With the fast growth of immersive video sequences, achieving seamless and high-quality compressed 3D content is even more critical. MPEG recently developed a video-based point cloud compression (V-PCC) standard for dynamic point cloud coding. However, reconstructed point clouds using V-PCC suffer from different artifacts, including losing data during pre-processing before applying existing video coding techniques, e.g., High-Efficiency Video Coding (HEVC). Patch generations and self-occluded points in the 3D to the 2D projection are the main reasons for missing data using V-PCC. This paper proposes a new method that introduces overlapping slicing as an alternative to patch generation to decrease the number of patches generated and the amount of data lost. In the proposed method, the entire point cloud has been cross-sectioned into variable-sized slices based on the number of self-occluded points so that data loss can be minimized in the patch generation process and projection. For this, a variable number of layers are considered, partially overlapped to retain the self-occluded points. The proposed method's added advantage is to reduce the bits requirement and to encode geometric data using the slicing base position. The experimental results show that the proposed method is much more flexible than the standard V-PCC method, improves the rate-distortion performance, and decreases the data loss significantly compared to the standard V-PCC method.
Nanjing University（南京大学）；Hangzhou Normal University（杭州师范大学）
This work extends the Multiscale Sparse Representation (MSR) framework developed for static Point Cloud Geometry Compression (PCGC) to support the dynamic PCGC through the use of multiscale inter conditional coding. To this end, the reconstruction of the preceding Point Cloud Geometry (PCG) frame is progressively downscaled to generate multiscale temporal priors which are then scale-wise transferred and integrated with lower-scale spatial priors from the same frame to form the contextual information to improve occupancy probability approximation when processing the current PCG frame from one s…
Heidelberg University（海德堡大学）；University Medical Center Mannheim（曼海姆大学医学中心）
At present, lesion segmentation is still performed manually (or semi-automatically) by medical experts. To facilitate this process, we contribute a fully-automatic lesion segmentation pipeline. This work proposes a method as a part of the LiTS (Liver Tumor Segmentation Challenge) competition for ISBI 17 and MICCAI 17 comparing methods for automatics egmentation of liver lesions in CT scans. By utilizing cascaded, densely connected 2D U-Nets and a Tversky-coefficient based loss function, our framework achieves very good shape extractions with high detection sensitivity, with competitive scores …
YOLO-PVC: 2D-to-3D Consolidation of Slice-wise Detections for Volumetric Liver Tumor Localization in MRI
ESME Research Lab（ESME研究实验室）；Université Paris-Est Créteil（巴黎东部克雷泰伊大学）；LRE EPITA（EPITA LRE实验室）；Institute of Space Technology（空间技术研究所）；Henri Mondor University Hospital（亨利·蒙多大学医院）
Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases. Automatic segmentation of brain tumors is a challenging task for computer-aided diagnosis due to low-tissue contrast in the tumor subregions. To overcome this, we devise a novel pixel-wise segmentation framework through a convolutional 3D to 2D MR patch conversion model to predict class labels of the central pixel in the input sliding patches. Precisely, we first extract 3D patches from each modality to calibrate slices through the squeeze and excitation (SE) block. Then, the outp…
Image segmentation has long been a basic problem in computer vision. Depth-wise Layering is a kind of segmentation that slices an image in a depth-wise sequence unlike the conventional image segmentation problems dealing with surface-wise decomposition. The proposed Depth-wise Layering technique uses a single depth image of a static scene to slice it into multiple layers. The technique employs a thresholding approach to segment rows of the dense depth map into smaller partitions called Line-Segments in this paper. Then, it uses the line-segment labelling method to identify number of objects an…
ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression
Electrical and Computer Engineering Department, The University of British Columbia（英属哥伦比亚大学电气与计算机工程系）；School of Engineering Science, Simon Fraser University（西蒙弗雷泽大学工程科学学院）；School of Electronic Science and Technology, Eastern Institute of Technology（电子科学与技术学院，东部技术学院）；Google LLC（谷歌公司）
Institute for Artificial Intelligence（人工智能研究所）；University of Georgia（佐治亚大学）；Shanghai Jiao Tong University（上海交通大学）
Segmenting an entire 3D image often has high computational complexity and requires large memory consumption; by contrast, performing volumetric segmentation in a slice-by-slice manner is efficient but does not fully leverage the 3D data. To address this challenge, we propose a multi-dimensional attention network (MDA-Net) to efficiently integrate slice-wise, spatial, and channel-wise attention into a U-Net based network, which results in high segmentation accuracy with a low computational cost. We evaluate our model on the MICCAI iSeg and IBSR datasets, and the experimental results demonstrate…
