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2010 IEEE International Conference on Image Processing最新文献

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Constructing a sparse convolution matrix for shift varying image restoration problems 变换图像恢复问题的稀疏卷积矩阵构造
2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5651989
Stanley H. Chan
{"title":"Constructing a sparse convolution matrix for shift varying image restoration problems","authors":"Stanley H. Chan","doi":"10.1109/ICIP.2010.5651989","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5651989","url":null,"abstract":"Convolution operator is a linear operator characterized by a point spread functions (PSF). In classical image restoration problems, the blur is usually shift invariant and so the convolution operator can be characterized by one single PSF. This assumption allows one to use fast operations such as Fast Fourier Transform (FFT) to perform a matrix-vector computation efficiently. However, as in most of the video motion deblurring problems, the blur is shift variant and so the matrix-vector multiplication can be difficult to perform. In this paper, we propose an efficient method to construct the convolution matrix explicitly. We exploit the submatrix structure of the convolution matrix and systematically assigning values to the nonzero locations. For small to medium sized images, the convolution matrix gives superior speed than some state-of-art convolution operators.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115380350","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: 10
A novel shadow restoration algorithm based on atmospheric effects for aerial images 一种新的基于大气效应的航拍图像阴影恢复算法
2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5653549
Çağlar Aytekin, Aydin Alatan
{"title":"A novel shadow restoration algorithm based on atmospheric effects for aerial images","authors":"Çağlar Aytekin, Aydin Alatan","doi":"10.1109/ICIP.2010.5653549","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5653549","url":null,"abstract":"In aerial images, the performance of the segmentation and object recognition algorithms could suffer due to shadows in the scene. This effort describes a novel shadow restoration algorithm based on atmospheric effects and characteristics of sun light for aerial images. Firstly, shadow regions are detected exploiting the Rayleigh scattering phenomena and the well-known fact related to the low illumination intensity in the shadow regions. After detection, shadow restoration is achieved by first restoring partially occluded shadow areas, as a result of modeling these transition regions with a continuous function that considers shadow formations. Next, fully occluded shadow regions are restored by first segmenting the image into multiple uniformly illuminated regions, then multiplying the intensity values in these regions with a constant, which is determined by the ratio of intensities between each segment and its non-shadow neighborhood. The simulation results indicate improvements over similar work from the literature.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115454771","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
A probabilistic approach to realistic face synthesis 现实人脸合成的概率方法
2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5653024
Hyunjung Shim, I. Ha, Taehyun Rhee, J. D. Kim, Chang-Yeong Kim
{"title":"A probabilistic approach to realistic face synthesis","authors":"Hyunjung Shim, I. Ha, Taehyun Rhee, J. D. Kim, Chang-Yeong Kim","doi":"10.1109/ICIP.2010.5653024","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5653024","url":null,"abstract":"This paper presents a novel approach to face modeling for realistic synthesis, powered by a probabilistic face diffuse model and a generic face specular map. We first construct a probabilistic face diffuse model for estimating the albedo and the normals of a face from an unknown input image. Then, we introduce a generic face specular map for estimating the specularity of the face. Using the estimated albedo, normal and specular information, we can synthesize the face under arbitrary lighting and viewing directions realistically. Unlike many existing face modeling techniques, our approach can retain both the diffuse and specular properties of the face without involving an elaborating 3D matching procedure. Thanks to the compact representation and the effective inference scheme, our technique can be applied to many practical applications, such as face normalization, avatar creation and de-identification.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115495591","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
Image clustering through community detection on hybrid image similarity graphs 基于混合图像相似图的群体检测图像聚类
2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5653478
S. Papadopoulos, Christos Zigkolis, Giorgos Tolias, Yannis Kalantidis, Phivos Mylonas, Y. Kompatsiaris, A. Vakali
{"title":"Image clustering through community detection on hybrid image similarity graphs","authors":"S. Papadopoulos, Christos Zigkolis, Giorgos Tolias, Yannis Kalantidis, Phivos Mylonas, Y. Kompatsiaris, A. Vakali","doi":"10.1109/ICIP.2010.5653478","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5653478","url":null,"abstract":"The wide adoption of photo sharing applications such as Flickr © and the massive amounts of user-generated content uploaded to them raises an information overload issue for users. An established technique to overcome such an overload is to cluster images into groups based on their similarity and then use the derived clusters to assist navigation and browsing of the collection. In this paper, we present a community detection (i.e. graph-based clustering) approach that makes use of both visual and tagging features of images in order to efficiently extract groups of related images within large image collections. Based on experiments we conducted on a dataset comprising publicly available images from Flickr ©, we demonstrate the efficiency of our method, the added value of combining visual and tag features and the utility of the derived clusters for exploring an image collection.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115597518","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: 26
Graphical symbol retrieval using a branch and bound algorithm 图形符号检索使用分支和定界算法
2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5651137
Nibal Nayef, T. Breuel
{"title":"Graphical symbol retrieval using a branch and bound algorithm","authors":"Nibal Nayef, T. Breuel","doi":"10.1109/ICIP.2010.5651137","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5651137","url":null,"abstract":"Graphical symbol spotting and retrieval in document images is an important sub-field in document analysis. This paper presents a branch and bound algorithm for spotting a queried graphical symbol in documents. The technique utilizes geometric primitives as feature points. The preprocessing of the input images involves applying some ordinary morphological operations and then sampling to get points or segments. As for the search; a branch and bound geometric matching algorithm is used to locate the required symbol(s) in the documents. The presented method has been tested on documents that contain electronic circuits and it achieved high accuracy.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115705545","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: 9
