Proceedings of the 2018 International Conference on Digital Medicine and Image Processing最新文献

Research on Traditional Auspicious Images and Cognition 传统吉祥意象及其认知研究

Proceedings of the 2018 International Conference on Digital Medicine and Image Processing Pub Date : 2018-11-12 DOI: 10.1145/3299852.3299856

Yu-Che Huang, Yan-Jie Chen, Ko-Jou Hsiao

引用次数: 1

Comparison Between Image Processing Methods for Detecting Object Like Circle 类圆物体检测图像处理方法的比较

Proceedings of the 2018 International Conference on Digital Medicine and Image Processing Pub Date : 2018-11-12 DOI: 10.1145/3299852.3299865

Faris Adnan Padhilah, Wahidin Wahab

引用次数: 1

A Model for Sibilant Distortion Detection in Children 儿童噪声失真检测模型研究

Proceedings of the 2018 International Conference on Digital Medicine and Image Processing Pub Date : 2018-11-12 DOI: 10.1145/3299852.3299863

I. Anjos, Margarida Grilo, Mariana Ascensão, I. Guimarães, João Magalhães, S. Cavaco

{"title":"A Model for Sibilant Distortion Detection in Children","authors":"I. Anjos, Margarida Grilo, Mariana Ascensão, I. Guimarães, João Magalhães, S. Cavaco","doi":"10.1145/3299852.3299863","DOIUrl":"https://doi.org/10.1145/3299852.3299863","url":null,"abstract":"The distortion of sibilant sounds is a common type of speech sound disorder in European Portuguese speaking children. Speech and language pathologists (SLP) use different types of speech production tasks to assess these distortions. One of these tasks consists of the sustained production of isolated sibilants. Using these sound productions, SLPs usually rely on auditory perceptual evaluation to assess the sibilant distortions. Here we propose to use an isolated sibilant machine learning model to help SLPs assess these distortions. Our model uses Mel frequency cepstral coefficients of the isolated sibilant phones from 145 children, and was trained using support vector machines. The analysis of the false negatives detected by the model can give insight into whether the child has a sibilant production distortion. We were able to confirm that there exists a relation between the model classification results and the distortion assessment of professional SLPs. Approximately 66% of the distortion cases identified by the model are confirmed by an SLP as having some sort of distortion or are perceived as being the production of a different sound.","PeriodicalId":210874,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Medicine and Image Processing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122234578","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: 3

Overlapped Fingerprint Separation Based on Deep Learning 基于深度学习的重叠指纹分离

Proceedings of the 2018 International Conference on Digital Medicine and Image Processing Pub Date : 2018-11-12 DOI: 10.1145/3299852.3299857

Chi-Hsiao Yih, Jui-Lung Hung, Jin-An Wu, Li-Ming Chen

引用次数: 1

Two-Dimensional Phase Unwrapping with Continuous Submodular Minimization 连续次模最小化的二维相位展开

Proceedings of the 2018 International Conference on Digital Medicine and Image Processing Pub Date : 2018-11-12 DOI: 10.1145/3299852.3299864

H. Kudo, S. Lian, Katsuhiro Wada

引用次数: 1

Ensemble Based Fuzzy with Particle Swarm Optimization Based Weighted Clustering (Efpso-Wc) and Gene Ontology for Microarray Gene Expression 基于集成的模糊与基于粒子群优化的加权聚类(Efpso-Wc)和基因本体的芯片基因表达

Proceedings of the 2018 International Conference on Digital Medicine and Image Processing Pub Date : 2018-11-12 DOI: 10.1145/3299852.3299866

M. Thangamani, J. A. Ibrahim

{"title":"Ensemble Based Fuzzy with Particle Swarm Optimization Based Weighted Clustering (Efpso-Wc) and Gene Ontology for Microarray Gene Expression","authors":"M. Thangamani, J. A. Ibrahim","doi":"10.1145/3299852.3299866","DOIUrl":"https://doi.org/10.1145/3299852.3299866","url":null,"abstract":"Data clustering proves to be a useful data mining approach for finding the sets of matching objects existing in the dataset. Scalability to manage massive volumes, reliability towards inherent outlier data and validity of clustering outcomes include the important issues in any data clustering technique. With the aim of addressing these problems, an Ensemble based fuzzy with Particle Swarm Optimization based Weighted Clustering (EFPSO-WC) technique that is extensively parallel and distributed in each stage, is introduced in this research work. Here Gene Ontology (GO) can be utilized for establishing the weight owing to the biological relevance exhibited by genes and its optimization is performed employing PSO. In the newly introduced work, Ensemble integrates different clustering outcomes achieved from fuzzy clustering, Fuzzy Weighted Clustering (FWC) and FPSO-WC of a group of objects into one integrated assorted clustering, frequently known as the harmony solution. This clustering can be utilized for the generation of more reliable and balanced clustering outcomes in comparison with a single clustering technique, carry out distributed computing under strict conditions or sharing information. In addition, the effectiveness of the newly introduced EFPSO-WC approach in terms of scalability and reliability was the compared with recently performed researches on the same subject. In all of the stated assessment analysis, the proposed technique performed better than the works carried out recently on the same datasets.","PeriodicalId":210874,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Medicine and Image Processing","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133034108","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

