2018 7th European Workshop on Visual Information Processing (EUVIP)最新文献

Deep Metric Learning for Color Differences 颜色差异的深度度量学习

2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-11-01 DOI: 10.1109/EUVIP.2018.8611776

Fedor Zolotarev, A. Kaarna

引用次数: 1

Geometry-Guided 3D Data Interpolation for Projection-Based Dynamic Point Cloud Coding 基于投影的动态点云编码的几何导向三维数据插值

2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-11-01 DOI: 10.1109/EUVIP.2018.8611760

Vida Fakour Sevom, S. Schwarz, M. Gabbouj

{"title":"Geometry-Guided 3D Data Interpolation for Projection-Based Dynamic Point Cloud Coding","authors":"Vida Fakour Sevom, S. Schwarz, M. Gabbouj","doi":"10.1109/EUVIP.2018.8611760","DOIUrl":"https://doi.org/10.1109/EUVIP.2018.8611760","url":null,"abstract":"With the recent improvements in acquisition techniques for 3D media applications, it has become easier to collect 3D data, for example, dynamic point cloud data. Such point clouds consist of a large amount of 3D coordinates, which describe a scene or object in 3D space by its geometry and texture attributes. Moreover, they are an effective representation of 3D environments for applications such as Augmented Reality or Virtual Reality. One of the main problems for such data is that the number of points is typically too large to allow for real-time transmission or efficient storage. Thus, compressing such 3D data is a key issue to reduce the amount of required bandwidth or memory. This paper presents a method for efficient compression of dynamic point cloud data within the current MPEG standardization framework for dynamic point cloud compression. The key benefit of the presented work is the reduced number of encoded and decoded 3D points compared to the reference framework, thus encoding and decoding complexity is reduced significantly. Objective results show a speed-up of around 35–40% in coding times. Furthermore, reconstruction quality is preserved, thus reducing bit rate requirements by up to 30%. Visual results verify the improved reconstruction quality, and compared to the reference at the same computational complexity, coding efficiency is improved by over 40%.","PeriodicalId":252212,"journal":{"name":"2018 7th European Workshop on Visual Information Processing (EUVIP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128412321","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

The Challenges of Applying Deep Learning for Hemangioma Lesion Segmentation 应用深度学习进行血管瘤病灶分割的挑战

2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-11-01 DOI: 10.1109/EUVIP.2018.8611730

Pedro Alves, Jaime S. Cardoso, M. Bom-Sucesso

{"title":"The Challenges of Applying Deep Learning for Hemangioma Lesion Segmentation","authors":"Pedro Alves, Jaime S. Cardoso, M. Bom-Sucesso","doi":"10.1109/EUVIP.2018.8611730","DOIUrl":"https://doi.org/10.1109/EUVIP.2018.8611730","url":null,"abstract":"Infantile Hemangiomas (IH) make up the most common type of benign vascular tumors affecting children. They can grow for several months until beginning to involute. In present-day clinical practice there's no objective monitoring protocol. For more objective measures, an automatic evaluation system (CAD system) is needed to aid clinicians in assessing the effectiveness of a given patient's response to a treatment. One of the stages of these systems is the lesion segmentation. This work addresses the automatic segmentation of lesions in IH. Acknowledging that the methods in the literature for IH lesion segmentation lag behind the state-of-the-art in the image segmentation community, we conduct a comparison of various methodologies for the segmentation of the IH, including both shallow and deep methodologies. Acknowledging the lack of data in the field for a robust learning of deep models, we also evaluate transfer learning techniques to benefit from knowledge extracted in other skin lesions. The best results were obtained with the shortest path method and a multiscale convolutional neural network that merges two pipelines working at different scales. Although promising, the results put in evidence the need for better databases, collected under suitable acquisition protocols.","PeriodicalId":252212,"journal":{"name":"2018 7th European Workshop on Visual Information Processing (EUVIP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125782725","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

An Improvement of BM3D Image Denoising and Deblurring Algorithm by Generalized Total Variation 基于广义全变分的BM3D图像去噪去模糊算法改进

2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-11-01 DOI: 10.1109/EUVIP.2018.8611693

A. Nasonov, A. Krylov

引用次数: 10

To See or Not To See: Determining the Recognition Threshold of Encrypted Images 看还是不看:确定加密图像的识别阈值

2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-11-01 DOI: 10.1109/EUVIP.2018.8611779