Hyperspectral imaging for skin recognition and biometrics 用于皮肤识别和生物识别的高光谱成像
2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5654042
C. P. Huynh, A. Robles-Kelly
{"title":"Hyperspectral imaging for skin recognition and biometrics","authors":"C. P. Huynh, A. Robles-Kelly","doi":"10.1109/ICIP.2010.5654042","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5654042","url":null,"abstract":"In this paper, we present a system for automatic spectral signature acquisition and recognition of skin from hyperspectral face imagery. In the acquisition step, hyperspectral cameras are used to capture multispectral or hyperspectral images of faces for skin recognition. The acquired signature may either be stored in a database for future testing or be used for purposes of identification. In the recognition step, the system accounts for variations in the illumination by recovering the light power spectrum in the scene and obtains the scene reflectance by normalising the input image radiance accordingly. Furthermore, incorporated into this system is a Non-Uniform Rational B-Spline (NURBS) compact descriptor of spectral reflectance for recognition purposes. We have employed this system as a profiling tool to classify a real-world multispectral face image database into separate ethnic groups.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123182646","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: 17
Gait recognition using Linear Discriminant Analysis with artificial walking conditions 基于线性判别分析的人工步态识别
2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5652564
Xiaxi Huang, N. Boulgouris
{"title":"Gait recognition using Linear Discriminant Analysis with artificial walking conditions","authors":"Xiaxi Huang, N. Boulgouris","doi":"10.1109/ICIP.2010.5652564","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5652564","url":null,"abstract":"In this paper, we present a novel method for the simulation of walking conditions and the generation of artificial subjects that are used for the application of Linear Discriminant Analysis. The proposed method works efficiently in situations where only one gallery sequence with one gait cycle is available for each subject in the database. The proposed method was experimentally assessed in combination with the Gait Energy Image (GEI) and the Shifted Energy Image (SEI). Very considerable improvements on the recognition and verification results are achieved.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116852656","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: 9
Non-rigid image registration by using graph-cuts with mutual information 基于互信息的非刚性图像配准
2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5653265
R. K. H. So, Albert C. S. Chung
{"title":"Non-rigid image registration by using graph-cuts with mutual information","authors":"R. K. H. So, Albert C. S. Chung","doi":"10.1109/ICIP.2010.5653265","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5653265","url":null,"abstract":"Non-rigid image registration plays an important role in medical image analysis. Recently, Tang and Chung proposed to model the non-rigid medical image registration problem as an energy minimization framework. The optimization was done by using the graph-cuts algorithm via alpha-expansions. However, the dissimilarity measure used in the energy function of this graph-cuts based method was restricted to the sum of absolute differences (SAD) and the sum of squared differences (SSD). In this paper, to utilize an advanced dissimilarity measure, such as mutual information (MI), we adopt an approximation of MI to the graph-cuts based method. Exploiting the mutual information is valuable as it can capture complex statistical relationships between the intensities of the image pair without a priori knowledge of those relationships. We have compared the proposed method against the original graph-cuts based methods, and two state-of-the-art approaches. The experimental results demonstrate that the proposed method can achieve lower registration errors.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121275014","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: 24
Multi-focus image fusion using wavelet-domain statistics 基于小波域统计的多焦点图像融合
2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5651791
Jing Tian, Li Chen
{"title":"Multi-focus image fusion using wavelet-domain statistics","authors":"Jing Tian, Li Chen","doi":"10.1109/ICIP.2010.5651791","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5651791","url":null,"abstract":"The aim of multi-focus image fusion is to combine multiple images with different focuses for enhancing the perception of a scene. The critical issue in the design of multi-focus image fusion algorithms is to evaluate the local content information of the input images. Motivated by the observation that the marginal distribution of the wavelet coefficients is different for images with different focus levels, an image fusion approach using wavelet-domain statistics is proposed in this paper. The proposed approach exploits the spreading of the wavelet coefficients distribution to measure the degree of the image's blur. Furthermore, the wavelet coefficients distribution is evaluated using a locally-adaptive Laplacian mixture model. Extensive experiments are conducted using three sets of test images under three objective metrics to demonstrate the superior performance of the proposed approach.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127124024","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: 41
Learning image similarities via Probabilistic Feature Matching 通过概率特征匹配学习图像相似性
2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5653990
Ziming Zhang, Ze-Nian Li, M. S. Drew
{"title":"Learning image similarities via Probabilistic Feature Matching","authors":"Ziming Zhang, Ze-Nian Li, M. S. Drew","doi":"10.1109/ICIP.2010.5653990","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5653990","url":null,"abstract":"In this paper, we propose a novel image similarity learning approach based on Probabilistic Feature Matching (PFM).We consider the matching process as the bipartite graph matching problem, and define the image similarity as the inner product of the feature similarities and their corresponding matching probabilities, which are learned by optimizing a quadratic formulation. Further, we prove that the image similarity and the sparsity of the learned matching probability distribution will decrease monotonically with the increase of parameter C in the quadratic formulation where C ≥ 0 is a pre-defined data-dependent constant to control the sparsity of the distribution of a feature matching probability. Essentially, our approach is the generalization of a family of similarity matching approaches. We test our approach on Graz datasets for object recognition, and achieve 89.4% on Graz-01 and 87.4% on Graz-02, respectively on average, which outperform the state-of-the-art.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124727464","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: 13
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