可视跟踪就是利用图像处理、模式识别的方法发现视频序列中与指定目标图像最相似的部分，在兼顾实时性的基础上提高跟踪算法的稳健性一直是可视跟踪研究中的前沿和热点。本文提出利用目标历史模型和当前匹配位置处得到的观测模型对目标核函数直方图进行Kalman滤波，从而对模型进行及时更新。首次提出把滤波残差作为样本进行假设检验，将其结果作为模型是否需要更新的准则。论证了Mean-shift框架下跟踪变尺度目标的充分条件，提出了“后向跟踪－形心配准”的核窗宽自动选取算法。实验验证了所提方法的有效性。

A software tool for data mining (DMiner-I) is introduced, which integrates pattern recognition (PCA, Fisher, clustering, HyperEnvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, HyperEnvelop, support vector machine, visualization. The principle, algorithms and knowledge representation of some function models of data mining are described. Nonmontony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining is realized by Visual C++ under Windows 2000. The software tool of data mining has been satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

The current preoperative way of assessing the degree of malignancy in brain glioma is based on magnetic resonance imaging (MRI) findings and clinical data. We studied 280 cases, of which 111 were high-grade malignancies and 169 low-grade, to acquire regular and interpretable patterns of the relations between glioma MRI features and the degree of malignancy. However, as uncertainties in the data and missing values existed, a fuzzy rule extraction algorithm based on Fuzzy Min-Max Neural Network (FMMNN) was proposed. The performance of Multi-Layer Perceptron network (MLP) trained with error Back-Propagation algorithm (BP), the well-known decision tree algorithm ID3, Nearest Neighbor, and the original Fuzzy Min-Max Neural Network were also evaluated. The results showed that two fuzzy decision rules on only 6 features achieved an accuracy of 84.6% (89.9% for low-grade cases and 76.6% for high-grade ones). Investigations with the proposed algorithm revealed that age, mass effect, edema, post–contrast enhancement, blood supply, calcification, hemorrhage, and signal intensity of the T1-weighted Image were important diagnostic factors.

Wavelet Domain Hidden Markov Model (WD HMM), in particular Hidden Markov Tree (HMT), has recently been proposed and applied to gray level image analysis. In this paper, color texture analysis using WD HMM is studied. In order to combine color and texture information to one single model, we extend WD HMM by grouping the wavelet coefficients from different color planes to one vector. The grouping way is chose according to a tradeoff between computation complexity and effectiveness. Besides, we propose Multivariate Gaussian Mixture Model (MGMM) to approximate the marginal distribution of wavelet coefficient vectors and to capture the interactions of different color planes. By employing our proposed approach, we can improve the performance of WD HMM on color texture classification. The experiment shows that our proposed WD HMM provides a 98% ercentage of correct classifications (PCC) on 44 color images from an Oulu Texture Database and outperforms other methods.

Clustering analysis is an important topic in artificial intelligence (AI) and pattern recognition (PR) research. Conventional clustering algorithms, such as the famous Fuzzy C-Means clustering algorithm (FCM) assume that all the attributes are equally relevant to all the clusters. However in most domains, some attributes are irrelevant, and some relevant ones are less important than others for a specific class. In this paper, such imbalance between the attributes is considered and a new weighted fuzzy kernel-clustering algorithm WFKCA is presented. WFKCA performs clustering in high feature space mapped by mercer kernels. Comparing with the conventional hard kernel-clustering algorithm, WFKCA can derive the meaningful prototypes of the clusters. Numerical convergence properties of WFKCA are also discussed. In order to tackle with the incomplete data effectively, we extend WFKCA to WFKCA2, which is demonstrated a useful tool for clustering incomplete data. Finally, we further demonstrate WFKCA is an effective tool for image segmentation with numerical examples.