Pub Date : 2014-10-27DOI: 10.1109/ICIP.2014.7025100
Y. Gaudeau, Julien Lambert, N. Labonne, J. Moureaux
It is admitted that lossy compression can be used in the field of medical images under the control of experts. Lossy compression can offer substantial reduction of the volumes of medical images, being thus an efficient solution for both storage and transmission problem in the medical context. Furthermore, the use of touchpads in medicine has grown and many medical applications on this kind of support is now available. The storage capacity of this kind of terminal is limited, lossy compression represents a good alternative to allow greedy medical applications on such terminals. In this work, we address the problem of quality assessment of MRI scans from an interactive upper limb radiology atlas (Monster Anatomy Upper Limb). The quality assessment protocol is adapted from the International Telecommunication Union recommendations (ITU-R BT-500-11). In this paper, we propose to determine compression thresholds which are acceptable according to the quality required for the proper use of this radiology atlas. We show that this application (using a simple JPEG encoder) has a lossy compression threshold ranging from 13: 1 for the majority of the atlas images up to 27: 1 for the hand images. Finally, several objective image quality assessment algorithms (IQA) are also linked to subjective ratings of the panel of health professionals.
{"title":"Compressed image quality assessment: Application to an interactive upper limb radiology atlas","authors":"Y. Gaudeau, Julien Lambert, N. Labonne, J. Moureaux","doi":"10.1109/ICIP.2014.7025100","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025100","url":null,"abstract":"It is admitted that lossy compression can be used in the field of medical images under the control of experts. Lossy compression can offer substantial reduction of the volumes of medical images, being thus an efficient solution for both storage and transmission problem in the medical context. Furthermore, the use of touchpads in medicine has grown and many medical applications on this kind of support is now available. The storage capacity of this kind of terminal is limited, lossy compression represents a good alternative to allow greedy medical applications on such terminals. In this work, we address the problem of quality assessment of MRI scans from an interactive upper limb radiology atlas (Monster Anatomy Upper Limb). The quality assessment protocol is adapted from the International Telecommunication Union recommendations (ITU-R BT-500-11). In this paper, we propose to determine compression thresholds which are acceptable according to the quality required for the proper use of this radiology atlas. We show that this application (using a simple JPEG encoder) has a lossy compression threshold ranging from 13: 1 for the majority of the atlas images up to 27: 1 for the hand images. Finally, several objective image quality assessment algorithms (IQA) are also linked to subjective ratings of the panel of health professionals.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75884255","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}
Pub Date : 2014-10-27DOI: 10.1109/ICIP.2014.7025765
A. Hasnat, O. Alata, A. Trémeau
Model Based Clustering (MBC) is a method that estimates a model for the data and produces probabilistic clustering. In this paper, we propose a novel MBC method to cluster three dimensional directional features. We assume that the features are generated from a finite statistical mixture model based on the von Mises-Fisher (vMF) distribution. The core elements of our proposed method are: (a) generate a set of vMF Mixture Models (vMFMM) and (b) select the optimal model using a parsimony based approach with information criteria. We empirically validate our proposed method by applying it on simulated data. Next, we apply it to cluster image normals in order to perform depth image analysis.
{"title":"Model based clustering for 3D directional features: Application to depth image analysis","authors":"A. Hasnat, O. Alata, A. Trémeau","doi":"10.1109/ICIP.2014.7025765","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025765","url":null,"abstract":"Model Based Clustering (MBC) is a method that estimates a model for the data and produces probabilistic clustering. In this paper, we propose a novel MBC method to cluster three dimensional directional features. We assume that the features are generated from a finite statistical mixture model based on the von Mises-Fisher (vMF) distribution. The core elements of our proposed method are: (a) generate a set of vMF Mixture Models (vMFMM) and (b) select the optimal model using a parsimony based approach with information criteria. We empirically validate our proposed method by applying it on simulated data. Next, we apply it to cluster image normals in order to perform depth image analysis.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73843927","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}
Pub Date : 2014-10-27DOI: 10.1109/ICIP.2014.7025284
Jean Kossaifi, Georgios Tzimiropoulos, M. Pantic
Active Appearance Models (AAMs) are statistical models of shape and appearance widely used in computer vision to detect landmarks on objects like faces. Fitting an AAM to a new image can be formulated as a non-linear least-squares problem which is typically solved using iterative methods. Owing to its efficiency, Gauss-Newton optimization has been the standard choice over more sophisticated approaches like Newton. In this paper, we show that the AAM problem has structure which can be used to solve efficiently the original Newton problem without any approximations. We then make connections to the original Gauss-Newton algorithm and study experimentally the effect of the additional terms introduced by the Newton formulation on both fitting accuracy and convergence. Based on our derivations, we also propose a combined Newton and Gauss-Newton method which achieves promising fitting and convergence performance. Our findings are validated on two challenging in-the-wild data sets.
