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2010 International Conference on Image Analysis and Signal Processing最新文献

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One kind of macrophages images edge detection method 一种巨噬细胞图像边缘检测方法
2010 International Conference on Image Analysis and Signal Processing Pub Date : 2010-04-09 DOI: 10.1109/IASP.2010.5476112
Ruihua Xia, Ping Wang, Qingwu Lai
{"title":"One kind of macrophages images edge detection method","authors":"Ruihua Xia, Ping Wang, Qingwu Lai","doi":"10.1109/IASP.2010.5476112","DOIUrl":"https://doi.org/10.1109/IASP.2010.5476112","url":null,"abstract":"Edge detection plays a fundamental role in higher level processing. Aiming at the actual macrophages images characteristic, this paper presents the method of image edge detection based on mathematical morphology. The image is first converted color space from RGB to YIQ. Then the image is segmented by Otsu threshold algorithm. And then the opening and closing of morphological filter are used to process the image. Finally, the edge detection operator based on morphology is carried out to extract the boundary of macrophages image. Experimental results show this method works successfully in the edge detection of macrophages microscopic images.","PeriodicalId":223866,"journal":{"name":"2010 International Conference on Image Analysis and Signal Processing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131690743","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
Image restoration of infrared focal plane array 红外焦平面阵列图像恢复
2010 International Conference on Image Analysis and Signal Processing Pub Date : 2010-04-09 DOI: 10.1109/IASP.2010.5476111
Liwei Hou, W. Xie, Fansheng Chen, M. Pan
{"title":"Image restoration of infrared focal plane array","authors":"Liwei Hou, W. Xie, Fansheng Chen, M. Pan","doi":"10.1109/IASP.2010.5476111","DOIUrl":"https://doi.org/10.1109/IASP.2010.5476111","url":null,"abstract":"Imaging system mentioned in this work is made up of a cryogenic infrared focal array (IR FPA). Images acquired from such system always have degradations due to diffraction and aberration in the optical system, atmospheric degradation source, relative motion between the camera and the original scene, integral sampling of the detector arrays, defocusing and so on. Restoration is essential to an infrared imaging system. The degradation model based on a convolution product is presented, and a restoration method based on a modified Wiener filter is proposed. Newton-Raphson method is used in the optimal parameter search procedure. The model is based on the uniform detectors assumption, but in practical applications such assumption cannot be satisfied. So a pre-process is needed to correct the nonuniformity to meet the assumption. Blind pixels compensation is also included in the pre-process procedure. Moreover, to avoid the ringing that may be created by Wiener filter, a boundary expansion method is applied before restoration procedure. To assess the restoration effect, we select image definition as the measurement criteria, and the calculation results show that the restored images have much higher definition than the degraded image. The restoration procedure is applied to an Oriental Pearl infrared image.","PeriodicalId":223866,"journal":{"name":"2010 International Conference on Image Analysis and Signal Processing","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130847038","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
Nonlinear filtering with multiple packet dropouts 多包丢包非线性滤波
2010 International Conference on Image Analysis and Signal Processing Pub Date : 2010-04-09 DOI: 10.1109/IASP.2010.5476156
Jinguang Chen, Jiancheng Li, Lili Ma
{"title":"Nonlinear filtering with multiple packet dropouts","authors":"Jinguang Chen, Jiancheng Li, Lili Ma","doi":"10.1109/IASP.2010.5476156","DOIUrl":"https://doi.org/10.1109/IASP.2010.5476156","url":null,"abstract":"This paper considers the nonlinear system filtering with packet dropouts. We assume that the packet arrived rate is known in advance but the sequence of packet dropouts is unknown. At first, we use the probability-weighted method to achieve a pseudo measurement sequence, and every pseudo measurement is the weighted value of the measurements acquired at the current time step and the prior time step. Some classical nonlinear filtering methods can be used via the pseudo measurement sequence and the dynamic equation of the system, and then the pseudo measurement unscented Kalman filter (PM_UKF) and the pseudo measurement particle filter (PM_PF) are given. This pseudo measurement sequence can be also used in the linear system, and its time complexity is lower than that of Sun's optimal filter at this time. Simulation results show the effectiveness of the proposed algorithms.","PeriodicalId":223866,"journal":{"name":"2010 International Conference on Image Analysis and Signal Processing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126536557","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