All-in-Focus Image Generation Using Improved Blind Image Deconvolution Technique 基于改进盲图像反卷积技术的全焦图像生成

Proceedings of the 2018 International Conference on Digital Medicine and Image Processing Pub Date : 2018-11-12 DOI: 10.1145/3299852.3299859

Sota Kawakami, H. Kudo

引用次数: 2

Asymmetric Distance Learning for Unsupervised Video Person Re-Identification with Tracklet Neighborhood Re-Ranking 基于Tracklet邻域重排序的非对称远程学习无监督视频人物再识别

Proceedings of the 2018 International Conference on Digital Medicine and Image Processing Pub Date : 2018-11-12 DOI: 10.1145/3299852.3299861

Xixi Hu, F. Zhou

{"title":"Asymmetric Distance Learning for Unsupervised Video Person Re-Identification with Tracklet Neighborhood Re-Ranking","authors":"Xixi Hu, F. Zhou","doi":"10.1145/3299852.3299861","DOIUrl":"https://doi.org/10.1145/3299852.3299861","url":null,"abstract":"The gruelling human-annotation and lack of sufficient labeled data make unsupervised person re-identification (re-ID) an important component in research. This paper proposes a re-ID system for unsupervised video-based re-ID, which mainly contains an asymmetric distance learning approach and a re-ranking meth-od. Specifically, using the sequence information provided by video, asymmetric learning makes a distinctive projection for features in each view, while label estimation makes this procedure efficient and effective. To further refine the results of the ranking list, an unsupervised re-ranking technique based on the already computed distance is introduced to the system. We show that both of our asymmetric distance learning and re-ranking method have achieved state-of-the-art performance on PRID-2011, iLIDS-VID and MARS datasets, meanwhile restrains the computational costs. The experiments show that our asymmetric learning method is suitable for video-based re-ID with multiple cameras, and the proposed re-ranking method is a good solution to refine the ranking list for video-based re-ID.","PeriodicalId":210874,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Medicine and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130824115","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

Real Time Multiple Face Recognition: A Deep Learning Approach 实时多人脸识别:一种深度学习方法

Proceedings of the 2018 International Conference on Digital Medicine and Image Processing Pub Date : 2018-11-12 DOI: 10.1145/3299852.3299853

Shobhit Mittal, Shubham Agarwal, M. Nigam

{"title":"Real Time Multiple Face Recognition: A Deep Learning Approach","authors":"Shobhit Mittal, Shubham Agarwal, M. Nigam","doi":"10.1145/3299852.3299853","DOIUrl":"https://doi.org/10.1145/3299852.3299853","url":null,"abstract":"Though a lot of research has already been done in the field of Face Recognition, one amongst the remaining challenges is recognizing multiple faces in weird conditions in a large group size. A robust face recognition system has been developed which detects faces in multiple, occluded, posed images obtained under low illumination conditions. The detector is a trained 34 layered Residual Network which obtains an accuracy of 98.4% on Visual Geometry Group Dataset [1]. A hybrid model has been proposed by combining the Residual Network detector with the novel approach of face embedding using triplet loss function [2] for recognition. The numerical and graphical results attached in the report depict the effectiveness of the proposed model for a variety of conditions. A 22 layered Inception Network has been trained for feature extraction and it achieves an accuracy of 99.5% on Labeled Faces in the Wild Dataset [3]. To achieve a similar accuracy on real life scenarios different methods like dimensionality reduction and data augmentation have been implemented. A mobile application has also been developed which utilizes the above described hybrid model for identification of people present in a large group. This application outweighs the fingerprint biometric in terms of speed, cost and group size.","PeriodicalId":210874,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Medicine and Image Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128408053","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: 15

A System for Disguised Face Recognition with Convolution Neural Networks 基于卷积神经网络的伪装人脸识别系统

Proceedings of the 2018 International Conference on Digital Medicine and Image Processing Pub Date : 2018-11-12 DOI: 10.1145/3299852.3299858

Kuo-Ming Hung, Jin-An Wu, Chia-Hung Wen, Li-Ming Chen

{"title":"A System for Disguised Face Recognition with Convolution Neural Networks","authors":"Kuo-Ming Hung, Jin-An Wu, Chia-Hung Wen, Li-Ming Chen","doi":"10.1145/3299852.3299858","DOIUrl":"https://doi.org/10.1145/3299852.3299858","url":null,"abstract":"Face recognition technology has been quite advanced in recent years and has been applied to various daily necessities and applications. However, people may make a false positive feature of the masked camouflage face because of makeup or wearing different equipment. In this paper, a two-stage disguise face recognition method based on CNN is proposed for the disguised face wearing equipment. In the first stage, we train a network that identifies the type of equipment and extracts the remaining faces that are not disguised. In the second stage of identification, the extracted remaining faces use the identified network for identity identification. The experimental results show that the proposed method has reached an average of 97.6% accuracy in the first stage of equipment type recognition. In the second stage of disguise face identification, 72.4% identification rate was obtained. The proposed method in this paper has reached the identification rate of the disguise identification research in recent years. The results of the above two stages show that the proposed method can effectively identify the type of disguise worn when people wear disguise. Then, the facial information of the disguise is removed to achieve a certain identity recognition effect.","PeriodicalId":210874,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Medicine and Image Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122711670","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