Tel Aviv University（特拉维夫大学）；Sheba Medical Center（示巴医疗中心）；Bar-Ilan University（巴伊兰大学）
In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classification into different hepatic lesions and normal liver tissues. Our dataset contains CT examinations from 140 patients with 333 CT images annotated by an expert radiologist. We tested our approach and compared it to the conventional training process. Results indicate superiority o…
Fully convolutional neural networks have made promising progress in joint liver and liver tumor segmentation. Instead of following the debates over 2D versus 3D networks (for example, pursuing the balance between large-scale 2D pretraining and 3D context), in this paper, we novelly identify the wide variation in the ratio between intra- and inter-slice resolutions as a crucial obstacle to the performance. To tackle the mismatch between the intra- and inter-slice information, we propose a slice-aware 2.5D network that emphasizes extracting discriminative features utilizing not only in-plane sem…
College of Health Science and Environmental Engineering, Shenzhen Technology University（深圳科技大学健康科学与环境工程学院）；University Medical Center Göttingen（哥廷根大学医学中心）；Guangdong-Hongkong-Macau CNS Regeneration Institute, Key Laboratory of CNS Regeneration (Jinan University)-Ministry of Education, Jinan University（广东-港澳-澳门神经科学再生研究所，神经科学再生重点实验室（暨南大学）-教育部，暨南大学）；Department of Radiology, The First Hospital of Tsinghua University（清华大学第一医院放射科）；Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University（清华大学生物医学成像研究中心，生物医学工程系，医学院）；Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University（教育部生物医学工程重点实验室，生物医学工程与仪器科学学院，浙江大学）；Department of Endocrinology and Metabolism, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine（浙江大学医学院邵逸夫医院内分泌科）；Lab of Molecular Imaging and Medical Intelligence, Department of Radiology, Longgang Central Hospital of Shenzhen (Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine（分子成像与医学智能实验室，放射科，深圳龙岗中心医院（深圳临床医学院，广州中医药大学；龙岗临床学院，汕头大学医学院））；Longgang Clinical Institute of Shantou University Medical College)
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Korea University（高丽大学）
In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images. IQA is first performed slice-wise using a nonlocal residual neural network (NR-Net) and then volume-wise by agglomerating the slice QA results using random forest. Our method requires only a small amount of quality-annotated images for training and is designed to be robust to annotation noise that might occur due to rater errors and the inevitable mix of good and bad slices in an image volume. Using a small set of quality-assessed images, we pre-train NR-Net to annotate each image …
GroupedMixer: An Entropy Model with Group-wise Token-Mixers for Learned Image Compression
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；School of Electronics Engineering and Computer Science, Peking University（北京大学电子工程与计算机科学学院）
Istanbul Medipol University（伊斯坦布尔梅迪波尔大学）；Louisiana State University（路易斯安那州立大学）
Through pixel-wise optical coding of images during exposure time, it is possible to extract sub-exposure images from a single capture. Such a capability can be used for different purposes, including high-speed imaging, high-dynamic-range imaging and compressed sensing. In this paper, we demonstrate a sub-exposure image extraction method, where the exposure coding pattern is inspired from frequency division multiplexing idea of communication systems. The coding masks modulate sub-exposure images in such a way that they are placed in non-overlapping regions in Fourier domain. The sub-exposure im…
This paper presents a multi-view distributed video coding framework for independent camera encoding and centralized decoding. Spatio-temporal-view concealment methods are developed that exploit the interleaved nature of the employed hybrid KEY/Wyner-Ziv frames for block-wise generation of the side information (SI). We study a number of view concealment methods and develop a joint approach that exploits all available correlation for forming the side information. We apply a diversity technique for fusing multiple such predictions thereby achieving more reliable results. We additionally introduce…
Orange（Orange公司）；Univ. Rennes, INSA Rennes CNRS, IETR – UMR 6164（雷恩大学、雷恩国立应用科学学院、法国国家科学研究中心、电子与电信研究所）
This paper introduces a new method for inter-frame coding based on two complementary autoencoders: MOFNet and CodecNet. MOFNet aims at computing and conveying the Optical Flow and a pixel-wise coding Mode selection. The optical flow is used to perform a prediction of the frame to code. The coding mode selection enables competition between direct copy of the prediction or transmission through CodecNet. The proposed coding scheme is assessed under the Challenge on Learned Image Compression 2020 (CLIC20) P-frame coding conditions, where it is shown to perform on par with the state-of-the-art vide…
Southwestern University of Finance and Economics（西南财经大学）；University of Electronic Science and Technology of China（电子科技大学）；The University of Hong Kong（香港大学）
