杨杰
智能信息处理的图象处理、模式识别、人工智能领域
个性化签名
 姓名：杨杰
 目前身份：
 担任导师情况：
 学位：

学术头衔：
博士生导师， 优秀教师/优秀教育工作者， 教育部“新世纪优秀人才支持计划”入选者
 职称：

学科领域：
模式识别
 研究兴趣：智能信息处理的图象处理、模式识别、人工智能领域
男，1964年生。1985年和1988年分别获上海交通大学自动化专业本科学位和“模式识别与智能系统”硕士学位，1989年作为优异生获国家奖学金被选派德国汉堡大学计算机系攻读博士学位，以独创性成果“Prioritized JustificationBased Nonmonotonic Logics and its Applications”取得博士学位，并在德国发表专著。1994年回国在上海交通大学图象处理与模式识别研究所工作以来，因学术和科研成绩突出，1997年被破格评聘为教授，1998年被评聘为博士生导师，自1998年起担任主管科研和研究生培养的所领导。作为“模式识别与智能系统” 国家重点学科青年学科带头人，先后入选教育部“跨世纪优秀人才计划”“资助优秀教师计划”“骨干教师计划”和上海市“曙光计划”“曙光跟踪计划”。
在智能信息处理的图象处理、模式识别、人工智能领域主持承担30多项国家和省部级科研项目，4项成果分别获得中国高校科技进步二等奖、上海市科技进步二等奖、上海市优秀发明竞赛一等奖。5项成果申报了国家发明专利。2本专著分别在德国和新加坡出版。在本项目相关领域发表200多篇论文，其中60多篇被SCI检索，80多篇EI检索，已指导博士生30多名，硕士生40多名。

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232

成果数
10
【期刊论文】Practical Fast Computation of Zernike Moments
杨杰， AlRawi Mohammed， Yang Jie
，0001，（）：
1年11月30日
Fast computation of Zernike moments from normalized geometric moments has been developed in this work. The computation is multiplications free and only additions are needed to generate Zernike moments. Geometric moments have been generated using Hatamian's filter up to high orders by a very simple and straightforward computation scheme. Other kinds of moments (i.e., Legendre, pseudo Zernike. etc.) can be computed using the same algorithm after giving the proper transformations that state their relation to geometric moments. Proper normalizations to geometric moments are necessary so that it can be used in the efficient computation of Zernike moments. To ensure fair comparisons, recursive algorithms are used to generate Zernike polynomials and any other coefficients. Computational complexity model and the test programs show that the speed up factor of the proposed algorithm is superior with respect to other fast and/or direct computations. To our knowledge, it is the first time that Zernike moments can be computed in real time rates which encourages the use of Zernike moments features in different image retrieval systems that supports huge database such as XM experimental model stated for MPEG7 experimental core. It is concluded that choosing direct computation would be impractical.
Zernike moments,， fast algorithms,， digital filter,， invariant pattern recognition,， image indexing.，

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【期刊论文】Fuzzy Rules to Predict Degree of Malignancy in Brain Glioma
杨杰， YE Chenzhou， YANG Jie， GENG Daoying， ZHOU Yue， CHEN Nianyi
，0001，（）：
1年11月30日
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 highgrade malignancies and 169 lowgrade, 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 MinMax Neural Network (FMMNN) was proposed. The performance of MultiLayer Perceptron network (MLP) trained with error BackPropagation algorithm (BP), the wellknown decision tree algorithm ID3, Nearest Neighbor, and the original Fuzzy MinMax 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 lowgrade cases and 76.6% for highgrade ones). Investigations with the proposed algorithm revealed that age, mass effect, edema, post–contrast enhancement, blood supply, calcification, hemorrhage, and signal intensity of the T1weighted Image were important diagnostic factors.
Brain Glioma,， Classification,， Fuzzy Rule Extraction,， MRI

