Proceedings of the 2nd International Conference on Vision, Image and Signal Processing最新文献_第2页

Enhancing Boundary for Video Object Segmentation 增强视频对象分割的边界

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271581

Qi Zhang, Xiaoqiang Lu, Yuan Yuan

{"title":"Enhancing Boundary for Video Object Segmentation","authors":"Qi Zhang, Xiaoqiang Lu, Yuan Yuan","doi":"10.1145/3271553.3271581","DOIUrl":"https://doi.org/10.1145/3271553.3271581","url":null,"abstract":"Video object segmentation aims to separate objects from background in successive video sequence accurately. It is a challenging task as the huge variance in object regions and similarity between object and background. Among previous methods, inner region of an object can be easily separated from background while the region around object boundary is often classified improperly. To address this problem, a novel video object segmentation method is proposed to enhance the object boundary by integrating video supervoxel into Convolutional Neural Network (CNN) model. Supervoxel is exploited in our method for its ability of preserving spatial details. The proposed method can be divided into four steps: 1) convolutional feature of video is extracted with CNN model; 2) supervoxel feature is constructed through averaging the convolutional features within each supervoxel to preserve spatial details of video; 3) the supervoxel feature and original convolutional feature are fused to construct video representation; 4) a softmax classifier is trained based on video representation to classify each pixel in video. The proposed method is evaluated both on DAVIS and Youtube-Objects datasets. Experimental results show that by considering supervoxel with spatial details, the proposed method can achieve impressive performance for video object segmentation through enhancing object boundary.","PeriodicalId":414782,"journal":{"name":"Proceedings of the 2nd International Conference on Vision, Image and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125980799","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

Speech Sound Classification and Estimation of Optimal Order of LPC Using Neural Network 基于神经网络的语音分类及LPC最优阶数估计

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271611

M. A. Sankar, M. Aiswariya, Dominic Anna Rose, B. Anushree, D. Shree, P. Lakshmipriya, P. S. Sathidevi

{"title":"Speech Sound Classification and Estimation of Optimal Order of LPC Using Neural Network","authors":"M. A. Sankar, M. Aiswariya, Dominic Anna Rose, B. Anushree, D. Shree, P. Lakshmipriya, P. S. Sathidevi","doi":"10.1145/3271553.3271611","DOIUrl":"https://doi.org/10.1145/3271553.3271611","url":null,"abstract":"Speech codec which is an integral part of most of the communication standards consists of a Voice activity detector (VAD) module followed by an encoder that uses Linear Predictive Coding (LPC). These two modules have a lot of potential for improvements that can yield low bit-rates without compromising quality. VAD is used for detecting voice activity in the input signal, which is an important step in achieving high efficiency speech coding. LPC analysis of input speech at an optimal order can assure maximum SNR and thereby perceptual quality while reducing the transmission bit-rate. This paper proposes a novel method to classify speech into Voiced/ Unvoiced/ Silence/ Music/ Background noise (V/UV/S/M/BN) frames and to find optimal order of LPC for each frame using neural network. The speech sound classifier module gives classification of frames into five categories with very high accuracy. Choosing the order predicted by neural network as the optimal LPC order for voiced frames while keeping a low order for unvoiced frames maintains the reconstruction quality and brings down the bit-rate.","PeriodicalId":414782,"journal":{"name":"Proceedings of the 2nd International Conference on Vision, Image and Signal Processing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122376374","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: 5

A Route Reconstruction Method with Spare AP for Wireless Mesh Networks in Disaster Situation 灾害情况下带备用AP的无线Mesh网络路由重建方法

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271559

E. Dorj, K. Kinoshita

引用次数: 6

Hyperconnectivity by Simultaneous EEG Recordings during Turn-taking 轮流过程中同时脑电图记录的超连通性

