2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)最新文献_第8页

Content-based fauna image retrieval system 基于内容的动物图像检索系统

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120594

M. Mustaffa, Wong San San

{"title":"Content-based fauna image retrieval system","authors":"M. Mustaffa, Wong San San","doi":"10.1109/ICSIPA.2017.8120594","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120594","url":null,"abstract":"Many animal species exist in this world and there are always new species being discovered each year. Therefore, it is very important that these valuable species be documented properly to be referred to in future. Numerous information retrieval systems for managing and documenting animal species today only allow users to search animal images and descriptions online via text-based input. Therefore, people without knowledge on the animal species or without Internet access are not able to search using the systems. Motivated by these issues, the focus of this work is to construct a colour-shape content-based image representation for fauna. Two orders of the Colour Moment are used to represent the colour feature while the i-means approach is used to represent the shape feature. Based on the conducted quantitative and qualitative studies, the proposed fusion method together with the Content-based Image Retrieval (CBIR) system are found to be very effective in retrieving animal images similar to the given query, able to provide reliable and useful information on animal species, an easy system to interact with, and has easy to understand and user-friendly interfaces.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"21 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":"115079839","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

Retrieving YouTube video by sentiment analysis on user comment 基于用户评论情感分析的YouTube视频检索

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120658

Hanif Bhuiyan, Jinat Ara, Rajon Bardhan, Md. Rashedul Islam

引用次数: 52

EEG biomarker of Sleep Apnoea using discrete wavelet transform and approximate entropy 基于离散小波变换和近似熵的睡眠呼吸暂停脑电生物标记

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120631

S. N. M. Usak, S. Sugiman, N. Sha'ari, Mugunthan Kaneson, H. Abdullah, N. Noor, C. Patti, Chamila Dissanayaka, D. Cvetkovic

引用次数: 2

Remote sensing images classification using moment features and attribute profiles 基于矩特征和属性轮廓的遥感图像分类

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120578

Niloofar Ghasemi Roochi, H. Ghassemian, F. Mirzapour

{"title":"Remote sensing images classification using moment features and attribute profiles","authors":"Niloofar Ghasemi Roochi, H. Ghassemian, F. Mirzapour","doi":"10.1109/ICSIPA.2017.8120578","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120578","url":null,"abstract":"Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. Remote sensing is used in numerous fields, including geography, land surveying and most Earth Science disciplines. In supervised classification, all of the feature extraction methods try to increase the accuracy of classification and simultaneously time of computation. At the present work, we use the moments and Attribute Morphology Profiles (APs) to extract texture information from satellite panchromatic images. We use four conventional moments in pattern recognition such as Geometric, Chebyshev, Legendre and Zernike moments and APs to extract features from remote sensing image. An MP is constructed based on the repeated use of openings and closings by reconstruction of a structuring elements (SE) of an increasing size, applied to a scalar image. Then, we use those two set of features together. The well-known support vector machine (SVM) is used for supervised classification. We compare our proposed method with moments and APs. Different criteria such as average accuracy, overall accuracy, κ statistic and computation time are used for assessment of classification performance.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"180 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":"133706239","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

Development of low-cost vision-based falling sphere viscometer 低成本视觉落球粘度计的研制

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120621

Riady Siswoyo Jo, H. S. Jo, Almon Chai

引用次数: 2

Augmented reality assisted localization for indoor navigation on embedded computing platform 基于嵌入式计算平台的增强现实辅助室内导航定位

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120589

M. Malek, P. Sebastian, M. Drieberg

引用次数: 8

Signal processing and classification for identification of clinically important parameters during neonatal resuscitation 新生儿复苏过程中重要临床参数识别的信号处理与分类

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120672

Jarle Urdal, K. Engan, T. Eftestøl, H. Kidanto, L. Yarrot, J. Eilevstjønn, H. Ersdal

{"title":"Signal processing and classification for identification of clinically important parameters during neonatal resuscitation","authors":"Jarle Urdal, K. Engan, T. Eftestøl, H. Kidanto, L. Yarrot, J. Eilevstjønn, H. Ersdal","doi":"10.1109/ICSIPA.2017.8120672","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120672","url":null,"abstract":"Neonatal mortality is a global challenge. One million newborns die each year within their first 24 hours as a result of complications during labour and birth asphyxia. Most of these deaths happen in low resource settings. However, basic resuscitation at birth can increase newborn survival. Identification of initial factors and simple therapeutic strategies determinant for neonatal outcome can aid health care workers provide the best follow-up during resuscitation. In this work, the initial condition of the newborn, the treatment given, and early heart rate response from manual bag mask ventilation are parameterized. The features are investigated in a machine learning framework to identify which features are determinant for the different outcomes. Using a selection of the defined features, an identification rate of 89% for newborns in the normal group, and an identification rate of 74% for episodes ending in death was found. This points to the direction of identifying the important factors of newborn survival.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"34 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120853888","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

Predicting dengue cases by aggregation of climate variable using manifold learning 利用流形学习的气候变量集合预测登革热病例

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120670

Shermon S. Mathulamuthu, V. Asirvadam, S. Dass, B. Gill

引用次数: 4

Pulse width modulation (PWM) signals using spiking neuronal networks 脉冲宽度调制(PWM)信号使用尖峰神经元网络

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120602

Maisam Jalilian, M. Nouri, A. Ahmadi, Nabeeh Kandalaft

引用次数: 5

Evaluation of simulated VEP signals on basis of Higuchi and Katz's algorithm 基于Higuchi和Katz算法的仿真VEP信号评估

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120627

S. S. M. Radzi, V. Asirvadam, S. Dass, Duma Kristina Yanti Hutapea

引用次数: 2