{"title":"A SVM-based method for face recognition using a wavelet PCA representation of faces","authors":"M. Safari, M. Harandi, Babak Nadjar Araabi","doi":"10.1109/ICIP.2004.1419433","DOIUrl":"https://doi.org/10.1109/ICIP.2004.1419433","url":null,"abstract":"This paper proposes a new method of face representation which is used for face recognition by SVM. For face representation we have used a two-step method, first two-dimensional discrete wavelet transform (DWT) is used to transform the faces to a more discriminated space and then principal component analysis (PCA) is applied. The proposed method produced a significant improvement which includes a substantial reduction in error rate and in time of processing during the obtaining PCA orthonormal basis.","PeriodicalId":184798,"journal":{"name":"2004 International Conference on Image Processing, 2004. ICIP '04.","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122714756","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}
{"title":"A fast and adaptive method for image contrast enhancement","authors":"Zeyun Yu, C. Bajaj","doi":"10.1109/ICIP.2004.1419470","DOIUrl":"https://doi.org/10.1109/ICIP.2004.1419470","url":null,"abstract":"In this paper we describe a fast approach for image contrast enhancement, based on localized contrast manipulation. Our approach is not only last and easy to implement, but also has several other promising properties (adaptive, multiscale, weighted localization, etc.). We will also discuss in this paper an anisotropic version of our approach. Several examples of medical images, including brain MR images, chest CT images and mammography images, will be provided to demonstrate the performance of our approach.","PeriodicalId":184798,"journal":{"name":"2004 International Conference on Image Processing, 2004. ICIP '04.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122844471","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}
{"title":"Concurrent encoding in hierarchical trees for wavelet based image compression","authors":"Jing-Xin Wang, Fanghui Cheng, A. Su","doi":"10.1109/ICIP.2004.1421787","DOIUrl":"https://doi.org/10.1109/ICIP.2004.1421787","url":null,"abstract":"Wavelet based compression approaches becomes very popular in the last few years. In this paper, a method called CEIHT (concurrent encoding in hierarchical trees) rooted from SPIHT is proposed. This method tries to explore the relationship between adjacent resolution levels and encodes multiple coefficients with three fixed Huffman tables at the same time. Consistent coding efficiency improvement is achieved compared to the original SPIHT. Though wavelet based compression methods usually encode on a whole image, block based approaches have to be applied in many low-cost embedded applications because their memory space and computation power are usually limited. The proposed method is especially efficient in such cases. The performance of the proposed method is compared favorably to the highly acclaimed EBCOT based J2K when small coding block sizes are necessary.","PeriodicalId":184798,"journal":{"name":"2004 International Conference on Image Processing, 2004. ICIP '04.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130097297","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}
{"title":"Toward an improved error metric","authors":"Q. Tian, Q. Xue, Jie Yu, N. Sebe, Thomas S. Huang","doi":"10.1109/ICIP.2004.1421533","DOIUrl":"https://doi.org/10.1109/ICIP.2004.1421533","url":null,"abstract":"In many computer vision algorithms, the well known Euclidean or SSD (sum of the squared differences) metric is prevalent and justified from a maximum likelihood perspective when the additive noise is Gaussian. However, Gaussian noise distribution assumption is often invalid. Previous research has found that other metrics such as double exponential metric or Cauchy metric provide better results, in accordance with the maximum likelihood approach. In this paper, we examine different error metrics and provide a theoretical approach to derive a rich set of nonlinear estimations. Our results on image databases show more robust results are obtained for noise estimation based on the proposed error metric analysis.","PeriodicalId":184798,"journal":{"name":"2004 International Conference on Image Processing, 2004. ICIP '04.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116117191","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}
{"title":"Blood flow generation in B-mode ultrasound images of the carotid artery","authors":"A. Hamou, M. El-Sakka","doi":"10.1109/ICIP.2004.1421730","DOIUrl":"https://doi.org/10.1109/ICIP.2004.1421730","url":null,"abstract":"This paper presents a new blood flow detection scheme, where a novel painting segmentation algorithm is utilized in order to achieve the accurate area of lumens, within carotid artery B-mode ultrasound images. The scheme allows for user-defined thresholding providing tolerance levels within the contour detection process in order to overcome crippling interference noise and degrading artifacts in ultrasound images. Since plaque buildup obstructs the flow of blood in the carotid artery, this scheme can identify these severe plaque-thickened inflamed regions, through detecting high and low levels of blood flow.","PeriodicalId":184798,"journal":{"name":"2004 International Conference on Image Processing, 2004. ICIP '04.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115316089","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}
{"title":"Redundant image representations in security applications","authors":"P. Jost, P. Vandergheynst, P. Frossard","doi":"10.1109/ICIP.2004.1421521","DOIUrl":"https://doi.org/10.1109/ICIP.2004.1421521","url":null,"abstract":"To be efficient, data protection algorithms should generally exploit the properties of the media information in the transform domain. In this paper, we will advocate the use of nonlinear image approximations using highly redundant dictionaries, for security algorithms. We show that a flexible image representation based on a multidimensional and geometry-based coding scheme, has precious attributes for security information embedding. Redundant expansions provide very good approximation properties, as well as an increased resiliency to coding noise and a simple stream structure enables easy manipulations. This paper describes simple examples of image scrambling and watermarking applications, based on a matching pursuit image coder. It illustrates the very interesting potential of redundant decompositions for data protection and security applications.","PeriodicalId":184798,"journal":{"name":"2004 International Conference on Image Processing, 2004. ICIP '04.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123181692","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}
