Pub Date : 2014-10-30DOI: 10.1109/ICIP.2014.7025500
Jacob Chakareski, V. Velisavljevic, V. Stanković
We study multicast of multi-view content in the video plus depth format to heterogeneous clients. We design a joint source-channel coding scheme based on view and rate embedded source coding and rateless channel coding. It comprises an optimization framework for joint view selection and source-channel rate allocation, and includes a fast method for separate optimization of the source and channel coding components, at a negligible performance loss wrt the joint solution. We demonstrate performance gains over a state-of-the-art method based on H.264/SVC, in the case of two client classes.
{"title":"Joint source and channel coding of view and rate scalable multi-view video","authors":"Jacob Chakareski, V. Velisavljevic, V. Stanković","doi":"10.1109/ICIP.2014.7025500","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025500","url":null,"abstract":"We study multicast of multi-view content in the video plus depth format to heterogeneous clients. We design a joint source-channel coding scheme based on view and rate embedded source coding and rateless channel coding. It comprises an optimization framework for joint view selection and source-channel rate allocation, and includes a fast method for separate optimization of the source and channel coding components, at a negligible performance loss wrt the joint solution. We demonstrate performance gains over a state-of-the-art method based on H.264/SVC, in the case of two client classes.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81788532","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 : 2014-10-29DOI: 10.1109/ICIP.2014.7025583
S. Yoon, Hosik Sohn, Yong Ju Jung, Yong Man Ro
This paper proposes a new inter-view consistent hole filling method in view extrapolation for multi-view image generation. In stereopsis, inter-view consistency regarding structure, color, and luminance is one of the crucial factors that affect the overall viewing quality of three-dimensional image contents. In particular, the inter-view inconsistency could induce visual stress on the human visual system. To ensure the inter-view consistency, the proposed method suggests a hole filling method in an order from the nearest to farthest view to the reference view by propagating the filled color information in the preceding view. In addition, a novel depth map filling method is incorporated to achieve the inter-view consistency. Experimental results show that the proposed method significantly improves the inter-view consistency for multiview images and depth maps, compared to those of previous methods.
{"title":"Inter-view consistent hole filling in view extrapolation for multi-view image generation","authors":"S. Yoon, Hosik Sohn, Yong Ju Jung, Yong Man Ro","doi":"10.1109/ICIP.2014.7025583","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025583","url":null,"abstract":"This paper proposes a new inter-view consistent hole filling method in view extrapolation for multi-view image generation. In stereopsis, inter-view consistency regarding structure, color, and luminance is one of the crucial factors that affect the overall viewing quality of three-dimensional image contents. In particular, the inter-view inconsistency could induce visual stress on the human visual system. To ensure the inter-view consistency, the proposed method suggests a hole filling method in an order from the nearest to farthest view to the reference view by propagating the filled color information in the preceding view. In addition, a novel depth map filling method is incorporated to achieve the inter-view consistency. Experimental results show that the proposed method significantly improves the inter-view consistency for multiview images and depth maps, compared to those of previous methods.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85354684","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 : 2014-10-28DOI: 10.1109/ICIP.2014.7025006
Min-Jung Kim, Tae-Hyun Oh, In-So Kweon
Since commercial light field cameras became available, the light field camera has aroused much interest from computer vision and image processing communities due to its versatile functions. Most of its special features are based on an estimated depth map, so reliable depth estimation is a crucial step. However, estimating depth on real light field cameras is a challenging problem due to noise and short baselines among sub-aperture images. We propose a depth map estimation method for light field cameras by exploiting correspondence and focus cues. We aggregate costs among all the sub-aperture images on cost volume to alleviate noise effects. With efficiency of the cost volume, cost-aware depth estimation is quickly achieved by discrete-continuous optimization. In addition, we analyze each property of correspondence and focus cues and utilize them to select reliable anchor points. A well reconstructed initial depth map from the anchors is shown to enhance convergence. We show our method outperforms the state-of-the-art methods by validating it on real datasets acquired with a Lytro camera.