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【期刊论文】Cephalometric image analysis and measurement for orthognathic surgery
杨杰， Jie Yang， Xufeng Ling， Yong Lu， Minggui Wei， Guowei Ding
，0001，（）：
1年11月30日
Automatic identification of landmarks in cephalometry is very important and useful for orthognathic surgery. In this paper, a computerized automatic cephalometric analysis system (CACAS) based on image processing is presented. For original Xray image, median filtering and histogram equalization is used to improve image quality. The edge of a Xray image is detected by wavelet transform and Canny filter. Seventeen landmarks in cephalometry are successfully identified by knowledgebased edge tracing and changeable template. Seventy three measurements based on distances, angles, ratios among landmarks are computed automatically. The reliability of landmarks and the validity of measurements are compared between automatic and manual operation. The values of measurements by CACAS are preciser and more reliable, the mean error for linear measurements is less than 0.9mm; the mean error for angular measurements is less than 1.2dg. The rate of validity is over 80% even if the image quality is poor. For an image with high signal to noise ratio, the rate of validity of landmarking and measurements by the CACAS system is over 90%.
orthognathic surgery,， image processing,， landmark identification,， cephalometry

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杨杰， Hongbin ShenP， P Jie Yang， Shitong Wang
，0001，（）：
1年11月30日
Clustering analysis is an important topic in artificial intelligence (AI) and pattern recognition (PR) research. Conventional clustering algorithms, such as the famous Fuzzy CMeans 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 kernelclustering algorithm WFKCA is presented. WFKCA performs clustering in high feature space mapped by mercer kernels. Comparing with the conventional hard kernelclustering 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.
Fuzzy Clustering,， Feature Space,， Pattern Recognition,， Unsupervised Learning,， Image Segmentation

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【期刊论文】HVSbased medical image compression
杨杰， Xie Kaia， ∗， Yang Jiea， Zhu Yue Minb， Li Xiao Lianga
European Journal of Radiology xxx (2004) xxx–xxx，0001，（）：
1年11月30日
Introduction: With the promotion and application of digital imaging technology in the medical domain, the amount of medical images has grown rapidly. However, the commonly used compression methods cannot acquire satisfying results. Methods: In this paper, according to the existed and stated experiments and conclusions, the lifting step approach is used for wavelet decomposition. The physical and anatomic structure of human vision is combined and the contrast sensitivity function (CSF) is introduced as the main research issue in human vision system (HVS), and then the main designing points of HVS model are presented. On the basis of multiresolution analyses of wavelet transform, the paper applies HVS including the CSF characteristics to the inner correlationremoved transform and quantization in image and proposes a new HVSbased medical image compression model. Results: The experiments are done on the medical images including computed tomography (CT) and magnetic resonance imaging (MRI). At the same bit rate, the performance of SPIHT, with respect to the PSNR metric, is significantly higher than that of our algorithm. But the visual quality of the SPIHTcompressed image is roughly the same as that of the image compressed with our approach. Our algorithm obtains the same visual quality at lower bit rates and the coding/decoding time is less than that of SPIHT. Conclusions: The results show that under common objective conditions, our compression algorithm can achieve better subjective visual quality, and performs better than that of SPIHT in the aspects of compression ratios and coding/decoding time.
Medical image， Image compression， Wavelet transform， The lifting scheme， Human vision system， Contrast sensitivity function

