2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR)最新文献

Heritage Image Classification by Convolution Neural Networks 基于卷积神经网络的遗产图像分类

2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2018-04-05 DOI: 10.1109/MAPR.2018.8337517

Manh-Tu Vu, M. Beurton-Aimar, V. Le

引用次数: 5

Improving Human Action Recognition with Two-Stream 3D Convolutional Neural Network 用双流三维卷积神经网络改进人体动作识别

2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2018-04-05 DOI: 10.1109/MAPR.2018.8337518

Van-Minh Khong, Thanh-Hai Tran

{"title":"Improving Human Action Recognition with Two-Stream 3D Convolutional Neural Network","authors":"Van-Minh Khong, Thanh-Hai Tran","doi":"10.1109/MAPR.2018.8337518","DOIUrl":"https://doi.org/10.1109/MAPR.2018.8337518","url":null,"abstract":"Human action recognition has became a hot topic in recent years because it opens a wide range of applications such as video surveillance, assisted living, entertainment. Recently, advanced techniques relying on convolutional neural networks produced impressive improvement compared to traditional handcrafted features based techniques. Besides, literature researches also showed that the use of different streams of data will help to increase recognition performance. This paper proposes a method that exploits both RGB and optical flow for human action recognition. Specifically, we deploy a two stream convolutional neural network that takes RGB and optical flow computed from RGB stream as inputs. Each stream has architecture of an existing 3D convolutional neural network (C3D) which has been shown to be compact but efficient for the task of action recognition from video. Each stream works independently then is combined by early fusion or late fusion to output the recognition results. We show that the proposed two-stream 3D convolutional neural network (2stream C3D) outperforms one stream C3D on two benchmark datasets UCF101 (from 82.37% to 88.79%) and HMDB51 (from 48.43 % to 62.54%).","PeriodicalId":349558,"journal":{"name":"2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121477610","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: 23

Discriminative Deep Feature Learning for Facial Emotion Recognition 基于判别深度特征学习的面部情绪识别

2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2018-04-05 DOI: 10.1109/MAPR.2018.8337514

D. V. Sang, Le Tran Bao Cuong, Pham Thai Ha

引用次数: 12

Landmarks Detection by Applying Deep Networks 应用深度网络的地标检测

2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2018-04-05 DOI: 10.1109/MAPR.2018.8337519

V. Le, M. Beurton-Aimar, A. Zemmari, N. Parisey

引用次数: 1

A Vision Based Method for Automatic Evaluation of Germination Rate of Rice Seeds 一种基于视觉的水稻种子发芽率自动评价方法

2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2018-04-05 DOI: 10.1109/MAPR.2018.8337511

Thuy Thi Nguyen, Van-Nam Hoang, Thi-Lan Le, Thanh-Hai Tran, Hai Vu

{"title":"A Vision Based Method for Automatic Evaluation of Germination Rate of Rice Seeds","authors":"Thuy Thi Nguyen, Van-Nam Hoang, Thi-Lan Le, Thanh-Hai Tran, Hai Vu","doi":"10.1109/MAPR.2018.8337511","DOIUrl":"https://doi.org/10.1109/MAPR.2018.8337511","url":null,"abstract":"Rice is one of the most cultivated cereal in Asian countries and Vietnam in particular. Good seed germination is important for rice seed quality, that impacts the rice production and crop yield. Currently, seed germination evaluation is carried out manually by experienced persons. This is a tedious and time-consuming task. In this paper, we present a system for automatic evaluation of rice seed germination rate based on advanced techniques in computer vision and machine learning. We propose to use U-Net - a convolutional neural network - for segmentation and separation of rice seeds. Further processing such as computing distance transform and thresholding will be applied on the segmented regions for rice seed detection. Finally, ResNet is utilized to classify segmented rice seed regions into two classes: germinated and non- germinated seeds. Our contributions in this paper are three-fold. Firstly, we propose a framework which confirms that convolutional neural networks are better than traditional methods for both segmentation and classification tasks (with F1- scores of 93.38% and 95.66% respectively). Secondly, we deploy successfully the automatic tool in a real application for estimating rice germination rate. Finally, we introduce a new dataset of 1276 images of rice seeds from 7 to 8 seed varieties germinated during 6 to 10 days. This dataset is publicly available for research purpose.","PeriodicalId":349558,"journal":{"name":"2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114835467","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: 9

Nissl Stained Rat Brain Cell Image Analysis Post Exposure to Electromagnetic Fields Using Image Processing Techniques 利用图像处理技术分析电磁场暴露后尼氏染色大鼠脑细胞图像

2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2018-04-01 DOI: 10.1109/MAPR.2018.8337523

