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

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

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

Traditional and recent approaches in background modeling for foreground detection: An overview

Visual object tracking is a significant computer vision task which can be applied to many domains, such as visual surveillance, human computer interaction, and video compression. Despite extensive research on this topic, it still suffers from difficulties in handling complex object appearance changes caused by factors such as illumination variation, partial occlusion, shape deformation, and camera motion. Therefore, effective modeling of the 2D appearance of tracked objects is a key issue for the success of a visual tracker. In the literature, researchers have proposed a variety of 2D appearance models. To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic. The contributions of this survey are fourfold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-by-detection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generative-discriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from a theoretical or practical viewpoint. Fourth, the existing benchmark resources (e.g., source codes and video datasets) are examined in this survey.

https://cs.adelaide.edu.au/~ard/Publications/tracking_survey.pdf

A survey of appearance models in visual object tracking

We propose a new finite mixture model based on the
formalism of general Gaussian distribution (GGD). Because it has
the flexibility to adapt to the shape of the data better than the Gaussian,
the GGD is less prone to overfitting the number of mixture
classes when dealing with noisy data. In the first part of this work,
we propose a derivation of the maximum likelihood estimation for
the parameters of the new mixture model, and elaborate an
information-theoretic approach for the selection of the number of
classes. In the second part, we validate the proposed model by
comparing it to the Gaussian mixture in applications related to image
and video foreground segmentation.

Finite general Gaussian mixture modeling and application to image and video foreground segmentation

We propose a learning-based approach for automatic detection of fabric defects Our approach is based on a statistical representation of fabric patterns using the redundant contourlet transform (RCT) The distribution of the RCT coefficients are modeled using a finite mixture of generalized Gaussians (MoGG), which constitute statistical signatures distinguishing between defective and defect-free fabrics In addition to being compact and fast to compute, these signatures enable accurate localization of defects Our defect detection system is based on three main steps In the first step, a preprocessing is applied for detecting basic pattern size for image decomposition and signature calculation In the second step, labeled fabric samples are used to train a Bayes classifier (BC) to discriminate between defect-free and defective fabrics Finally, defects are detected during image inspection by testing local patches using the learned BC Our approach can deal with multiple types of textile fabrics, from simple to more complex ones Experiments on the TILDA database have demonstrated that our method yields better results compared with recent state-of-the-art methods Note to Practitioners —Fabric defect detection is central to automated visual inspection and quality control in textile manufacturing This paper deals with this problem through a learning-based approach By opposite to several existing approaches for fabric defect detection, which are effective in only some types of fabrics and/or defects, our method can deal with almost all types of patterned fabric and defects To enable both detection and localization of defects, a fabric image is first divided into local blocks, which are representative of the repetitive pattern structure of the fabric Then, statistical signatures are calculated by modeling the distribution of coefficients of an RCT using the finite MoGG The discrimination between defect-free and defective fabrics is then achieved through supervised classification of RCT-MoGG signatures based on expert-labeled examples of defective fabric images Experiments have shown that our method yields very good performance in terms of defect detection and localization In addition to its accuracy, inspection of images can be performed in a fully automatic fashion, whereas only labeled examples are initially required Finally, our method can be easily adapted to a real-time scenario since defect detection on inspected images is performed at the block level, which can be easily parallelized through hardware implementation

Automatic Fabric Defect Detection Using Learning-Based Local Textural Distributions in the Contourlet Domain

In this paper, we propose a robust video foreground modeling by using a finite mixture model of generalized Gaussian distributions (GDD). The model has a flexibility to model the video background in the presence of sudden illumination changes and shadows, allowing for an efficient foreground segmentation. In a first part of the present work, we propose a derivation of the online estimation of the parameters of the mixture of GDDS and we propose a Bayesian approach for the selection of the number of classes. In a second part, we show experiments of video foreground segmentation demonstrating the performance of the proposed model.

A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling

This paper addresses statistical-based texture modeling using wavelets. We propose a new approach to represent the marginal distribution of the wavelet coefficients using finite mixtures of generalized Gaussian (MoGG) distributions. The MoGG captures a wide range of histogram shapes, which provides better description and discrimination of texture than using single probability density functions (pdf's), as proposed by recent state-of-the-art approaches. Moreover, we propose a model similarity measure based on Kullback-Leibler divergence (KLD) approximation using Monte Carlo sampling methods. Through experiments on two popular texture data sets, we show that our approach yields significant performance improvements for texture discrimination and retrieval, as compared with recent methods of statistical-based wavelet modeling.

Wavelet Modeling Using Finite Mixtures of Generalized Gaussian Distributions: Application to Texture Discrimination and Retrieval

In this letter, we propose a clustering model that efficiently mitigates image and video under/over-segmentation by combining generalized Gaussian mixture modeling and feature selection. The model has flexibility to accurately represent heavy-tailed image/video histograms, while automatically discarding uninformative features, leading to better discrimination and localization of regions in high-dimensional spaces. Experimental results on a database of real-world images and videos showed us the effectiveness of the proposed approach.

Mohand Saïd Allili

Papers

Finite general Gaussian mixture modeling and application to image and video foreground segmentation

Automatic Fabric Defect Detection Using Learning-Based Local Textural Distributions in the Contourlet Domain

A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling

Wavelet Modeling Using Finite Mixtures of Generalized Gaussian Distributions: Application to Texture Discrimination and Retrieval

Image and Video Segmentation by Combining Unsupervised Generalized Gaussian Mixture Modeling and Feature Selection