2009 IEEE International Conference on Signal and Image Processing Applications最新文献_第4页

Automatic MRI brain segmentation using local features, Self-Organizing Maps, and watershed 使用局部特征、自组织图和分水岭的自动MRI脑分割

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478631

Mehryar Emambakhsh, Mohammad Hossein Sedaaghi

引用次数: 3

Traffic flow simulation at an unsignalized T-junction using Monte Carlo Markov Chains 基于蒙特卡洛马尔可夫链的无信号t型交叉口交通流模拟

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478675

Wong Xin Ci, Syed Khaleel Ahmed, F. Zulkifli, A. Ramasamy

引用次数: 6

Convergence improvement of the LMS adaptive noise canceller using low distortion filter banks 利用低失真滤波器组改进LMS自适应消噪的收敛性

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478720

A.O. Abid Noor, Salina Abdul Samad, A. Hussain

引用次数: 0

Video-rate image segmentation by means of region splitting and merging 基于区域分割和合并的视频速率图像分割

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478693

K. Aneja, F. Laguzet, L. Lacassagne, A. Mérigot

引用次数: 12

Facial feature detection and tracking in a new multimodal technology-enhanced learning environment for social communication 一种新的多模态技术增强的社交学习环境中的面部特征检测和跟踪

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478661

Jingying Chen, Oliver Lemon

引用次数: 10

On GCL based preamble for time synchronization in OFDM system 基于GCL的OFDM系统时间同步前导

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478672

D.K. Yanti, V. Jeoti

引用次数: 0

An extraction computational algorithm based on the Morlet wavelet coefficient spectrum 一种基于Morlet小波系数谱的提取计算算法

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478722

T. E. Putra, S. Abdullah, M. Nuawi

引用次数: 1

The effect of input limiting on linear and nonlinear filters for the removal of impulsive noise 输入限制对去除脉冲噪声的线性和非线性滤波器的影响

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478605

S. Masra, M. Muhammad, R. Sapawi

{"title":"The effect of input limiting on linear and nonlinear filters for the removal of impulsive noise","authors":"S. Masra, M. Muhammad, R. Sapawi","doi":"10.1109/ICSIPA.2009.5478605","DOIUrl":"https://doi.org/10.1109/ICSIPA.2009.5478605","url":null,"abstract":"This paper investigates the effects of input limiting on the performance of linear and nonlinear filters when the input signal is contaminated by impulsive noise modeled as an alpha-stable (α-stable) random process. The nonlinear filters chosen are from the median family filters: median and recursive median filter, and Wiener filter from the linear filter group. The hypothesis being tested in this paper is that front-end limiting will increased the performance of median filter when the noise was extremely impulsive. Each image has been analyzed for four impulsive noise parameter values, α = 2, 1.5, 1, and 0.5 which cover a range of noise behaviors from Gaussian, through Cauchy, to impulsive. One method of handling very large impulses is to incorporate in the receiver over-voltage protection in the form of an analog limiter which clips the received signal before the filtering process. The simulation results obtained suggest that the front-end limiting enhances the performance of median filters in dealing with images, where it eliminates the highly impulsive noise while preserving useful detail in the image.","PeriodicalId":400165,"journal":{"name":"2009 IEEE International Conference on Signal and Image Processing Applications","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114998995","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

Characteristics of mobility models for mobile ad hoc networks 移动自组织网络的移动模型特征

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478718

Lama J. Madany, M. Madkour, Abdulrahman H. Al-Talhi

引用次数: 7

Vehicle speed detection system 车速检测系统

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478629

C. Pornpanomchai, Kaweepap Kongkittisan

{"title":"Vehicle speed detection system","authors":"C. Pornpanomchai, Kaweepap Kongkittisan","doi":"10.1109/ICSIPA.2009.5478629","DOIUrl":"https://doi.org/10.1109/ICSIPA.2009.5478629","url":null,"abstract":"This research intends to develop the vehicle speed detection system using image processing technique. Overall works are the software development of a system that requires a video scene, which consists of the following components: moving vehicle, starting reference point and ending reference point. The system is designed to detect the position of the moving vehicle in the scene and the position of the reference points and calculate the speed of each static image frame from the detected positions. The vehicle speed detection from a video frame system consists of six major components: 1) Image Acquisition, for collecting a series of single images from the video scene and storing them in the temporary storage. 2) Image Enhancement, to improve some characteristics of the single image in order to provide more accuracy and better future performance. 3) Image Segmentation, to perform the vehicle position detection using image differentiation. 4) Image Analysis, to analyze the position of the reference starting point and the reference ending point, using a threshold technique. 5) Speed Detection, to calculate the speed of each vehicle in the single image frame using the detection vehicle position and the reference point positions, and 6) Report, to convey the information to the end user as readable information. The experimentation has been made in order to assess three qualities: 1) Usability, to prove that the system can determine vehicle speed under the specific conditions laid out. 2) Performance, and 3) Effectiveness. The results show that the system works with highest performance at resolution 320×240. It takes around 70 seconds to detect a moving vehicle in a video scene.","PeriodicalId":400165,"journal":{"name":"2009 IEEE International Conference on Signal and Image Processing Applications","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128593375","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: 39