In this paper, we propose a novel tensor learning and coding model for third-order data completion. Our model is to learn a data-adaptive dictionary from the given observations, and determine the coding coefficients of third-order tensor tubes. In the completion process, we minimize the low-rankness of each tensor slice containing the coding coefficients. By comparison with the traditional pre-defined transform basis, the advantages of the proposed model are that (i) the dictionary can be learned based on the given data observations so that the basis can be more adaptively and accurately const…
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；Institute of Engineering & Management（工程与管理学院）；Indian Statistical Institute（印度统计研究所）
Retrieval of text information from natural scene images and video frames is a challenging task due to its inherent problems like complex character shapes, low resolution, background noise, etc. Available OCR systems often fail to retrieve such information in scene/video frames. Keyword spotting, an alternative way to retrieve information, performs efficient text searching in such scenarios. However, current word spotting techniques in scene/video images are script-specific and they are mainly developed for Latin script. This paper presents a novel word spotting framework using dynamic shape co…
Shenzhen University（深圳大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；City University of Hong Kong（香港城市大学）
In this paper, we propose a distortion-aware loop filtering model to improve the performance of intra coding for 360$^o$ videos projected via equirectangular projection (ERP) format. To enable the awareness of distortion, our proposed module analyzes content characteristics based on a coding unit (CU) partition mask and processes them through partial convolution to activate the specified area. The feature recalibration module, which leverages cascaded residual channel-wise attention blocks (RCABs) to adjust the inter-channel and intra-channel features automatically, is capable of adapting with…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Biomedical Engineering, University of Science and Technology of China（中国科学技术大学生物医学工程学院）
针对 SAM 难以建模三维医学切片上下文且全体积提示成本高的问题，本文提出 Slide-SAM，利用三切片滑动窗口和相邻掩码传播提示，实现极少提示下的高效三维分割。
Nanjing University of Posts and Telecommunications（南京邮电大学）；Nanjing University of Science and Technology（南京理工大学）；Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education（高维信息智能感知与系统教育部重点实验室）
In this paper, we propose an easily trained yet powerful representation learning approach with performance highly competitive to deep neural networks in a digital pathology image segmentation task. The method, called sparse coding driven deep decision tree ensembles that we abbreviate as ScD2TE, provides a new perspective on representation learning. We explore the possibility of stacking several layers based on non-differentiable pairwise modules and generate a densely concatenated architecture holding the characteristics of feature map reuse and end-to-end dense learning. Under this architect…
Reliable detection of anomalies is crucial when deploying machine learning models in practice, but remains challenging due to the lack of labeled data. To tackle this challenge, contrastive learning approaches are becoming increasingly popular, given the impressive results they have achieved in self-supervised representation learning settings. However, while most existing contrastive anomaly detection and segmentation approaches have been applied to images, none of them can use the contrastive losses directly for both anomaly detection and segmentation. In this paper, we close this gap by maki…
RotCAtt-TransUNet++: Novel Deep Neural Network for Sophisticated Cardiac Segmentation
Le Hong Phong High School for the Gifted（黎鸿峰天才高中）；Faculty of Information Technology, University of Science, VNU-HCM（胡志明市国家大学科学学院信息技术系）；Viet Nam National University, Ho Chi Minh City（越南国家大学胡志明市分校）
Towards Voxel Spacing Consistency for Medical Image Segmentation
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Department of Ultrasound, Shanghai Chest Hospital（上海市胸科医院超声科）；CAMPAR, Technical University of Munich（慕尼黑工业大学CAMPAR）
A Workflow to Efficiently Generate Dense Tissue Ground Truth Masks for Digital Breast Tomosynthesis
Department of Radiology, University of Pittsburgh（匹兹堡大学放射科）；Center of Informatics, Federal University of Paraíba（巴西北部联邦大学信息中心）；Department of Radiology, University of Pennsylvania（宾夕法尼亚大学放射科）；Department of Industrial Engineering, University of Pittsburgh（匹兹堡大学工业工程系）
Volumetric segmentation of lesions on CT scans is important for many types of analysis, including lesion growth kinetic modeling in clinical trials and machine learning of radiomic features. Manual segmentation is laborious, and impractical for large-scale use. For routine clinical use, and in clinical trials that apply the Response Evaluation Criteria In Solid Tumors (RECIST), clinicians typically outline the boundaries of a lesion on a single slice to extract diameter measurements. In this work, we have collected a large-scale database, named LesionVis, with pixel-wise manual 2D lesion delin…
