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IEEE International Conference on Image Processing

作者： Tao Wan Xu Liu Jianhui Chen Zengchang Qin Intelligent Computing and Machine Learning Lab Beihang University No 91 Central Hospital of PLA

ISBN: (纸本)9781479957521

To diagnose breast cancer (BCa), the number of mitotic cells present in tissue sections is an important parameter to examine and grade breast biopsy specimen. The differentiation of mitotic from non-mitotic cells in breast histopatho-logical images is a crucial step for automatical mitosis detection. This work aims at improving the accuracy of mitosis classification by characterizing objects of interest (tissue cells) in wavelet based multi-resolution representations that better capture the statistical features having mitosis discrimination. A dual-tree complex wavelet transform (DT-CWT) is performed to decompose the image patches into multi-scale forms. Five commonly-used statistical features are extracted on each wavelet subband. Since both mitotic and non-mitotic cells appear as small objects with a large variety of shapes in the images, characterization of mitosis is a challenging problem. The inter-scale dependencies of wavelet coefficients allow extraction of important texture features within the cells that are more likely to appear at all different scales. The wavelet-based statistical features were evaluated on a dataset containing 327 mitotic and 406 non-mitotic cells via a support vector machine classifier in iterative cross-validation. The quantitative results showed that our DT-CWT based approach achieved superior classification performance with the accuracy of 87.94%, sensitivity of 86.80%, specificity of 89.89%, and the area under the curve (AUC) value of 0.94.

关键词： Mitosis Wavelet transform Multi-resolution representation Breast cancer histopathology

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Wavelet-based statistical features for distinguishing mitotic and non-mitotic cells in breast cancer histopathology

Wavelet-based statistical features for distinguishing mitoti...

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作者： Wan, Tao Liu, Xu Chen, Jianhui Qin, Zengchang Intelligent Computing and Machine Learning Lab Beihang University Beijing100191 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China No 91 Central Hospital of PLA Henan454003 China

ISBN: (纸本)9781479957514

To diagnose breast cancer (BCa), the number of mitotic cells present in tissue sections is an important parameter to examine and grade breast biopsy specimen. The differentiation of mitotic from non-mitotic cells in breast histopathological images is a crucial step for automatical mitosis detection. This work aims at improving the accuracy of mitosis classification by characterizing objects of interest (tissue cells) in wavelet based multi-resolution representations that better capture the statistical features having mitosis discrimination. A dual-tree complex wavelet transform (DT-CWT) is performed to decompose the image patches into multi-scale forms. Five commonly-used statistical features are extracted on each wavelet subband. Since both mitotic and non-mitotic cells appear as small objects with a large variety of shapes in the images, characterization of mitosis is a challenging problem. The inter-scale dependencies of wavelet coefficients allow extraction of important texture features within the cells that are more likely to appear at all different scales. The wavelet-based statistical features were evaluated on a dataset containing 327 mitotic and 406 non-mitotic cells via a support vector machine classifier in iterative cross-validation. The quantitative results showed that our DT-CWT based approach achieved superior classification performance with the accuracy of 87.94%, sensitivity of 86.80%, specificity of 89.89%, and the area under the curve (AUC) value of 0.94. © 2014 IEEE.

关键词： Wavelet transforms

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TuneRank Model for Main Melody Extraction from Multi-part Musical Scores

TuneRank Model for Main Melody Extraction from Multi-part Mu...

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International Conference on intelligent Human-machine Systems and Cybernetics, IHMSC

作者： Hanqing Zhao Zengchang Qin École d'Ingénieur Généraliste Beihang University Beijing China Intelligent Computing and Machine Learning Lab. Beihang University China

ISBN: (纸本)9781479949540

A musical part is a strand of music played by an individual instrument within a larger music work, main melody is a single musical part which consists of the most significant melodic elements of multi-part musical score. This part is typically related to dissonance between musical parts and musical instruments. In this paper, we propose a novel model for extracting the main melody from multi-part musical scores. This model is referred to as the TuneRank model that has the conceptually similar idea of the PageRank model. If each musical note can be considered like a web page in the Internet, and the dissonance value between two notes is like the quantity of links between two web pages. The TuneRank (rank of becoming main melody) of each note is calculated using Markov transition probability. This model is tested on the ECPK4 database. By comparing to the previous work, we find that this note-based model is more effective for processing scores containing main melody in multiple parts. Also, the accuracy does not change with the increase of the number of parts. In general, this model can be used for extracting the single-part main melody of digital musical scores.