{"title":"Fast Newton active appearance models","authors":"Jean Kossaifi, Georgios Tzimiropoulos, M. Pantic","doi":"10.1109/ICIP.2014.7025284","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025284","url":null,"abstract":"Active Appearance Models (AAMs) are statistical models of shape and appearance widely used in computer vision to detect landmarks on objects like faces. Fitting an AAM to a new image can be formulated as a non-linear least-squares problem which is typically solved using iterative methods. Owing to its efficiency, Gauss-Newton optimization has been the standard choice over more sophisticated approaches like Newton. In this paper, we show that the AAM problem has structure which can be used to solve efficiently the original Newton problem without any approximations. We then make connections to the original Gauss-Newton algorithm and study experimentally the effect of the additional terms introduced by the Newton formulation on both fitting accuracy and convergence. Based on our derivations, we also propose a combined Newton and Gauss-Newton method which achieves promising fitting and convergence performance. Our findings are validated on two challenging in-the-wild data sets.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77433120","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}
Pub Date : 2014-10-27DOI: 10.1109/ICIP.2014.7025032
Ying Lu, Liming Chen, A. Saidi, Zhaoxiang Zhang, Yunhong Wang
To solve the challenging task of learning effective visual categories with limited training samples, we propose a new sparse representation classifier based transfer learning method, namely SparseTL, which propagates the cross-category knowledge from multiple source categories to the target category. Specifically, we enhance the target classification task in learning a both generative and discriminative sparse representation based classifier using pairs of source categories most positively and most negatively correlated to the target category. We further improve the discriminative ability of the classifier by choosing the most discriminative bins in the feature vector with a feature selection process. The experimental results show that the proposed method achieves competitive performance on the NUS-WIDE Scene database compared to several state of the art transfer learning algorithms while keeping a very efficient runtime.
{"title":"Learning visual categories through a sparse representation classifier based cross-category knowledge transfer","authors":"Ying Lu, Liming Chen, A. Saidi, Zhaoxiang Zhang, Yunhong Wang","doi":"10.1109/ICIP.2014.7025032","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025032","url":null,"abstract":"To solve the challenging task of learning effective visual categories with limited training samples, we propose a new sparse representation classifier based transfer learning method, namely SparseTL, which propagates the cross-category knowledge from multiple source categories to the target category. Specifically, we enhance the target classification task in learning a both generative and discriminative sparse representation based classifier using pairs of source categories most positively and most negatively correlated to the target category. We further improve the discriminative ability of the classifier by choosing the most discriminative bins in the feature vector with a feature selection process. The experimental results show that the proposed method achieves competitive performance on the NUS-WIDE Scene database compared to several state of the art transfer learning algorithms while keeping a very efficient runtime.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84524927","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}
Pub Date : 2014-10-27DOI: 10.1109/ICIP.2014.7025444
Romain Negrel, David Picard, P. Gosselin
In web-scale image retrieval, the most effective strategy is to aggregate local descriptors into a high dimensionality signature and then reduce it to a small dimensionality. Thanks to this strategy, web-scale image databases can be represented with small index and explored using fast visual similarities. However, the computation of this index has a very high complexity, because of the high dimensionality of signature projectors. In this work, we propose a new efficient method to greatly reduce the signature dimensionality with low computational and storage costs. Our method is based on the linear projection of the signature onto a small subspace using a sparse projection matrix. We report several experimental results on two standard datasets (Inria Holidays and Oxford) and with 100k image distractors. We show that our method reduces both the projectors storage cost and the computational cost of projection step while incurring a very slight loss in mAP (mean Average Precision) performance of these computed signatures.
