International Journal of Pattern Recognition and Artificial Intelligence

Breast cancer continues to be the most common cause of cancer deaths in women. Early detection of breast cancer is significant for better prognosis. Digital Mammography currently offers the best control strategy for the early detection of breast cancer. The research work in this paper investigates the significance of neural-association of microcalcification patterns for their reliable classification in digital mammograms. The proposed technique explores the auto-associative abilities of a neural network approach to regenerate the composite of its learned patterns most consistent with the new information, thus the regenerated patterns can uniquely signify each input class and improve the overall classification. Two types of features: computer extracted (gray level based statistical) features and human extracted (radiologists' interpretation) features are used for the classification of calcification type of breast abnormalities. The proposed technique attained the highest 90.5% classification rate on the calcification testing dataset.

Self-organizing feature maps (SOFM) can learn both the distribution and topology of the input vectors they are trained on. According to this characteristic, we construct neural networks with a family of self-organizing feature maps to cluster the input data space. The proposed algorithm in this paper defines a novel similarity measure, topological similarity, and employs some new concepts, such as SOFM family, UsageFactor. The clustering algorithm handles the clusters with arbitrary shapes and avoid the limitations of the conventional clustering algorithms. We conclude our paper by several experiments with synthetic and standard data set of different characteristics, which show good performance of the proposed algorithm.

Data clustering has been widely used in many areas, such as data mining, statistics, machine learning and so on. A variety of clustering approaches have been proposed so far, but most of them are not qualified to quickly cluster a large-scale high-dimensional database. This paper is devoted to a novel data clustering approach based on a generalized particle model (GPM). The GPM transforms the data clustering process into a stochastic process over the configuration space on a GPM array. The proposed approach is characterized by the self-organizing clustering and many advantages in terms of the insensitivity to noise, quality robustness to clustered data, suitability for high-dimensional and massive data sets, learning ability, openness and easier hardware implementation with the VLSI systolic technology. The analysis and simulations have shown the effectiveness and good performance of the proposed GPM approach to data clustering.

In this paper, we present a real time lip-synch system that activates 2-D avatar's lip motion in synch with incoming speech utterance. To achieve the real time operation of the system, the processing time was minimized by "merge and split" procedures resulting in coarse-to-fine phoneme classification. At each stage of phoneme classification, the support vector machine (SVM) method was applied to reduce the computational load while maintaining the desired accuracy. The coarse-to-fine phoneme classification, is accomplished via two_stages of feature extraction: in the first stage, each speech frame is acoustically analyzed for three classes of lip opening using Mel Frequency Cepstral Coefficients (MFCC) as a feature; in the second stage, each frame is further refined for detailed lip shape using formant information. The method was implemented in 2-D lip animation and it was demonstrated that the system was effective in accomplishing real-time lip-synch. This approach was tested on a PC using the Microsoft Visual Studio with an Intel Pentium IV 1.4 Giga Hz CPU and 384 MB RAM. It was observed that the methods of phoneme merging and SVM achieved about twice the speed in recognition than the method employing the Hidden Markov Model (HMM). A typical latency time per a single frame observed using the proposed method was in the order of 18.22 milliseconds while an HMM method under identical conditions resulted about 30.67 milliseconds.

With the advance of semiconductor technology, the current mobile devices support multimodal input and multimedia output. In turn, human computer communication applications can be developed in mobile devices such as mobile phone and PDA. This paper addresses the research issues of face and eye detection on mobile devices. The major obstacles that we need to overcome are the relatively low processor speed, low storage memory and low image (CMOS senor) quality. To solve these problems, this paper proposes a novel and efficient method for face and eye detection. The proposed method is based on color information because the computation time is small. However, the color information is sensitive to the illumination changes. In view of this limitation, this paper proposes an adaptive Illumination Insensitive (AI²) Algorithm, which dynamically calculates the skin color region based on an image color distribution. Moreover, to solve the strong sunlight effect, which turns the skin color pixel into saturation, a dual-color-space model is also developed. Based on AI²algorithm and face boundary information, face region is located. The eye detection method is based on an average integral of density, projection techniques and Gabor filters. To quantitatively evaluate the performance of the face and eye detection, a new metric is proposed. 2158 head & shoulder images captured under uncontrolled indoor and outdoor lighting conditions are used for evaluation. The accuracy in face detection and eye detection are 98% and 97% respectively. Moreover, the average computation time of one image using Matlab code in Pentium III 700MHz computer is less than 15 seconds. The computational time will be reduced to tens hundreds of millisecond (ms) if low level programming language is used for implementation. The results are encouraging and show that the proposed method is suitable for mobile devices.

We invented a progressive image transmission scheme that is used for transmitting Chinese calligraphy. The scheme employs the property of simple colors of calligraphy images to design a method of transmitting images phase by phase. Overall, our scheme can achieve the following two goals. One is compressing the image data to reduce the transmission time while the other is gaining less response time by using progressive image transmission. Furthermore, the recovered image still maintains the colors of the seals with high image quality.

AdaBoost rarely suffers from overfitting problems in low noise data cases. However, recent studies with highly noisy patterns have clearly shown that overfitting can occur. A natural strategy to alleviate the problem is to penalize the data distribution skewness in the learning process to prevent several hardest examples from spoiling decision boundaries. In this paper, we pursue such a penalty scheme in the mathematical programming setting, which allows us to define a suitable classifier soft margin. By using two smooth convex penalty functions, based on Kullback–Leibler divergence (KL) and l₂ norm, we derive two new regularized AdaBoost algorithms, referred to as AdaBoost_KL and AdaBoost_Norm2, respectively. We prove that our algorithms perform stage-wise gradient descent on a cost function, defined in the domain of their associated soft margins. We demonstrate the effectiveness of the proposed algorithms through experiments over a wide variety of data sets. Compared with other regularized AdaBoost algorithms, our methods achieve at least the same or better performance.