Bhakti Netke, Swati Dongre, Nalini Bhadouria, M. Bhattacharya

{"title":"Nissl Stained Rat Brain Cell Image Analysis Post Exposure to Electromagnetic Fields Using Image Processing Techniques","authors":"Bhakti Netke, Swati Dongre, Nalini Bhadouria, M. Bhattacharya","doi":"10.1109/MAPR.2018.8337523","DOIUrl":"https://doi.org/10.1109/MAPR.2018.8337523","url":null,"abstract":"Studies have shown that mobile phones which emit high-frequency electromagnetic fields have a bleak effect on the human brain. However, the results are found to be inconsistent pertaining to the exact nature of thump it causes with respect to the changes in the brain cell morphology, cell structure, cell shape, cell size, nuclei count and apoptosis. The paper intends to demonstrate novel techniques developed to elucidate the effect of electromagnetic fields (EMF) exposure on neuronal cells of post-natal rats as subjects. The brain cell images of the subjects, prior and post exposure to the electromagnetic fields (EMF) are obtained using the Cresyl Violet (Nissl) Staining method. The images are then subjected to analysis using pre-processing techniques such as average filtering combined with median filtering to denoise the Nissl stained cell images, image enhancement techniques like local enhancement using CLAHE algorithm for abating the pixel intensity variations. The image procured after pre-processing is then segmented using a hybrid segmentation technique that integrates adaptive single seed region growing and threshold based segmentation to obtain separate cells and analyzes them for apoptosis. A novel technique using distance transform with maxima propagation has been developed for automated cell counting. The end results intend to limn the accuracy of the developed cell counting and segmentation techniques over the manual approach. And also, to delineate the impact of electromagnetic fields (EMF) radiations beyond a threshold value at the neuronal level.","PeriodicalId":349558,"journal":{"name":"2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131278230","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

Enhancing Person Re-Identification Based on Recurrent Feature Aggregation Network 基于循环特征聚合网络的人再识别增强

2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2018-04-01 DOI: 10.1109/MAPR.2018.8337516

Hong-Quan Nguyen, Thuy-Binh Nguyen, Thi-Lan Le

{"title":"Enhancing Person Re-Identification Based on Recurrent Feature Aggregation Network","authors":"Hong-Quan Nguyen, Thuy-Binh Nguyen, Thi-Lan Le","doi":"10.1109/MAPR.2018.8337516","DOIUrl":"https://doi.org/10.1109/MAPR.2018.8337516","url":null,"abstract":"This paper proposes a method for video-based person re-identification. Motivated by the capacity of Recurrent Feature Aggregation Network (RFA-Net) that allows to aggregate the image-level features over time-steps and to yield a sequence level feature. Our work improves the person re- identification performance by (1) proposing to use Gaussian of Gaussian (GOG) - a more powerful feature at image level instead of LBP-Color as in the original work and (2) applying metric learning techniques for person matching. To analyze the role of image feature and metric learning, we have performed extensive experiments on two public benchmark datasets including PRID 2011 and iLIDS- VID. The proposed solution obtains impressive results on the PRID 2011 dataset. The matching rate at rank 1 is increased by 16.8% compared to that of original method. For the iLID-SVID dataset, the matching rate at rank 1 of proposed method is increased 1.1% whereas these values at the latter ranks are higher than those of the original method by approximately 4%. In additional, the mean and standard deviation of the rank where the correct matching is found of the proposed method on the PRID 2011 and the iLIDS-VID are 2.32, 4.33 and 4.61, 7.67 while those of the original work are 3.50, 5.42 and 8.03, 17.52, respectively. These obtained results emphasize the robustness of the proposed method compared to the original one.","PeriodicalId":349558,"journal":{"name":"2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132979616","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

Towards Real-Time Smile Detection Based on Faster Region Convolutional Neural Network 基于更快区域卷积神经网络的实时微笑检测

2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2018-04-01 DOI: 10.1109/MAPR.2018.8337524

Chi Cuong Nguyen, Giang Son Tran, Thi Phuong Nghiem, Nhat-Quang Doan, D. Gratadour, J. Burie, C. Luong

引用次数: 12

Blind Deconvolution Method Using Omnidirectional Gabor Filter-Based Edge Information 基于全向Gabor滤波器边缘信息的盲反卷积方法

2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2018-04-01 DOI: 10.1109/MAPR.2018.8337513

T. Do, X. Cui, Thi Hai Binh Nguyen, Hakil Kim, Van Huan Nguyen

引用次数: 1

Multi-Task Regression Learning for Prediction of Response Against a Panel of Anti-Cancer Drugs in Personalized Medicine 个性化医疗中一组抗癌药物反应预测的多任务回归学习

2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2018-04-01 DOI: 10.1109/MAPR.2018.8337521

Duc-Hau Le, Doanh Nguyen-Ngoc

{"title":"Multi-Task Regression Learning for Prediction of Response Against a Panel of Anti-Cancer Drugs in Personalized Medicine","authors":"Duc-Hau Le, Doanh Nguyen-Ngoc","doi":"10.1109/MAPR.2018.8337521","DOIUrl":"https://doi.org/10.1109/MAPR.2018.8337521","url":null,"abstract":"The main goal of medicine research is to precisely provide treatment for each patient. In particular, clinicians want to use right drugs and doses for each patient according to their biological characteristics to maximize treatment's efficiency. With support of high-throughput technologies, a large amount of -omics and drug response data has been generated for human's tumors and cell lines. This wealth data facilitates the research for predicting drug response for each patient before selecting drugs for intake. Many computational methods have been also proposed for this purpose using -omics data of cell lines to predict the response on untested drugs. Previous methods have assumed a linear/non-linear relationship between -omics data and drug response for individual drugs. However, it should be noted that drugs can be functionally related to others since they may share some chemical structures. In addition, clinicians are also interested in knowing which biomarkers affect the response. Therefore, in this study, we assumed a linear relation and proposed a method based on multi-task regression learning to predict response for not only one but a panel of drugs. Comparing with previous single-task linear regression method, we found that our method achieves better performance in terms of correlation coefficient for major number of drugs.","PeriodicalId":349558,"journal":{"name":"2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127439522","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