2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)最新文献_第4页

Sparse signal recovery from compressed measurements using hybrid particle swarm optimization 基于混合粒子群优化的压缩测量稀疏信号恢复

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120649

Hassaan Haider, J. Shah, Shahid Ikram, Idris Abd Latif

{"title":"Sparse signal recovery from compressed measurements using hybrid particle swarm optimization","authors":"Hassaan Haider, J. Shah, Shahid Ikram, Idris Abd Latif","doi":"10.1109/ICSIPA.2017.8120649","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120649","url":null,"abstract":"The computationally intensive part of compressed sensing (CS) deals with the sparse signal reconstruction from lesser number of random projections. Finding sparse solution to such an underdetermined system is highly ill-conditioned and therefore requires additional regularization constraints. This research paper introduces a new approach for recovering a K-sparse signal from compressed samples using particle swarm optimization (PSO) along with separable surrogate functionals (SSF) algorithm. The suggested hybrid mechanism applied with appropriate regularization constraints speeds up the convergence of PSO. The estimated original sparse signal is also recovered with great precision. Simulation results show that the signal estimated with PSO-SSF combination outperforms the signal recovery through PSO, SSF and parallel coordinate descent (PCD) methods in terms of reconstruction accuracy. Finally, the efficiency of the proposed algorithm is validated experimentally by exactly recovering a one-dimensional K-sparse signal from only a few number of non-adaptive random measurements.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116313681","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: 1

Automatic classification of diabetic macular edema using a modified completed Local Binary Pattern (CLBP) 基于改进的局部完全二值模式(CLBP)的糖尿病黄斑水肿自动分类

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120570

S. T. Lim, M. K. Ahmed, Sungbin Lim

{"title":"Automatic classification of diabetic macular edema using a modified completed Local Binary Pattern (CLBP)","authors":"S. T. Lim, M. K. Ahmed, Sungbin Lim","doi":"10.1109/ICSIPA.2017.8120570","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120570","url":null,"abstract":"Diabetic macular edema is the leading cause of visual loss for patients with diabetic retinopathy, a complication of diabetes. Early screening and treatment has been shown to prevent blindness in diabetic retinopathy and diabetic macular edema. The Early Treatment Diabetic Retinopathy Study (ETDRS) and the Diabetic Macular Edema Disease Severity Scale are the common screening standards based on the distance of exudates from the fovea. Instead of focusing on the macula region, this research adopts a global approach using texture classification to grade the fundus images into three stages: normal, moderate diabetic macular edema and severe diabetic macular edema. The proposed algorithm starts with a modified completed Local Binary Pattern (CLBP) to extract the image local gray level for all RGB channels. The obtained feature vector will then be fed into a multiclass Support Vector Machine (SVM) for classification. The 100 fundus images selected to be utilized for training and testing set were taken from MESSIDOR and these images were reviewed by an ophthalmologist for cross-validation. The algorithm using the CLBP demonstrates a sensitivity of 67% with a specificity of 30% while the proposed modified CLBP yields a higher sensitivity and specificity of 80% and 70% respectively.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"331 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116358646","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: 8

Multiple trials in event related fMRI for different conditions 不同条件下事件相关fMRI的多项试验

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120628

R. Zafar, A. Malik, Aliyu Nuhu Shuaibu, M. J. U. Rehman, S. Dass

引用次数: 0

Cooperative non-orthogonal multiple access using two-way relay 采用双向中继的协作非正交多址

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120655

Chun Yeen Ho, C. Leow

引用次数: 17

Tumor detection and whole slide classification of H&E lymph node images using convolutional neural network 基于卷积神经网络的H&E淋巴结图像肿瘤检测及全片分类