H. Hofbauer, F. Autrusseau, A. Uhl

引用次数: 1

Learning Overcomplete and Sparsifying Transform With Approximate and Exact Closed Form Solutions 用近似和精确闭形式解学习过完全和稀疏化变换

2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-11-01 DOI: 10.1109/EUVIP.2018.8611650

Dimche Kostadinov, S. Voloshynovskiy, Sohrab Ferdowsi

{"title":"Learning Overcomplete and Sparsifying Transform With Approximate and Exact Closed Form Solutions","authors":"Dimche Kostadinov, S. Voloshynovskiy, Sohrab Ferdowsi","doi":"10.1109/EUVIP.2018.8611650","DOIUrl":"https://doi.org/10.1109/EUVIP.2018.8611650","url":null,"abstract":"This paper addresses the learning problem for data-adaptive transform that provides sparse representation in a space with dimensions larger than (or equal to)the dimensions of the original space. We present an iterative, alternating algorithm that has two steps: (i)transform update and (ii)sparse coding. In the transform update step, we focus on novel problem formulation based on a lower bound of the objective that addresses a trade-off between (a) how much are aligned the gradients of the approximative objective and the original objective, and (b)how much the lower bound is close to the original objective. This allows us not only to propose approximate closed form solution but also gives the possibility to find an update that can lead to accelerated local convergence and enables us to estimate an update that can lead to a satisfactory solution under a small amount of data. Since in the transform update, the approximate closed form solution preserves the gradient and in the sparse coding step, we use exact closed form solution, the resulting algorithm is convergent. On the practical side, we evaluate on image denoising application and demonstrate promising denoising performance together with advantages in training data requirements, accelerated local convergence and the resulting computational complexity.","PeriodicalId":252212,"journal":{"name":"2018 7th European Workshop on Visual Information Processing (EUVIP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116629303","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: 6

WaSP: Hierarchical Warping, Merging, and Sparse Prediction for Light Field Image Compression WaSP:光场图像压缩的分层翘曲、合并和稀疏预测

2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-11-01 DOI: 10.1109/EUVIP.2018.8611756

P. Astola, I. Tabus

{"title":"WaSP: Hierarchical Warping, Merging, and Sparse Prediction for Light Field Image Compression","authors":"P. Astola, I. Tabus","doi":"10.1109/EUVIP.2018.8611756","DOIUrl":"https://doi.org/10.1109/EUVIP.2018.8611756","url":null,"abstract":"We propose a versatile light field compression scheme that is organized on hierarchical levels, where all views belonging to a particular level are encoded using several views already encoded in the previous hierarchical levels. The new scheme builds on an earlier version of our codec, and provides a more generalized functionality with improved view merging. The operations needed when one view is encoded conditional on its reference views are: first warping its reference views to the location of the current view and partitioning the pixels according to their state of occlusion in various warped versions; then merging the warped references using one optimal LS merger for each class of occluded pixels; finally, adjustment of the overall merged image to the original view by using a sparse predictor. The new scheme is applied to both plenoptic camera images and high density camera array data, and is evaluated in accordance with the JPEG Pleno test conditions. We compare the performance of the proposed codec to that of the HEVC anchors defined in the JPEG Pleno test conditions. We also make comparisons to the performance achieved by our earlier scheme. The proposed codec is publicly available on GitHub and it was accepted as the Verification Model (VM) 1.0 software for JPEG Pleno Light Field coding standard.","PeriodicalId":252212,"journal":{"name":"2018 7th European Workshop on Visual Information Processing (EUVIP)","volume":"34 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116495234","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: 43

Pseudo Spectral Method Based on Symmetric Extension 基于对称扩展的伪谱方法

2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-11-01 DOI: 10.1109/EUVIP.2018.8611666

Izumi Ito

引用次数: 2

Joint Statistical Models for No-Reference Stereoscopic Image Quality Assessment 无参考立体图像质量评价的联合统计模型

2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-11-01 DOI: 10.1109/EUVIP.2018.8611676

Zohaib Amjad Khan, M. Kaaniche, Azeddine Beghdadi, F. A. Cheikh

引用次数: 3

Real-time light-field 3D telepresence 实时光场3D远程呈现

2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-11-01 DOI: 10.1109/EUVIP.2018.8611663

A. Cserkaszky, A. Barsi, Zsolt Nagy, Gabor Puhr, T. Balogh, P. A. Kara

引用次数: 18