Face recognition using a color tensor framework 人脸识别使用颜色张量框架
2010 International Conference on Image Analysis and Signal Processing Pub Date : 2010-04-09 DOI: 10.1109/IASP.2010.5476150
Xiao-Ping Hu, Duan-Sheng Chen
{"title":"Face recognition using a color tensor framework","authors":"Xiao-Ping Hu, Duan-Sheng Chen","doi":"10.1109/IASP.2010.5476150","DOIUrl":"https://doi.org/10.1109/IASP.2010.5476150","url":null,"abstract":"Facial images are affected by multiple factors including facial geometries, expressions, viewpoints and illuminations. We apply multi-linear algebra to separate these factors and extract the people factor used for face recognition. Compared to standard PCA and its variants, the method allows for bigger changes in viewpoints and illuminations. Our method is based on the method, adding color information, using (2D)2-PCA for dimensionality reduction and centering face recognition in a color tensor framework. Good results are obtained by doing experiments on Weizman and Facepix database.","PeriodicalId":223866,"journal":{"name":"2010 International Conference on Image Analysis and Signal Processing","volume":"443 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116068836","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
Research and simulation of spatial spectrum estimation algorithm 空间频谱估计算法的研究与仿真
2010 International Conference on Image Analysis and Signal Processing Pub Date : 2010-04-09 DOI: 10.1109/IASP.2010.5476061
Yang Zhifei, Wang Yanqing, Wang Lei
{"title":"Research and simulation of spatial spectrum estimation algorithm","authors":"Yang Zhifei, Wang Yanqing, Wang Lei","doi":"10.1109/IASP.2010.5476061","DOIUrl":"https://doi.org/10.1109/IASP.2010.5476061","url":null,"abstract":"The spatial spectrum indicates the signal's energy distribution in all directions. If we can get the signal's spatial spectrum, then the signal's direction of arrival (DOA) is known. The paper makes researches on the spatial spectrum estimation algorithms for uniform linear array, and establishes three mathematical models of MUSIC, ESPRIT and Capon. Moreover, with different ranges of SNR, the paper simulates the passive direction finding on non-associated narrow-band signals, and measured the performance by MSE. By simulation, the paper analyzes the computation consumption, resolution and signal types which can deal with for these three algorithms. Moreover, we compare their estimation abilities and summarizes their advantages and disadvantages in different engineering requirements.","PeriodicalId":223866,"journal":{"name":"2010 International Conference on Image Analysis and Signal Processing","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124315763","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
Quantum fuzzy particle swarm optimization algorithm for image clustering 图像聚类的量子模糊粒子群优化算法
2010 International Conference on Image Analysis and Signal Processing Pub Date : 2010-04-09 DOI: 10.1109/IASP.2010.5476115
Q. Zhong, Min Yao, Wei Jiang
{"title":"Quantum fuzzy particle swarm optimization algorithm for image clustering","authors":"Q. Zhong, Min Yao, Wei Jiang","doi":"10.1109/IASP.2010.5476115","DOIUrl":"https://doi.org/10.1109/IASP.2010.5476115","url":null,"abstract":"In this paper, a novel quantum fuzzy particle swarm optimization (QFPSO) approach has been proposed for image clustering. The particle swarm optimization is used to search the global optimal clustering center. Moreover, the quantum encoding is introduced and the quantum operation is implemented on each particle to overcome the premature convergence problem effectively. The experimental results showed that QFPSO algorithm for image clustering performs much better than other contrast methods.","PeriodicalId":223866,"journal":{"name":"2010 International Conference on Image Analysis and Signal Processing","volume":"72 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123265235","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: 4
Enhanced point descriptors for dense stereo matching 增强点描述符密集立体匹配
2010 International Conference on Image Analysis and Signal Processing Pub Date : 2010-04-09 DOI: 10.1109/IASP.2010.5476124
H. Lang, Yongtian Wang, Xin Qi, Weiqing Pan