{"title":"Cost-aware depth map estimation for Lytro camera","authors":"Min-Jung Kim, Tae-Hyun Oh, In-So Kweon","doi":"10.1109/ICIP.2014.7025006","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025006","url":null,"abstract":"Since commercial light field cameras became available, the light field camera has aroused much interest from computer vision and image processing communities due to its versatile functions. Most of its special features are based on an estimated depth map, so reliable depth estimation is a crucial step. However, estimating depth on real light field cameras is a challenging problem due to noise and short baselines among sub-aperture images. We propose a depth map estimation method for light field cameras by exploiting correspondence and focus cues. We aggregate costs among all the sub-aperture images on cost volume to alleviate noise effects. With efficiency of the cost volume, cost-aware depth estimation is quickly achieved by discrete-continuous optimization. In addition, we analyze each property of correspondence and focus cues and utilize them to select reliable anchor points. A well reconstructed initial depth map from the anchors is shown to enhance convergence. We show our method outperforms the state-of-the-art methods by validating it on real datasets acquired with a Lytro camera.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88374631","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.2014.7025967
V. Itier, W. Puech, A. Bors
3-D object security is increasingly brought to the attention of the public by the expansion of new multimedia technologies such as the 3-D printing. In the development of crypto-security systems of 3-D objects, we can identify two major directions represented by the cryptography and digital watermarking. A good security system has to be format compliant, has to preserve the original bit rate and, whenever possible, it should be reversible. Watermarking methodology has the advantage of ensuring that the embedded hidden message can be verified at any processing stage such as the transmission, storage and when visualizing the embedding media. In this paper, we review the previous work in 3-D security and analyze the crypto-security of a 3-D watermarking method which embeds information by mesh surface distortion minimization. Then, we discuss future avenues of research by presenting emerging applications.
{"title":"Cryptanalysis aspects in 3-D watermarking","authors":"V. Itier, W. Puech, A. Bors","doi":"10.1109/ICIP.2014.7025967","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025967","url":null,"abstract":"3-D object security is increasingly brought to the attention of the public by the expansion of new multimedia technologies such as the 3-D printing. In the development of crypto-security systems of 3-D objects, we can identify two major directions represented by the cryptography and digital watermarking. A good security system has to be format compliant, has to preserve the original bit rate and, whenever possible, it should be reversible. Watermarking methodology has the advantage of ensuring that the embedded hidden message can be verified at any processing stage such as the transmission, storage and when visualizing the embedding media. In this paper, we review the previous work in 3-D security and analyze the crypto-security of a 3-D watermarking method which embeds information by mesh surface distortion minimization. Then, we discuss future avenues of research by presenting emerging applications.","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":"81835418","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.2014.7025142
A. Ledoux, N. Richard, A. Capelle-Laizé, H. Deborah, C. Fernandez-Maloigne
Facing the increasing number of multi and hyperspectral image acquisitions, in particular for medical and industrial applications, we need accurate features to analyse and assess the content complexity in a metrological way. In this paper, we explore an original way to compute texture features for spectral images in a full-band and vector process. To do it, we developed a dedicated approach for Mathematical Morphology using distance function. Thanks to this, we extend the classical mathematical morphology to spectral images. We show in this paper the scientific construction and preliminary results.
