Digital Image Processing - Advances and Applications [Working Title]最新文献

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Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition 部分遮挡人脸识别的加权模块线性回归分类

Digital Image Processing - Advances and Applications [Working Title] Pub Date : 2021-11-05 DOI: 10.5772/intechopen.100621

Wei-Jong Yang, Cheng-Yu Lo, P. Chung, J. Yang

{"title":"Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition","authors":"Wei-Jong Yang, Cheng-Yu Lo, P. Chung, J. Yang","doi":"10.5772/intechopen.100621","DOIUrl":"https://doi.org/10.5772/intechopen.100621","url":null,"abstract":"Face images with partially-occluded areas create huge deteriorated problems for face recognition systems. Linear regression classification (LRC) is a simple and powerful approach for face recognition, of course, it cannot perform well under occlusion situations as well. By segmenting the face image into small subfaces, called modules, the LRC system could achieve some improvements by selecting the best non-occluded module for face classification. However, the recognition performance will be deteriorated due to the usage of the module, a small portion of the face image. We could further enhance the performance if we can properly identify the occluded modules and utilize all the non-occluded modules as many as possible. In this chapter, we first analyze the texture histogram (TH) of the module and then use the HT difference to measure its occlusion tendency. Thus, based on TH difference, we suggest a general concept of the weighted module face recognition to solve the occlusion problem. Thus, the weighted module linear regression classification method, called WMLRC-TH, is proposed for partially-occluded fact recognition. To evaluate the performances, the proposed WMLRC-TH method, which is tested on AR and FRGC2.0 face databases with several synthesized occlusions, is compared to the well-known face recognition methods and other robust face recognition methods. Experimental results show that the proposed method achieves the best performance for recognize occluded faces. Due to its simplicity in both training and testing phases, a face recognition system based on the WMLRC-TH method is realized on Android phones for fast recognition of occluded faces.","PeriodicalId":135831,"journal":{"name":"Digital Image Processing - Advances and Applications [Working Title]","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129863729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Performance Analysis of OpenCL and CUDA Programming Models for the High Efficiency Video Coding 基于OpenCL和CUDA编程模型的高效视频编码性能分析

Digital Image Processing - Advances and Applications [Working Title] Pub Date : 2021-10-19 DOI: 10.5772/intechopen.99823

Randa Khemiri, Soulef Bouaafia, Asma Bahba, Maha Nasr, Fatma Ezahra Sayadi

引用次数: 0

Diffuse Optical Tomography System in Soft Tissue Tumor Detection 漫射光学断层成像系统在软组织肿瘤检测中的应用

Digital Image Processing - Advances and Applications [Working Title] Pub Date : 2021-06-30 DOI: 10.5772/intechopen.98708

Umamaheswari Kumarasamy, G. Shrichandran, A. V. Srivatson

{"title":"Diffuse Optical Tomography System in Soft Tissue Tumor Detection","authors":"Umamaheswari Kumarasamy, G. Shrichandran, A. V. Srivatson","doi":"10.5772/intechopen.98708","DOIUrl":"https://doi.org/10.5772/intechopen.98708","url":null,"abstract":"Topical review of recent trends in Modeling and Regularization methods of Diffuse Optical Tomography (DOT) system promotes the optimization of the forward and inverse modeling methods which provides a 3D cauterization at a faster rate of 40frames/second with the help of a laser torch as a hand-held device. Analytical, Numerical and Statistical methods are reviewed for forward and inverse models in an optical imaging modality. The advancement in computational methods is discussed for forward and inverse models along with Optimization techniques using Artificial Neural Networks (ANN), Genetic Algorithm (GA) and Artificial Neuro Fuzzy Inference System (ANFIS). The studies carried on optimization techniques offers better spatial resolution which improves quality and quantity of optical images used for morphological tissues comparable to breast and brain in Near Infrared (NIR) light. Forward problem is based on the location of sources and detectors solved statistically by Monte Carlo simulations. Inverse problem or closeness in optical image reconstruction is moderated by different regularization techniques to improve the spatial and temporal resolution. Compared to conventional methods the ANFIS structure of optimization for forward and inverse modeling provides early detection of Malignant and Benign tumor thus saves the patient from the mortality of the disease. The ANFIS technique integrated with hardware provides the dynamic 3D image acquisition with the help of NIR light at a rapid rate. Thereby the DOT system is used to continuously monitor the Oxy and Deoxyhemoglobin changes on the tissue oncology.","PeriodicalId":135831,"journal":{"name":"Digital Image Processing - Advances and Applications [Working Title]","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128982367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0