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120585

Mohammad F. Jamaluddin, M. F. A. Fauzi, F. S. Abas

{"title":"Tumor detection and whole slide classification of H&E lymph node images using convolutional neural network","authors":"Mohammad F. Jamaluddin, M. F. A. Fauzi, F. S. Abas","doi":"10.1109/ICSIPA.2017.8120585","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120585","url":null,"abstract":"Histopathological analysis of tissues has been gaining a lot of interests recently, from developing computer algorithms to assist pathologists from cell detection and counting, to tissue classification and cancer grading. With the advent of whole slide imaging, the field of digital pathology has gained enormous popularity, and is currently regarded as one of the most promising avenues of diagnostic medicine. Deep learning advancement on image set today has successfully evolved as many models has been proposed and produced state-of-the-art object classifying results. This is not limited to large database such as Imagenet but also has seen applications in other medical image analysis related areas. In this paper we have carefully constructed and expanded the deep model network to classify normal and tumor slides in histology images of lymph nodes tissue. We have proposed our own deep learning model based on convolutional neural network with smaller requirement using 64×64×3 input image with 12 convolutional layer with max pooling and ReLU activation function. Our method has better AUC result at 0.94 than the winner of Camelyon16 Challenge with AUC of 0.925.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121904647","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: 13

A modified direct data domain STAP approach based on cost function reconstruction 基于成本函数重构的直接数据域STAP改进方法

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120654

Jie He, Da-Zheng Feng, Xiao-Jun Yang

引用次数: 0

An optimized low computational algorithm for human fall detection from depth images based on Support Vector Machine classification 基于支持向量机分类的深度图像人体跌倒检测优化算法

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120645

M. N. Mohd, Yoosuf Nizam, S. Suhaila, M. M. Jamil

{"title":"An optimized low computational algorithm for human fall detection from depth images based on Support Vector Machine classification","authors":"M. N. Mohd, Yoosuf Nizam, S. Suhaila, M. M. Jamil","doi":"10.1109/ICSIPA.2017.8120645","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120645","url":null,"abstract":"Systems developed to classify human activities to identify unintentional falls are highly demanding and play an important role in our daily life. Human falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approaches are used to develop human fall detection systems for elderly and people with special needs. The three basic approaches used include some sort of wearable devices, ambient based devices or non-invasive vision based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. This paper proposes a fall detection system based on an algorithm using combination of machine learning and human activity measurements such as changes of human height and rate of change of the subject during any of the activity. Classification of human fall from other activities of daily life is accomplished using height, changes in velocity and acceleration of the subject extracted from the depth information. Finally position of the subject and SVM classification is used for fall confirmation. From the experimental results, the proposed system was able to achieve an average accuracy of 97.39% with sensitivity of 100% and specificity of 96.61%.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"4 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120978695","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: 7

Software profiling analysis for DNA microarray image processing algorithm DNA微阵列图像处理算法的软件分析

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120592

Omar Salem Baans, A. B. Jambek

引用次数: 2

Intra block prediction using first-second row non-directional samples in HEVC video coding HEVC视频编码中第一-第二行非定向样本的块内预测

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120582

E. Jaja, A. Rahman, Z. Omar, M. Zabidi, U. U. Sheikh

引用次数: 0

A deep architecture for face recognition based on multiple feature extraction techniques 基于多特征提取技术的人脸识别深度体系结构

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120642

Saleh Albelwi, A. Mahmood

{"title":"A deep architecture for face recognition based on multiple feature extraction techniques","authors":"Saleh Albelwi, A. Mahmood","doi":"10.1109/ICSIPA.2017.8120642","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120642","url":null,"abstract":"Some of the best current face recognition approaches use feature extraction techniques based on either Principle Component Analysis (PCA), Local Binary Patterns (LBP), Autoencoder (non-linear PCA), etc. While each of these feature techniques works fairly well, we propose to combine multiple feature extractors with deep learning in a system so that the overall face recognition accuracy can be improved. The output from multiple feature extractions is classified using a deep learning approach. Deep learning algorithms possess high capability to learn more complex functions in order to handle difficult computer vison tasks. Our proposed method integrates the output of three different feature extractors, specifically PCA, LBP+PCA, and dimensionality reduction of LBP features using a Neural Network (NN). The features from the above three techniques are concatenated to form a joint feature vector. This feature vector is fed into a deep Sacked Sparse Autoencoder (SSA) as a classifier to generate the recognition results. Our proposed approach is evaluated by ORL and AR face databases. The experimental results indicate that our system outperforms existing ones based on individual feature techniques as well as reported systems employing multiple feature types.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114931147","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: 4