{"title":"Toward a full-band texture features for spectral images","authors":"A. Ledoux, N. Richard, A. Capelle-Laizé, H. Deborah, C. Fernandez-Maloigne","doi":"10.1109/ICIP.2014.7025142","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025142","url":null,"abstract":"Facing the increasing number of multi and hyperspectral image acquisitions, in particular for medical and industrial applications, we need accurate features to analyse and assess the content complexity in a metrological way. In this paper, we explore an original way to compute texture features for spectral images in a full-band and vector process. To do it, we developed a dedicated approach for Mathematical Morphology using distance function. Thanks to this, we extend the classical mathematical morphology to spectral images. We show in this paper the scientific construction and preliminary results.","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":"85852263","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.2014.7025862
Thibaut Durand, Nicolas Thome, M. Cord, David Picard
Visual learning with weak supervision is a promising research area, since it offers the possibility to build large image datasets at reasonable cost. In this paper, we address the problem of weakly supervised object detection, where the goal is to predict the label of the image using object position as latent variable. We propose a new method that builds upon the Latent Structural SVM (LSSVM) formalism. Specifically, we introduce an original coarse-to-fine approach that limits the evolution of the latent parameter subspace. This incremental strategy drives the learning towards better solutions, providing a model with increased predictive accuracy. In addition, this leads to a significant speed up during learning and inference compared to standard sliding window methods. Experiments carried out on Mammal dataset validate the good performances and fast training of the method compared to state-of-the-art works.
{"title":"Incremental learning of latent structural SVM for weakly supervised image classification","authors":"Thibaut Durand, Nicolas Thome, M. Cord, David Picard","doi":"10.1109/ICIP.2014.7025862","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025862","url":null,"abstract":"Visual learning with weak supervision is a promising research area, since it offers the possibility to build large image datasets at reasonable cost. In this paper, we address the problem of weakly supervised object detection, where the goal is to predict the label of the image using object position as latent variable. We propose a new method that builds upon the Latent Structural SVM (LSSVM) formalism. Specifically, we introduce an original coarse-to-fine approach that limits the evolution of the latent parameter subspace. This incremental strategy drives the learning towards better solutions, providing a model with increased predictive accuracy. In addition, this leads to a significant speed up during learning and inference compared to standard sliding window methods. Experiments carried out on Mammal dataset validate the good performances and fast training of the method compared to state-of-the-art works.","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":"88827589","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.2014.7025850
Adolfo López, Carina E. I. Westling, R. Emonet, M. Easteal, L. Lavia, H. Witchel, J. Odobez
In this paper, we investigate the influence of music on human walking behaviors in a public setting monitored by surveillance cameras. To this end, we propose a novel algorithm to characterize the frequency and phase of the walk. It relies on a human-by-detection tracking framework, along with a robust fitting of the human head bobbing motion. Preliminary experiments conducted on more than 100 tracks show that an accuracy greater than 85% for foot strike estimation can be achieved, suggesting that large scale analysis is at reach for finer music/walking behavior relationship studies.
{"title":"Automated bobbing and phase analysis to measure walking entrainment to music","authors":"Adolfo López, Carina E. I. Westling, R. Emonet, M. Easteal, L. Lavia, H. Witchel, J. Odobez","doi":"10.1109/ICIP.2014.7025850","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025850","url":null,"abstract":"In this paper, we investigate the influence of music on human walking behaviors in a public setting monitored by surveillance cameras. To this end, we propose a novel algorithm to characterize the frequency and phase of the walk. It relies on a human-by-detection tracking framework, along with a robust fitting of the human head bobbing motion. Preliminary experiments conducted on more than 100 tracks show that an accuracy greater than 85% for foot strike estimation can be achieved, suggesting that large scale analysis is at reach for finer music/walking behavior relationship studies.","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":"89300618","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.2014.7025455
Emilie Niaf, Rémi Flamary, A. Rakotomamonjy, O. Rouvière, C. Lartizien
We propose a new computer-aided detection scheme for prostate cancer screening on multiparametric magnetic resonance (mp-MR) images. Based on an annotated training database of mp-MR images from thirty patients, we train a novel support vector machine (SVM)-inspired classifier which simultaneously learns an optimal linear discriminant and a subset of predictor variables (or features) that are most relevant to the classification task, while promoting spatial smoothness of the malignancy prediction maps. The approach uses a ℓ1-norm in the regularization term of the optimization problem that rewards sparsity. Spatial smoothness is promoted via an additional cost term that encodes the spatial neighborhood of the voxels, to avoid noisy prediction maps. Experimental comparisons of the proposed ℓ1-Smooth SVM scheme to the regular ℓ2-SVM scheme demonstrate a clear visual and numerical gain on our clinical dataset.
