2013 IEEE International Conference on Signal and Image Processing Applications最新文献_第2页

Efficient, robust, and scale-invariant decomposition of Raman spectra 高效、稳健、尺度不变的拉曼光谱分解

2013 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2013-10-01 DOI: 10.1109/ICSIPA.2013.6708025

S. Bayraktar, B. Labitzke, J. Bader, R. Bornemann, P. Bolívar, A. Kolb

引用次数: 4

Modified Hopfield Neural Network Algorithm (MHNNA) for TSS mapping in Penang strait, Malaysia 改进Hopfield神经网络算法(MHNNA)在马来西亚槟城海峡的TSS测绘

2013 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2013-10-01 DOI: 10.1109/ICSIPA.2013.6708001

Ahmed Asal Kzar, M. MatJafri, H. Lim, K. N. Mutter, S. Syahreza

{"title":"Modified Hopfield Neural Network Algorithm (MHNNA) for TSS mapping in Penang strait, Malaysia","authors":"Ahmed Asal Kzar, M. MatJafri, H. Lim, K. N. Mutter, S. Syahreza","doi":"10.1109/ICSIPA.2013.6708001","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708001","url":null,"abstract":"The use of traditional ship sampling method of for environmental monitoring is time consuming, requires a high survey cost, and exert great efforts. In this study we classify one of the water pollutants which is the Total Suspended Solids (TSS) of polluted water in Penang strait, Malaysia by applying Modified Hopfield Neural Network Algorithm (MHNNA) on THEOS (Thailand Earth Observation System) image. The samples were collected from study area simultaneously with the airborne image acquisition. The samples locations were determined by using a handheld global positioning system (GPS), and the measurement of TSS concentrations was conducted in the lab as validation data (sea-truth data). By using algorithm (MHNNA) the concentrations of TSS have been classified according their varied values to produce the map. The map was colour-coded for visual interpretation. The investigation of efficiency of the proposed algorithm was based on dividing the validation data into two groups, the first group refers to standard samples for supervisor classification by the used algorithm. And the second group for test, where after classification we detect the second group data positions in the produced classes, then finding correlation coefficient (R) and root-mean-square-error (RMSE) between the first group data and the second group data according to their correspondence in the classes. The observations were high (R=0.899) with low (RMSE=17.687). This study indicates that TSS mapping of polluted water can be carried out using remote sensing technique by the application of MHNNA on THEOS satellite data over Penang strait, Malaysia.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"05 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129315314","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

Filter design for synthesis of musical notes: A multidimensional feature-based approach 用于合成音符的滤波器设计:一种基于多维特征的方法

2013 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2013-10-01 DOI: 10.1109/ICSIPA.2013.6707986

S. Ramamurthy, M. Raghavan

引用次数: 0

Power system harmonics estimation using sliding window based LMS 基于滑动窗口LMS的电力系统谐波估计

2013 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2013-10-01 DOI: 10.1109/ICSIPA.2013.6708027

Hussam M. M. Alhaj, N. M. Nor, V. Asirvadam, M. F. Abdullah

引用次数: 8

Amplitude characteristics of linear frequency modulation signal in FRFT domain 线性调频信号在频域内的幅值特性

2013 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2013-10-01 DOI: 10.1109/ICSIPA.2013.6708045

Yan Ma, Rui Wang, Jinxiang Du

引用次数: 6

Face recognition based on opposition particle swarm optimization and support vector machine 基于对立粒子群优化和支持向量机的人脸识别

2013 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2013-10-01 DOI: 10.1109/ICSIPA.2013.6708043

Mohammed Hasan, S. Abdullah, Z. Othman

{"title":"Face recognition based on opposition particle swarm optimization and support vector machine","authors":"Mohammed Hasan, S. Abdullah, Z. Othman","doi":"10.1109/ICSIPA.2013.6708043","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708043","url":null,"abstract":"One of the most recently developed face recognition technique has utilized PSO-SVM, this method lacks in the initial phase of the PSO technique. That is in PSO; initially the populations are generated in random manner. Due to this random process, the population results may also be in random. Thus, it is not certain that this method will produce precise result. Hence to avoid this drawback, a modified face recognition method is proposed in this paper. Here, a new face recognition method based on Opposition based PSO with SVM (OPSO-SVM) is introduced. To accomplish the face recognition with our proposed OPSO-SVM, initially feature extraction process is carried out on the image database. In the feature extraction process, the efficient features are extracted and then given to the SVM training and testing process. In OPSO, the populations are generated in two ways: one is random population as same as the normal PSO technique and the other is opposition population, which is based on the random population values. The optimized parameters in SVM by OPSO efficiently perform the face recognition process. Two human face databases FERET and YALE are utilized to analyze the performance of our proposed OPSO-SVM technique and also this OPSO-SVM is compared with PSO-SVM and standard SVM techniques.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114900604","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: 11

Human pose tracking in low-dimensional subspace using manifold learning by charting 基于流形学习的低维子空间人体姿态跟踪

2013 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2013-10-01 DOI: 10.1109/ICSIPA.2013.6708014

Sanjay Saini, D. R. A. Rambli, S. Sulaiman, M. N. Zakaria

{"title":"Human pose tracking in low-dimensional subspace using manifold learning by charting","authors":"Sanjay Saini, D. R. A. Rambli, S. Sulaiman, M. N. Zakaria","doi":"10.1109/ICSIPA.2013.6708014","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708014","url":null,"abstract":"Tracking full articulated human body motion is a very challenging task due to the high dimensionality of human skeleton model, self-occlusion and large variety of body poses. In this work, we explore a novel Low-dimensional Manifold Learning (LDML) approach to overcome high dimensional search space of human model. Low-dimensional demonstration not only delivers a compact tractable search space, but it is efficient to capture general human pose variations. The key contribution of this work is an algorithm of Quantum-behaved Particle Swarm Optimization (QPSO) for pose optimization in latent space of human motion. Firstly, we learn the human motion model in low-dimensional latent space using nonlinear dimension reduction technique charting based on hierarchical strategy. Increased dependence provision is carried out using hierarchy strategic measures in charting, which improves accuracy in higher flexibility and adaptation. Then we applied QPSO algorithm to estimate the human poses in low-dimensional latent space. Preliminary experimental tracking results show that our approach is able to give good accuracy as compared to conventional state-of-the-arts methods.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"s2-36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117122656","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

Modified HL contrast enhancement technique for breast MR images 乳腺MR图像的改进HL对比度增强技术

2013 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2013-10-01 DOI: 10.1109/ICSIPA.2013.6708033

S. S. Chong, K. Sim, M. E. Nia

引用次数: 7

An windowed frequency domain interpolation algorithms for damped sinusoidal signals 一种阻尼正弦信号的窗频域插值算法

2013 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2013-10-01 DOI: 10.1109/ICSIPA.2013.6708021

R. Diao, Qingfeng Meng, Yumei Liang

引用次数: 2

MFCC based frog identification system in noisy environment 噪声环境下基于MFCC的青蛙识别系统

2013 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2013-10-01 DOI: 10.1109/ICSIPA.2013.6707989

H. Jaafar, D. A. Ramli, S. Shahrudin

引用次数: 12