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

The Special Sensor Microwave/Imager (SSM/I) radiometer is practical in monitoring snow conditions for its sensitive response to the changes in snow properties. A single-hidden-layer artificial neural network (ANN) was employed to accomplish this remote sensing task, with radiometric observations of brightness temperatures (Tbs) as input data, to derive information about snow. Error backpropagation learning was applied to train the ANN. After learning the mapping of SSM/I Tbs to snow classes, the ANN approach showed a significant promise for identifying mountainous snow conditions. Error rates were 3% for snow-free, 5% for dry snow, 9% for wet snow, and 0% for refrozen snow, respectively. This study indicates the potential of ANN supervised learning for the inference of snow conditions from SSM/I observations. Further improvement on the application of ANN for large-scale snow monitoring can be expected by using more training data derived from both plains and mountain regions.

Identification of mountain snow cover using SSM/I and artificial neural network

A neutrosophic set (Ns), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a powerful general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. We apply the neutrosophic set in image domain and define some concepts and operations for image thresholding. The image G is transformed into Ns domain, which is described using three subsets T, I and F. The entropy in neutrosophic set is defined and employed to evaluate the indetermination. A new λmean operation is proposed to reduce the set’s indetermination. Finally, the proposed method is employed to perform image thresholding. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively. Especially, it can process the “clean” images, the images with different kind of noise and the images with multiple kinds of noise well without knowing the type of the noise, which is the most difficult task for image thresholding.

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A new neutrosophicappraoch to image thresholding

This paper presents a methodology for tracking multiple skaters in short track speed skating competitions. Nonrigid skaters move at high speed with severe occlusions happening frequently among them. The camera is panned quickly in order to capture the skaters in a large and dynamic scene. To automatically track the skaters and precisely output their trajectories becomes a challenging task in object tracking. We employ the global rink information to compensate camera motion and obtain the global spatial information of skaters, utilize random forest to fuse multiple cues and predict the blob of each skater, and finally apply a silhouette- and edge-based template-matching and blob-evolving method to labelling pixels to a skater. The effectiveness and robustness of the proposed method are verified through thorough experiments.

Robust multiple cue fusion-based high-speed and nonrigid object tracking algorithm for short track speed skating

The identification of images irrespective of their location, size and orientation is one of the important tasks in pattern analysis. The use of global moment features has been one of the most popular techniques for this purpose. We present a simple and effective method for gray-level image representation and identification which utilizes fuzzy radial moments of image segments (local moments) as features as opposed to global features. A multilayer perceptron neural network is employed for classification. Fuzzy entropy measure is applied to optimize the parameters of the membership function. The technique does not require translation, scaling or rotation of the image. Furthermore, it is suitable for parallel implementation which is an advantage for real-time applications. The classification capability and robustness of the technique are demonstrated by experiments on scaled, rotated and noisy gray-level images of uppercase and lowercase characters and digits of English alphabet, as well as the images of a set of tools. The proposed approach can handle rotation, scale and translation invariance, noise and fuzziness simultaneously.

Heng-Da Cheng

Papers

Identification of mountain snow cover using SSM/I and artificial neural network

A new neutrosophicappraoch to image thresholding

Robust multiple cue fusion-based high-speed and nonrigid object tracking algorithm for short track speed skating

Scene Classification by Fuzzy Local Moments

Transformation of gray level and color images