关键词： Tuners Accuracy Feature extraction Probability Educational institutions Internet

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A novel texture image segmentation model based on multi-scale structure

A novel texture image segmentation model based on multi-scal...

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International Conference on Multisensor Fusion and Integration for intelligent Systems (MFI)

作者： Hai Min Xiao-Feng Wang De-Shuang Huang Department of Automation University of Science and Technology of China Hefei Anhui China Intelligent Computing Lab Chinese Academy of Sciences Department of Computer Science and Technology Hefei University Hefei Anhui China Machine Learning and Systems Biology Laboratory Tongji University Shanghai China

ISBN: (纸本)9781479967339

Using neighborhood information from different scales in an effective way for object segmentation, however, remains a difficult problem. In this paper, we propose a novel method which incorporates the multi-scale structure into intensity information. The multi-scale structure is proposed as structure information to describe the texture feature of image. We select eight points as the sample components of each layer and further solve the maximum variance between eight components and center pixel. In addition, the multi-scale structure can be suitably used to extract the texture information of image. Then, by combining texture information with intensity information, we use level set method to obtain the segmentation results. Experiments using synthetic and real natural scene images demonstrate the superior performance of the proposed method.

关键词： Image segmentation Level set Feature extraction Gabor filters Tensile stress Visualization Image color analysis

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Image super-resolution using local learnable kernel regression

Image super-resolution using local learnable kernel regressi...

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11th Asian Conference on Computer Vision, ACCV 2012

作者： Liao, Renjie Qin, Zengchang Intelligent Computing and Machine Learning Lab. School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9783642374302

In this paper, we address the problem of learning-based image super-resolution and propose a novel approach called Local Learnable Kernel Regression (LLKR). The proposed model employs a local metric learning method to improve the kernel regression for reconstructing high resolution images. We formulate the learning problem as seeking multiple optimal Mahalanobis metrics to minimize the total kernel regression errors on the training images. Through learning local metrics in the space of low resolution image patches, our method is capable to build a precise data-adaptive kernel regression model in the space of high resolution patches. Since the local metrics split the whole data set into several subspaces and the training process can be executed off-line, our method is very efficient at runtime. We demonstrate that the new developed method is comparable or even outperforms other super-resolution algorithms on benchmark test images. The experimental results also show that our algorithm can still achieve a good performance even with a large magnification factor. © 2013 Springer-Verlag.

关键词： Image enhancement

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Hybrid Bayesian estimation tree learning with discrete and fuzzy labels

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Frontiers of Computer Science 2013年第6期7卷 852-863页

作者： Zengchang QIN Tao WAN Intelligent Computing and Machine Learning Lab School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China School of Biological Science and Medical Engineering Beihang University Beijing 100191 China Department of Biomedical Engineering Case Western Reserve University Cleveland OH 44106 USA

Classical decision tree model is one of the classical machine learning models for its simplicity and effectiveness in applications. However, compared to the DT model, probability estimation trees （PETs） give a better estimation on class probability. In order to get a good probability estimation, we usually need large trees which are not desirable with respect to model transparency. Linguistic decision tree （LDT） is a PET model based on label semantics. Fuzzy labels are used for building the tree and each branch is associated with a probability distribution over classes. If there is no overlap between neighboring fuzzy labels, these fuzzy labels then become discrete labels and a LDT with discrete labels becomes a special case of the PET model. In this paper, two hybrid models by combining the naive Bayes classifier and PETs are proposed in order to build a model with good performance without losing too much transparency. The first model uses naive Bayes estimation given a PET, and the second model uses a set of small-sized PETs as estimators by assuming the independence between these trees. Empirical studies on discrete and fuzzy labels show that the first model outperforms the PET model at shallow depth, and the second model is equivalent to the naive Bayes and PET.