{"title":"Dimensionality reduction of visual features using sparse projectors for content-based image retrieval","authors":"Romain Negrel, David Picard, P. Gosselin","doi":"10.1109/ICIP.2014.7025444","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025444","url":null,"abstract":"In web-scale image retrieval, the most effective strategy is to aggregate local descriptors into a high dimensionality signature and then reduce it to a small dimensionality. Thanks to this strategy, web-scale image databases can be represented with small index and explored using fast visual similarities. However, the computation of this index has a very high complexity, because of the high dimensionality of signature projectors. In this work, we propose a new efficient method to greatly reduce the signature dimensionality with low computational and storage costs. Our method is based on the linear projection of the signature onto a small subspace using a sparse projection matrix. We report several experimental results on two standard datasets (Inria Holidays and Oxford) and with 100k image distractors. We show that our method reduces both the projectors storage cost and the computational cost of projection step while incurring a very slight loss in mAP (mean Average Precision) performance of these computed signatures.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89388248","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}
Pub Date : 2014-10-27DOI: 10.1109/ICIP.2014.7025570
A. Jezierska, J. Pesquet, Hugues Talbot, C. Chaux
This paper deals with noise parameter estimation from a single image under Poisson-Gaussian noise statistics. The problem is formulated within a mixed discrete-continuous optimization framework. The proposed approach jointly estimates the signal of interest and the noise parameters. This is achieved by introducing an adjustable regularization term inside an optimized criterion, together with a data fidelity error measure. The optimal solution is sought iteratively by alternating the minimization of a label field and of a noise parameter vector. Noise parameters are updated at each iteration using an Expectation-Maximization approach. The proposed algorithm is inspired from a spatial regularization approach for vector quantization. We illustrate the usefulness of our approach on macroconfocal images. The identified noise parameters are applied to a denoising algorithm, so yielding a complete denoising scheme.
{"title":"Iterative poisson-Gaussian noise parametric estimation for blind image denoising","authors":"A. Jezierska, J. Pesquet, Hugues Talbot, C. Chaux","doi":"10.1109/ICIP.2014.7025570","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025570","url":null,"abstract":"This paper deals with noise parameter estimation from a single image under Poisson-Gaussian noise statistics. The problem is formulated within a mixed discrete-continuous optimization framework. The proposed approach jointly estimates the signal of interest and the noise parameters. This is achieved by introducing an adjustable regularization term inside an optimized criterion, together with a data fidelity error measure. The optimal solution is sought iteratively by alternating the minimization of a label field and of a noise parameter vector. Noise parameters are updated at each iteration using an Expectation-Maximization approach. The proposed algorithm is inspired from a spatial regularization approach for vector quantization. We illustrate the usefulness of our approach on macroconfocal images. The identified noise parameters are applied to a denoising algorithm, so yielding a complete denoising scheme.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74515138","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}
Pub Date : 2014-10-27DOI: 10.1109/ICIP.2014.7025105
A. Traoré, P. Carré, C. Olivier
This paper proposes a new reduced-reference metric based on the modeling of Quaternionic Wavelet Transform (QWT) coefficients from Information Criteria (IC). To obtain the reduced-references, we will model the QWT coefficients using probability density functions (pdf) whose parameters are used as reduced-references. IC are proposed in order to build the optimal histograms of the QWT coefficients to get most likely pdf of these. In the mixture model, IC are also used to obtain the number of distribution. From these models, we propose a measure of degradation by comparing probability density functions of the reference image and the distributions of the degraded image of the QWT subbands. We shall demonstrate that one phase of the QWT provides relevant information in the Image Quality Assessment. Tests confirmed the potentiality of this information and showed that the QWT produces a better coefficient of correlation with the Human Visual System than the Discrete Wavelet Transform.
{"title":"Reduced-reference metric based on the quaternionic wavelet coefficients modeling by information criteria","authors":"A. Traoré, P. Carré, C. Olivier","doi":"10.1109/ICIP.2014.7025105","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025105","url":null,"abstract":"This paper proposes a new reduced-reference metric based on the modeling of Quaternionic Wavelet Transform (QWT) coefficients from Information Criteria (IC). To obtain the reduced-references, we will model the QWT coefficients using probability density functions (pdf) whose parameters are used as reduced-references. IC are proposed in order to build the optimal histograms of the QWT coefficients to get most likely pdf of these. In the mixture model, IC are also used to obtain the number of distribution. From these models, we propose a measure of degradation by comparing probability density functions of the reference image and the distributions of the degraded image of the QWT subbands. We shall demonstrate that one phase of the QWT provides relevant information in the Image Quality Assessment. Tests confirmed the potentiality of this information and showed that the QWT produces a better coefficient of correlation with the Human Visual System than the Discrete Wavelet Transform.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74007023","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}
Pub Date : 2014-10-27DOI: 10.1109/ICIP.2014.7025868
J. Portilla
Directly applying circular de-convolution to real-world blurred images usually results in boundary artifacts. Classic boundary extension techniques fail to provide likely results, in terms of a circular boundary-condition observation model. Boundary reflection gives raise to non-smooth features, especially when oblique oriented features encounter the image boundaries. Tapering the boundaries of the image support, or similar strategies (like constrained diffusion), provides smoothness on the toroidal support; however this does not guarantee consistency with the spectral properties of the blur (in particular, to its zeros). Here we propose a simple, yet effective, model-derived method for extending real-world blurred images, so that they become likely in terms of a Gaussian circular boundary-condition observation model. We achieve artifact-free results, even under highly unfavorable conditions, when other methods fail.