Jonathan D. Rymel, John-Paul Renno, D. Greenhill, J. Orwell, Graeme A. Jones
{"title":"Adaptive eigen-backgrounds for object detection","authors":"Jonathan D. Rymel, John-Paul Renno, D. Greenhill, J. Orwell, Graeme A. Jones","doi":"10.1109/ICIP.2004.1421436","DOIUrl":"https://doi.org/10.1109/ICIP.2004.1421436","url":null,"abstract":"Most tracking algorithms detect moving objects by comparing incoming images against a reference frame. Crucially, this reference image must adapt continuously to the current lighting conditions if objects are to be accurately differentiated. In this work, a novel appearance model method is presented based on the eigen-background approach. The image can be efficiently represented by a set of appearance models with few significant dimensions. Rather than accumulating the necessarily enormous training set to generate the eigen model, the described technique builds and adapts the eigen-model online evolving both the parameters and number of significant dimension. For each incoming image, a reference frame may be efficiently hypothesized from a subsample of the incoming pixels. A comparative evaluation that measures segmentation accuracy using large amounts of manually derived ground truth is presented.","PeriodicalId":184798,"journal":{"name":"2004 International Conference on Image Processing, 2004. ICIP '04.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116629922","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}
{"title":"A set theoretic approach to target detection using spectral signature statistics","authors":"David M. Rouse, H. Trussell","doi":"10.1109/ICIP.2004.1421594","DOIUrl":"https://doi.org/10.1109/ICIP.2004.1421594","url":null,"abstract":"Pixels in hyperspectral images usually contain spectra from several classifiable objects, so that the recorded pixel is a mixture of the classes. Current methods estimate the proportion of each class using a set of spectral signatures describing only the class means. Since the means are known only by estimation methods, we introduce an approach that also incorporates the variation inherent in this estimation. The total least squares approach using projections onto convex sets (POCS) produces improved performance over simple maximum likelihood methods, even one that also uses the constraint sets and POCS.","PeriodicalId":184798,"journal":{"name":"2004 International Conference on Image Processing, 2004. ICIP '04.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116914444","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}
{"title":"secure media streaming & secure adaptation for non-scalable video","authors":"J. Apostolopoulos","doi":"10.1109/ICIP.2004.1421415","DOIUrl":"https://doi.org/10.1109/ICIP.2004.1421415","url":null,"abstract":"Two important capabilities in media streaming are (1) adapting the media for the time-varying available network bandwidth and diverse client capabilities, and (2) protecting the security of the media. Providing both end-to-end security and adapting at a (potentially untrusted) sender or mid-network node or proxy can be solved via a framework called secure scalable streaming (SSS) which provides the ability to transcode the content without requiring decryption. In addition, this enables secure transcoding to be performed in a R-D optimized manner. The original SSS work was performed for scalably coded media. This paper examines its potential application to non-scalable media. Specifically, we examine the problems of how to scale non-scalable H.264/MPEG-4 AVC video and how to do it securely. We first show, perhaps surprisingly, (hat the importance of different P-frames in a sequence can vary by two orders of magnitude. Then we propose two approaches for securely streaming and adapting encrypted H.264 video streams in an R-D optimized manner using (1) secure-media R-D hint tracks, and (2) secure scalable packets. While we can not scale the bit rate of encrypted non-scalable H.264 to the same extent possible for scalably coded media, our method does provide some scaling capability and more importantly provides 4-8 dB gain compared to conventional approaches.","PeriodicalId":184798,"journal":{"name":"2004 International Conference on Image Processing, 2004. ICIP '04.","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117054373","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}
Vijay Venkataraman, Sabeshan Srinivasan, A. Stefanidis
{"title":"Object color propagation in an unregistered distributed video sensor network","authors":"Vijay Venkataraman, Sabeshan Srinivasan, A. Stefanidis","doi":"10.1109/ICIP.2004.1421707","DOIUrl":"https://doi.org/10.1109/ICIP.2004.1421707","url":null,"abstract":"In this paper we address the use of object color characteristics as a predominant linking parameter between disjoint video scenes in unregistered distributed video sensor networks. In our approach we proceed by tracking objects in high-resolution video scenes using accumulated frame differencing and morphological techniques. We continue by minimizing shadow effects in object tracking through background detection, in order to obtain more accurate object color signatures. These signatures are then used to link trajectory fragments across disjoint video feeds in a sensor network. We present experimental results to demonstrate the performance of our approach, especially addressing the effect of resolution in tracking, and the role of color signatures as a key linking element when video scene registration is not possible.","PeriodicalId":184798,"journal":{"name":"2004 International Conference on Image Processing, 2004. ICIP '04.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121116720","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}