images. Results of detection show 95% true positive (TP) fraction at 1.9 FPs per image for the 56 images and 1.3 FPs per image for the entire database.

Owing to the large amount of speckle noise and ill-defined edges present in echocardiographic images, computer-based boundary detection of the left ventricle has proved to be a challenging problem. In this paper, a Markovian level set method for boundary detection in long-axis echocardiographic images is proposed. It combines Markov random field (MRF) model, which makes use of local statistics with level set method that handles topological changes, to detect a continuous and smooth boundary. Experimental results show that higher accuracy can be achieved with the proposed method compared with two related MRF-based methods.