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271589

Tianyu Yang, Yishu Yang, Changle Zhou

{"title":"Hyperconnectivity by Simultaneous EEG Recordings during Turn-taking","authors":"Tianyu Yang, Yishu Yang, Changle Zhou","doi":"10.1145/3271553.3271589","DOIUrl":"https://doi.org/10.1145/3271553.3271589","url":null,"abstract":"Turn-taking is a common scene in our daily life, however, the neural mechanism behind it is not fully understood yet. Researchers have proposed several theories to explain this phenomenon, and one of these theories is the oscillator model. In this model, the brains of the speaker and the listener are described as two \"oscillators\" and become mutually entrained during turn-taking. EEG hyperscanning is a method for studying two or more individuals simultaneously with the objective of elucidating how co-variations in their neural activity are influenced by their behavioral and social interactions. Turn-taking, as a frequent social interaction, could be investigated with EEG hyperscanning technique. In this paper, we designed an experiment allowing us to simultaneously record the EEG signals of the subjects during turn-taking in conversations, and depicted the method to measure the \"hyperconnectivity\" (functional connectivity between the two brains) by means of Partial Directed Coherence. Our study showed that: (1) there are significant hyperconnectivity links between the speaker and the listener; (2) The hyperconnectivity links mostly direct from the speaker to the listener; (3) Hyperconnectivity links in Beta band are much denser than those in Alpha band; (4) The T8 electrode plays a key role in the hyperconnectivity network.","PeriodicalId":414782,"journal":{"name":"Proceedings of the 2nd International Conference on Vision, Image and Signal Processing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128038489","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

A Utility Tool for Personalised Medicine 个性化医疗的实用工具

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271562

Chetana Gavankar, Aditya Phatak, N. Thakkar, Vaidehi Patel, Bhoomi Pragda, Rutuja Lathkar

引用次数: 1

Wearable Technologies for Enhanced Soldier Situational Awareness 增强士兵态势感知的可穿戴技术

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271620

C. Korpela, A. Walker

引用次数: 3

Perceptually Lossless Image Compression with Error Recovery 感知无损图像压缩与错误恢复

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271602

C. Kwan, Eric Shang, T. Tran

引用次数: 13

Comparative Analysis of Emotion Detection from Facial Expressions and Voice Using Local Binary Patterns and Markov Models: Computer Vision and Facial Recognition 基于局部二值模式和马尔可夫模型的面部表情和语音情感检测的比较分析:计算机视觉与面部识别

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271574

Kennedy Chengeta

{"title":"Comparative Analysis of Emotion Detection from Facial Expressions and Voice Using Local Binary Patterns and Markov Models: Computer Vision and Facial Recognition","authors":"Kennedy Chengeta","doi":"10.1145/3271553.3271574","DOIUrl":"https://doi.org/10.1145/3271553.3271574","url":null,"abstract":"Emotion detection has been achieved widely in facial and voice recognition separately with considerable success. The 6 emotional categories coming out of the classification include anger, fear, disgust, happiness and surprise. These can be infered from one's facial expressions both in the form of micro and macro expressions. In facial expressions the emotions are derived by feature extracting the facial expressions in different facial poses and classifying the expression feature vectors derived. Similarly automatic classification of a person's speech's affective state has also been used in signal processing to give insights into the nature of emotions. Speech being a critical tool for communication has been used to derive the emotional state of a human being. Different approaches have been successfully used to derive emotional states either in the form of facial expression recognition or speech emotional recognition being used. Less work has looked at fusing the two approaches to see if this improves emotional recognition accuracy. The study analyses the strengths of both and also limitations of either. The study reveals that emotional derivation based on facial expression recognition and acoustic information complement each other and a fusion of the two leads to better performance and results compared to the audio or acoustic recognition alone.","PeriodicalId":414782,"journal":{"name":"Proceedings of the 2nd International Conference on Vision, Image and Signal Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114484417","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

Watermark Extraction under Print-Cam Process Using Wave Atoms Based Blind Digital Watermarking 基于波原子盲数字水印的印模水印提取

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271619

Fawad Ahmad, Lee-Ming Cheng

引用次数: 1

Redundant Dictionary Construction via Genetic Algorithm 基于遗传算法的冗余字典构建

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271604

Haipeng Li, C. Zheng, Jucheng Zhang

引用次数: 1