{"title":"Enhanced point descriptors for dense stereo matching","authors":"H. Lang, Yongtian Wang, Xin Qi, Weiqing Pan","doi":"10.1109/IASP.2010.5476124","DOIUrl":"https://doi.org/10.1109/IASP.2010.5476124","url":null,"abstract":"We propose a novel local feature descriptor named Enhanced Point Descriptor (referred to as EPD) for dense stereo matching applications. The existing local feature descriptors, e.g., SIFT and SURF, can only be used to represent sparse image extreme points which make stereo matching sparsely. We design EPDs to represent common image points. To generate an EPD, we first build image characteristics vectors for neighborhood points around interest point in a specific sampled window. An EPD is a covariance matrix of characteristics vectors for all sampled points. The image characteristics we used to build vectors include HSV color, Gaussian-weighted gradient norms and orientations, which make EPD robust to rotation, perspective and illumination change. Experimental results show that EPD's performance is superior to commonly used correlation windows methods in dense stereo matching.","PeriodicalId":223866,"journal":{"name":"2010 International Conference on Image Analysis and Signal Processing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123471526","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
Image denoising using Contourlet and two-dimensional Principle Component Analysis 基于Contourlet和二维主成分分析的图像去噪
2010 International Conference on Image Analysis and Signal Processing Pub Date : 2010-04-09 DOI: 10.1109/IASP.2010.5476106
Zhe Liu, Huanan Xu
{"title":"Image denoising using Contourlet and two-dimensional Principle Component Analysis","authors":"Zhe Liu, Huanan Xu","doi":"10.1109/IASP.2010.5476106","DOIUrl":"https://doi.org/10.1109/IASP.2010.5476106","url":null,"abstract":"This paper proposes a novel image denoising algorithm using the Contourlet transform and the two-dimensional Principle Component Analysis (2DPCA). The noise image can be decomposed by the Contourlet into directional subbands. The 2DPCA is then carried out to estimate the threshold for the image blocks in high frequency subbands. The soft thresholding shrinkage can hence be employed on the Contourlet coefficients without estimating the noise variance. The denoising algorithm is validated by numerical experiments on two images. Numerical results show that the proposed method can obtain higher PSNR than former methods.","PeriodicalId":223866,"journal":{"name":"2010 International Conference on Image Analysis and Signal Processing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125393327","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
A fast motion segmentation algorithm based on hypothesis test for surveillance video coding 基于假设检验的监控视频编码快速运动分割算法
2010 International Conference on Image Analysis and Signal Processing Pub Date : 2010-04-09 DOI: 10.1109/IASP.2010.5476186
L. Xuedong, Wang Hong
{"title":"A fast motion segmentation algorithm based on hypothesis test for surveillance video coding","authors":"L. Xuedong, Wang Hong","doi":"10.1109/IASP.2010.5476186","DOIUrl":"https://doi.org/10.1109/IASP.2010.5476186","url":null,"abstract":"Motion segmentation is an important task in video comprehension and object based video coding. This paper proposes a fast motion segmentation algorithm based on hypothesis test. At first, statistical model of camera noise is obtained offline. Then, pixels are classified into the moving and still by hypothesis test and a binary mask image is generated. Median filtering is used further to remove isolated spots. At last, macro block (MB) mask is formed according to the number of moving pixels inside MBs. Experimental results show the proposed “test for pixels - median filtering - MB mask” strategy is robust without increasing complexity.","PeriodicalId":223866,"journal":{"name":"2010 International Conference on Image Analysis and Signal Processing","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116387339","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
An adaptive threshold for the Canny Operator of edge detection 边缘检测Canny算子的自适应阈值
2010 International Conference on Image Analysis and Signal Processing Pub Date : 2010-04-09 DOI: 10.1109/IASP.2010.5476095
Yuankai Huo, Geng Wei, Yu-Dong Zhang, Le-nan Wu
{"title":"An adaptive threshold for the Canny Operator of edge detection","authors":"Yuankai Huo, Geng Wei, Yu-Dong Zhang, Le-nan Wu","doi":"10.1109/IASP.2010.5476095","DOIUrl":"https://doi.org/10.1109/IASP.2010.5476095","url":null,"abstract":"The thresholds play an important role in the Canny Operator which used in the image edge detection. Many self-adaptive threshold algorithms have been proposed to improve the performance of Canny Operator. The Otsu method is one of the most popular improvements. However the Otsu method can not automatically set the low threshold according to the different image intensity adaptively. In order to overcome this defect, an adaptive threshold algorithm for the Canny Operator was proposed which calculated the low threshold adaptively based on a probability model. Experiments show that this method produces better edge detection results both on objective and subjective evaluations than the Otsu method.","PeriodicalId":223866,"journal":{"name":"2010 International Conference on Image Analysis and Signal Processing","volume":"87 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126294178","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: 72
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