关键词： fuzzy labels label semantics random set probability estimation tree mass assignment linguistic decision tree naive Bayes

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A robot vision system in ABU robocon contest: Visual servoing navigation and object tracking

A robot vision system in ABU robocon contest: Visual servoin...

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2nd International Conference on Materials and Products Manufacturing Technology, ICMPMT 2012

作者： Li, Xihua Zhang, Ping Lin, Sina Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China Intelligent Computing and Machine Learning Lab Beihang University Beijing 100191 China

ISBN: (纸本)9783037855447

This article took the 2011 ABU Robocon Contest as background, and introduced the design and implementation of a vision system for visual servoing navigation and object tracking in this contest. For visual servoing navigation part, it had a series of simple image processing and then added to a previous control law;tests showed only 3cm distance and 1°angle error occur when robot ran one line of 6m. For object tracking part, article introduced a combined algorithm for the detecting of a black and white semi-circle object, which moved quickly and uncertainty in a colorful background and shining environment;tests showed the proposed algorithm work better than other exist algorithms in complex environment. © (2013) Trans Tech Publications, Switzerland.

关键词： Navigation

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Preface

Advanced Topics in Science and Technology in China

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Advanced Topics in Science and Technology in China 2014年 vii页

作者： Qin, Zengchang Tang, Yongchuan Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing China College of Computer Science Zhejiang University HangzhouZhejiang China

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A bag-of-tones model with MFCC features for musical genre classification

A bag-of-tones model with MFCC features for musical genre cl...

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9th International Conference on Advanced Data Mining and Applications, ADMA 2013

作者： Qin, Zengchang Liu, Wei Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing 100191 China School of Advanced Engineering Beihang University Beijing 100191 China School of Biological Science and Medical Engineering Beihang University Beijing 100191 China Department of Biomedical Engineering Case Western Reserve University Cleveland OH 44106 United States

ISBN: (纸本)9783642539138

Musical genres are categorical labels created by humans to characterize pieces of music. These labels may be highly subjective but typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. In this paper, we propose a model for music genre classification. The new model is referred to as the bag-of-tones (BOT) model which follows the conceptually similar idea of the bag-of-words (BOW) model in natural language processing and the bag-of-feature (BOF) model in image processing. The basic low-level music features such as Mel-frequency cepstral coefficients (MFCC) are clustered into a set of codewords referred to as "tones". By using such a model, each piece of music can be represented by a new feature vector of distribution on tones. Classical machine learning models such as support vector machines (SVM) can be applied for genre classification. The model is tested using two datasets. We found that the polynomial kernel function has the best performance in the SVM classification. By comparing to the previous work, we found the new proposed model outperform classical models on a given benchmark dataset. In general, this model can be used to structure the large collections of music available on the Web. It can play an important role in automatic digital music categorization and retrieval. © Springer-Verlag 2013.

关键词： Music

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Statistical fault localization using execution sequence

Statistical fault localization using execution sequence

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2012 International Conference on machine learning and Cybernetics, ICMLC 2012

作者： You, Zunwen Qin, Zengchang Zheng, Zheng Beihang University China Intelligent Computing and Machine Learning Lab. School of ASEE Beihang University China

ISBN: (纸本)9781467314855

Fault localization is one of the most expensive and time consuming jobs in program debugging. Many approaches were proposed in order to locate faults effectively and efficiently. In this paper, we proposed a novel statistical approach by exploiting the statistical behavior of two sequentially connected predicates in the execution. If the predicates are regarded as the vertices of a graph, then the edges of the graph represent the transition of two sequential predicates in the execution trace of the program. The label of each edge is the frequency of each transition. For each edge, we apply hypothesis testing to evaluate the difference between edge evaluation bias in the passed runs and that in the failed runs. The edges are ranked according to the fault relevance score obtained from the hypothesis testing. The experimental results on Siemens suite show that the our proposed predicate-based fault localization method outperforms other well-used statistical fault localization techniques. © 2012 IEEE.

关键词： Program debugging

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