{"title":"Maximum likelihood extension for non-circulant deconvolution","authors":"J. Portilla","doi":"10.1109/ICIP.2014.7025868","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025868","url":null,"abstract":"Directly applying circular de-convolution to real-world blurred images usually results in boundary artifacts. Classic boundary extension techniques fail to provide likely results, in terms of a circular boundary-condition observation model. Boundary reflection gives raise to non-smooth features, especially when oblique oriented features encounter the image boundaries. Tapering the boundaries of the image support, or similar strategies (like constrained diffusion), provides smoothness on the toroidal support; however this does not guarantee consistency with the spectral properties of the blur (in particular, to its zeros). Here we propose a simple, yet effective, model-derived method for extending real-world blurred images, so that they become likely in terms of a Gaussian circular boundary-condition observation model. We achieve artifact-free results, even under highly unfavorable conditions, when other methods fail.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91470144","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}
Pub Date : 2014-10-27DOI: 10.1109/ICIP.2014.7026144
Sid Ahmed Fezza, M. Larabi, K. Faraoun
Asymmetric stereoscopic coding is a very promising technique to decrease the bandwidth required for stereoscopic 3D delivery. However, one large obstacle is linked to the limit of asymmetric coding or the just noticeable threshold of asymmetry, so that 3D viewing experience is not altered. By way of subjective experiments, recent works have attempted to identify this asymmetry threshold. However, fixed threshold, highly dependent on the experiment design, do not allow to adapt to quality and content variation of the image. In this paper, we propose a new non-uniform asymmetric stereoscopic coding adjusting in a dynamic manner the level of asymmetry for each image region to ensure unaltered binocular perception. This is achieved by exploiting several HVS-inspired models; specifically we used the Binocular Just Noticeable Difference (BJND) combined with visual saliency map and depth information to quantify precisely the asymmetry threshold. Simulation results show that the proposed method results in up to 44% of bitrate saving and provides better 3D visual quality compared to state-of-the-art asymmetric coding methods.
{"title":"Asymmetric coding of stereoscopic 3D based on perceptual significance","authors":"Sid Ahmed Fezza, M. Larabi, K. Faraoun","doi":"10.1109/ICIP.2014.7026144","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7026144","url":null,"abstract":"Asymmetric stereoscopic coding is a very promising technique to decrease the bandwidth required for stereoscopic 3D delivery. However, one large obstacle is linked to the limit of asymmetric coding or the just noticeable threshold of asymmetry, so that 3D viewing experience is not altered. By way of subjective experiments, recent works have attempted to identify this asymmetry threshold. However, fixed threshold, highly dependent on the experiment design, do not allow to adapt to quality and content variation of the image. In this paper, we propose a new non-uniform asymmetric stereoscopic coding adjusting in a dynamic manner the level of asymmetry for each image region to ensure unaltered binocular perception. This is achieved by exploiting several HVS-inspired models; specifically we used the Binocular Just Noticeable Difference (BJND) combined with visual saliency map and depth information to quantify precisely the asymmetry threshold. Simulation results show that the proposed method results in up to 44% of bitrate saving and provides better 3D visual quality compared to state-of-the-art asymmetric coding methods.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81371171","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}
Pub Date : 2014-10-27DOI: 10.1109/ICIP.2014.7026227
N. Pustelnik, P. Abry, H. Wendt, N. Dobigeon
The identification of texture changes is a challenging problem that can be addressed by considering local regularity fluctuations in an image. This work develops a procedure for local regularity estimation that combines a convex optimization strategy with wavelet leaders, specific wavelet coefficients recently introduced in the context of multifractal analysis. The proposed procedure is formulated as an inverse problem that combines the joint estimation of both local regularity exponent and of the optimal weights underlying regularity measurement. Numerical experiments using synthetic texture indicate that the performance of the proposed approach compares favorably against other wavelet based local regularity estimation formulations. The method is also illustrated with an example involving real-world texture.
{"title":"Inverse problem formulation for regularity estimation in images","authors":"N. Pustelnik, P. Abry, H. Wendt, N. Dobigeon","doi":"10.1109/ICIP.2014.7026227","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7026227","url":null,"abstract":"The identification of texture changes is a challenging problem that can be addressed by considering local regularity fluctuations in an image. This work develops a procedure for local regularity estimation that combines a convex optimization strategy with wavelet leaders, specific wavelet coefficients recently introduced in the context of multifractal analysis. The proposed procedure is formulated as an inverse problem that combines the joint estimation of both local regularity exponent and of the optimal weights underlying regularity measurement. Numerical experiments using synthetic texture indicate that the performance of the proposed approach compares favorably against other wavelet based local regularity estimation formulations. The method is also illustrated with an example involving real-world texture.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79569852","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：