다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

CA : A Cancer Journal for Clinicians

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)

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Survey over image thresholding techniques and quantitative performance evaluation

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

This paper discusses the task of human action detection in crowded videos. First, we propose a novel mask based shape matching method for action recognition. Our method does not need human detection or segmentation, and it can be used in both clean and crowed backgrounds. Next, shape and flow based features are combined due to their complementary nature. For each action, a binary sequence is used as the template for both shape and flow matching. For a testing sequence and a template sequence, dynamic time warping technique is first applied for time alignment, then shape and flow matching distances are computed between matched frames. We test our algorithm on the CMU dataset and achieve an encouraging performance.

Masks based human action detection in crowded videos

To improve the diagnostic accuracy of breast ultrasound classification, a novel computer-aided diagnosis (CAD) system based on B-Mode and color Doppler flow imaging is proposed. Several new features are modeled and extracted from the static images and color Doppler image sequences to study blood flow characteristics. Moreover, we proposed a novel classifier ensemble strategy for obtaining the benefit of mutual compensation of classifiers with different characteristics. Experimental results demonstrate that the proposed CAD system can improve the true-positive and decrease the false positive detection rate, which is useful for reducing the unnecessary biopsy and death rate.

An effective computer aided diagnosis system using B-Mode and color Doppler flow imaging for breast cancer

Cluster analysis is a generic name for a variety of mathematical methods that can be used to classify a given data set. By using the cluster analysis the people try to understand a set of data and to reveal the structure of the data. Clustering technologies find very important applications in the disciplining of pattern recognition and image processing. They are very useful for unsupervised pattern classification and image segmentation. This paper presents a VLSI cluster analyser for implementing the squared-error clustering technique using extensive pipelining and parallel computation capabilities. The proposed cluster analyser could perform one pass of the squared-error algorithm (which includes finding the squared distances between every cluster center, assigning each pattern to its closest cluster center and recomputing the cluster centers) in 0(N+M+K) time units, where M is the dimension of the feature vector, N is the number of sample patterns, and K is the desired number of clusters. And it will need 0(N x M xK) time units, if a uniprocessor is used. The algorithm partition problem is also studied in this paper.

Clustering Analyser For Pattern Recognition

Conformal prediction presents a novel idea whose error rate is provably controlled by given significant levels. So the remaining goal of conformal prediction is its efficiency. High efficiency means that the predictions are as certain as possible. As we know, ensemble methods are able to obtain a better predictive performance than that obtained from any of the constituent models. Ensemble method such as random forest has been used as underlying method to build conformal predictor. But we do not know the differences of conformal predictors with and without ensemble methods, and how the corresponding performances are improved. In this paper, an ensemble method AdaBoost is used to build conformal predictor, and we introduce another evaluation metric-correct efficiency, which measures the efficiency of correct classification correctly. The good performance of AdaBoost-based conformal predictor (CP-AB) has been validated on seven datasets. The experimental results show that the proposed method has a much higher efficiency.

AdaBoost-based conformal prediction with high efficiency

Fuzzy Logic was intruded by Lotfi A. Zadeh in 1965. It is for solving uncertainty and ambiguity problems. Fuzzy logic is a multivalued logic defined in [0,1] . However, there are a lot of paradoxes, as proposition, can not be described in Fuzzy Logic. In this paper, we will define an extended fuzzy logic and applied it on watershed segmentation method in image processing.

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Heng-Da Cheng

Papers

Masks based human action detection in crowded videos

An effective computer aided diagnosis system using B-Mode and color Doppler flow imaging for breast cancer

Clustering Analyser For Pattern Recognition

AdaBoost-based conformal prediction with high efficiency

An Extended Fuzzy Logic Method for Watershed