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【期刊论文】Target Recognition and Tracking based on Data Fusion and Data Mining
杨杰， Jie YANG， Ying HU， Qing YANG
，0001，（）：
1年11月30日
Systems with a single sensor (radar or infrared image sensor) have their limitations in target recognition and tracking. A system with multisensors can fuse data from different sensors to overcome the limitations in the system with a single sensor, it can make use of the complement and redundancy of data from different sensors to improve the precision of target recognition and tracking and the robustness and reliability. In our system for target recognition and tracking, radar and infrared image sensors are used. As a radar sensor in our system can provide with the information of the distance and direction of the target (not the image of the target), data fusion is implemented only at characteristic level and at decision level. For data fusion at characteristic level, characteristics of a target obtained from radar can be used in the IR Imagebased subsystem to improve the ability of object recognition, and vice versa. The process of target recognition based on IR image analysis is composed of image enhancement, image segmentation and recognition of segmented objects. For image enhancement, medianfilter, histogram equalization, wavelet transformation and cannyoperator are used. A medianfilter is used for the elimination of punctate noises in an IR image. The grayness of a pixel in an IR image is determined by the grayness of its neighbors. Histogram equalization is used for the improvement of the contrast of an IR image. Multiscale pyramidal wavelet transformation is used to delete unexpected edges and improve the continuity of edges in an IR image. Canny operator based on Gaussfunction is used for edge detection, an IR image is transformed into a binary image based on the adaptive threshold. According to area (number of pixels) of segmented objects, the recognition of segmented objects is divided into two classes: recognition of dot targets and area targets. Rulebased reasoning is used to deal with the recognition of dot targets; a classifier based on neural network is used to deal with the recognition of area targets. The models for target recognition are extracted by data mining. The rules for the recognition of dot targets are extracted by decision trees. A neural network for the recognition of area targets is constructed by multilayer preceptron and trained by training examples. The following characteristics of target are used as inputs of the neural classifier: •distance of the target obtained from the radarbased subsystem. •area of the target in the IR image, the variation of areas of the target in the consecutive two IR images. •the mean grayness of pixels of the target. •the variation of centers of the target in the consecutive two IR images. •the topological shape of the target (seven moment invariants Ψ1,Ψ2,...,Ψ7, the number of forks in the frame extracted.) •the direction of the target motion predicated by radar and the relation of angles among the axes of the missile, Radar and IR image sensor. After data fusion at characteristic level, a true target is recognized by the radarbased subsystem and the IR imagebased subsystem. Based on these two decisions, data fusion at decision level is to make a final decision of target tracking. a factor "decision certainty" is introduced to realize data fusion at decision level, which represents the relative certainty of decisions of target tracking.
Target Recognition and Tracking,， Data Fusion,， Data Mining,， Artificial Neural Network

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【期刊论文】DMinerI: A Software Tool of Data Mining and its Applications
杨杰， Jie YANG， Chenzhou YE， Nianyi CHEN
，0001，（）：
1年11月30日
A software tool for data mining (DMinerI) 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.
data mining,， knowledge representation,， decision trees,， Brain glioma Diagnosis

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【期刊论文】Color Texture Analysis Using WaveletBased Hidden Markov Model
杨杰， Xu Qing， Yang Jie， Zhou Yue
，0001，（）：
1年11月30日
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.
wavelet domain hidden Markov model,， color texture analysis,， Multivariate Gaussian Mixture Model.，

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【期刊论文】Illumination Invariant Recognition of ThreeDimensional Texture in Color Images∗
杨杰， Yang Jie， and Mohammed AlRawi
，0001，（）：
1年11月30日
In this paper, we present illuminationaffine invariant methods based on affine moment normalization techniques, Zernike moments, and multiband correlation functions. The methods are suitable for the illumination invariant recognition of 3D color texture. Complex valued moments (i.e., Zernike moments) and affine moment normalization are used in the derivation of illumination affine invariants where the real valued affine moment invariants fail to provide affine invariants that are independent of illumination changes. Three different moment normalization methods have been used, two of which are based on affine moment normalization technique and the third is based on reducing the affine transformation to a Euclidian transform. It is shown that for a change of illumination and orientation, the affinely normalized Zernike moment matrices are related by a linear transform. Experimental results are obtained in two directions; the first is used with textures of outdoor scenes while the second test is performed on the wellknown CUReT texture database. Both tests show high recognition efficiency of the proposed recognition methods.
3D Color texture recognition,， illumination invariance,， affine moment normalization,， Zernike moments,， affine invariants.，

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杨杰， 彭宁嵩
，0001，（）：
1年11月30日
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Meanshift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。
可视跟踪， Meanshift理论， 核函数直方图， 视频图象处理

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