{"title":"SVM with feature selection and smooth prediction in images: Application to CAD of prostate cancer","authors":"Emilie Niaf, Rémi Flamary, A. Rakotomamonjy, O. Rouvière, C. Lartizien","doi":"10.1109/ICIP.2014.7025455","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025455","url":null,"abstract":"We propose a new computer-aided detection scheme for prostate cancer screening on multiparametric magnetic resonance (mp-MR) images. Based on an annotated training database of mp-MR images from thirty patients, we train a novel support vector machine (SVM)-inspired classifier which simultaneously learns an optimal linear discriminant and a subset of predictor variables (or features) that are most relevant to the classification task, while promoting spatial smoothness of the malignancy prediction maps. The approach uses a ℓ1-norm in the regularization term of the optimization problem that rewards sparsity. Spatial smoothness is promoted via an additional cost term that encodes the spatial neighborhood of the voxels, to avoid noisy prediction maps. Experimental comparisons of the proposed ℓ1-Smooth SVM scheme to the regular ℓ2-SVM scheme demonstrate a clear visual and numerical gain on our clinical dataset.","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":"73653809","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.2014.7025158
R. Amhaz, S. Chambon, J. Idier, V. Baltazart
This paper proposes a new algorithm for crack detection based on the selection of minimal paths. It takes account of both photometric and geometric characteristics and requires few information a priori. It is validated on synthetic and real images.
{"title":"A new minimal path selection algorithm for automatic crack detection on pavement images","authors":"R. Amhaz, S. Chambon, J. Idier, V. Baltazart","doi":"10.1109/ICIP.2014.7025158","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025158","url":null,"abstract":"This paper proposes a new algorithm for crack detection based on the selection of minimal paths. It takes account of both photometric and geometric characteristics and requires few information a priori. It is validated on synthetic and real images.","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":"80369655","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.2014.7026164
Zhongwei Tang, P. Monasse, J. Morel
We evaluate and improve the matching precision of the SIFT method [1], defined as the root mean square error (RMSE) under a ground truth geometric transform. We first argue that the matching precision reflects to some extent the average relative localization precision between two images. For scale invariant feature detectors like SIFT, we show that the matching precision decreases with the scale of the keypoints, and that this is caused by the scale space sub-sampling in SIFT. We verify that canceling this sub-sampling therefore improves drastically the matching precision. Yet, in case of scale change, this improvement is marginal due to the coarse scale quantization in the scale space. A more sophisticated method is therefore also proposed to improve the matching precision even in case of scale change. This incremented precision is a key ingredient in many important image processing tasks requiring the best precision, such as registration, stitching, and camera calibration.
{"title":"Improving the matching precision of SIFT","authors":"Zhongwei Tang, P. Monasse, J. Morel","doi":"10.1109/ICIP.2014.7026164","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7026164","url":null,"abstract":"We evaluate and improve the matching precision of the SIFT method [1], defined as the root mean square error (RMSE) under a ground truth geometric transform. We first argue that the matching precision reflects to some extent the average relative localization precision between two images. For scale invariant feature detectors like SIFT, we show that the matching precision decreases with the scale of the keypoints, and that this is caused by the scale space sub-sampling in SIFT. We verify that canceling this sub-sampling therefore improves drastically the matching precision. Yet, in case of scale change, this improvement is marginal due to the coarse scale quantization in the scale space. A more sophisticated method is therefore also proposed to improve the matching precision even in case of scale change. This incremented precision is a key ingredient in many important image processing tasks requiring the best precision, such as registration, stitching, and camera calibration.","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":"78652554","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}
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