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Temporal Scalable Coding For Dynamic Meshes

Temporal Scalable Coding For Dynamic Meshes

引用

IEEE International conference on image processing

作者： Jianfeng Xu Haruhisa Kato Kei Kawamura Advanced Visual Communication Laboratory KDDI Research Inc 2-1-15 Ohara Fujimino-shi Saitama Japan

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

This paper presents the first implementation of temporal scalability in the ongoing standard for Video-based Dynamic Mesh Coding (V-DMC), a crucial enhancement that enables bitstream adaptation to diverse network conditions and device capabilities. While displacement and texture, two of the V-DMC’s sub-bitstreams, already benefit from existing video codec temporal scalability, the non-video basemesh sub-bitstream lacks this feature. To address this gap, we propose an adaptive coding structure designed for the basemesh. Moreover, we propose a novel cost function to adaptively select the frame type between intra-frame and inter-frame in this coding structure. Our experimental results demonstrate significant improvements in coding efficiency compared to the original V-DMC, i.e., the total BD-rates of D1, D2, Luma, Cb, and Cr averaged across all eight test sequences are -15.1%, -15.0%, 0.3%,-9.9%, and -8.4 %, respectively.

关键词： image coding Scalability Estimation Cost function Video codecs Standards Adaptive coding

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MCTS-Based Robotic Exploration for Scene Graph Generation 6th

MCTS-Based Robotic Exploration for Scene Graph Generation

引用

6th International conference on Cognitive Systems and Signal processing, ICCSIP 2021

作者： Zhou, Fangbo Liu, Huaping Li, Xinghang Zhao, Huailin School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789811692468

Many researchers have used scene graphs to improve performance in some tasks in recent years, such as complete image matching, image generation, and visual questions answers. However, the current scene graph generation mainly constructs a single picture or a scene graph of a part of the scene. To construct a scene graph of the entire room, the agent needs to explore the entire room autonomously. We propose a scene graph generation method based on Monte Carlo Tree Search (MCTS). In the agent’s exploration of the room, the agent uses MCTS to select a reliable location among the locations that the agent can reach in the next step. After multiple searches, the agent can find most or all objects in the room. Experiments show that our method helps the agent finds more objects in a fixed step size than the agent’s random exploration of the room. The agent can create a complete scene graph because it sees most of the objects in the room. © 2022, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Content-Aware image Smoothing Based on Fuzzy Clustering 19th

Content-Aware Image Smoothing Based on Fuzzy Clustering

引用

19th International conference on Information processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022

作者： Antunes-Santos, Felipe Lopez-Molina, Carlos Mir-Fuentes, Arnau Mendioroz, Maite De Baets, Bernard Department of Estadistica Informatica y Matematicas Universidad Pública de Navarra Pamplona31006 Spain KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Ghent9000 Belgium NavarraBiomed Hospital Universitario de Navarra Pamplona31008 Spain

ISBN: (纸本)9783031089732

Literature contains a large variety of content-aware smoothing methods. As opposed to classical smoothing methods, content-aware ones intend to regularize the image while avoiding the loss of relevant visual information. In this work, we propose a novel approach to content-aware image smoothing based on fuzzy clustering, specifically the Spatial Fuzzy c-Means (SFCM) algorithm. We develop the proposal and put it to the test in the context of automatic analysis of immunohistochemistry imagery for neural tissue analysis. © 2022, Springer Nature Switzerland AG.

关键词： Fuzzy clustering

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Quality Assessment of Screen Content images Based on Multi-Pathway Convolutional Neural Network

Quality Assessment of Screen Content Images Based on Multi-P...

引用

IEEE visual communications and image processing (VCIP)

作者： Mengyao Li Sumei Li School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781665475938

In this paper, considering the retinal structure of human eye, and the composition characteristics of screen content images (SCIs), a multi-pathway convolutional neural network (CNN) with picture-text competition is proposed for SCIs quality assessment. According to the visual mechanism of human retina, we design a retinal structure simulation module, which uses multiple parallel convolution pathways to simulate the parallel transmission of visual signals by bipolar cells and uses a multi-pathway feature fusion (MPFF) module to allocate the weight for each channel to simulate horizontal cells' regulation of the information transmission. In addition, we design an adaptive feature extraction and competition module (AFEC) to directly extract the features of textural and pictorial regions and distribute the weight. Furthermore, the attention module combined with deformable convolution and channel attention can accurately extract image edge features and reduce redundancy of information. Experimental results show that the proposed method is superior to the mainstream methods.

关键词： visualization Convolution visual communication image edge detection Feature extraction Retina Regulation

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PointNetGeM: Simple and Efficient Point Cloud Based Network for Place Recognition

PointNetGeM: Simple and Efficient Point Cloud Based Network ...

引用

IEEE visual communications and image processing (VCIP)

作者： Keli Wen Ruonan Zhang Ge Li School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen China

ISBN: (纸本)9781665475938

Point Cloud based place recognition is a popular area of current research. Most existing methods add additional structures to PointNet to further extract compact information from point clouds, e.g. PointNetVLAD, PCAN. These methods achieved good results but incur severe computational overhead, increase structural complexity and are not easily scalable. In this paper, we propose a simple and efficient method named PointNetGeM. We optimize the training process and achieve discriminative global feature descriptors. Experiments on several datasets demonstrate the effectiveness of our network structure.

关键词： Point cloud compression Training image recognition visual communication Feature extraction Complexity theory Data mining

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Tire pattern classification based on ConvNeXt Network

Tire pattern classification based on ConvNeXt Network

引用

IEEE visual communications and image processing (VCIP)

作者： Yuting Zhu Mingkai Tao Lingxi Guo Xiaoqing Wang School of Electronic and Communication Engineering Sun Yat-sen University Shenzheng China School of Systems Science and Engineering Sun Yat-sen University Guangzhou China Science and Technology on Space Physics Laboratory Beijing China

ISBN: (纸本)9781665475938

Tire pattern classification is an important means to provide clues for traffic accident processing. With the rapid increase in the number of vehicles, it is in urgent need to develop efficient and automatic tire pattern image classification and recognition system. In this paper, we design a novel lightweight network model for tire pattern image classification dataset. The proposed method consists of two parts. On the one hand, the powerful ConvNeXt is used as the main baseline. On the other hand, the attention mechanism is introduced without sacrificing FLOPs, which improves the classification accuracy. Experimental results on the Tire pattern dataset demonstrate that, the proposed model performed satisfactorily when tested against low quality tire patterns taken from actual scenarios. In addition, experimental analysis of ablation demonstrates the good robustness of the proposed method.

关键词： image recognition visual communication Pipelines Pattern classification Channel estimation Tires Robustness

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In-Loop Filtering via Trained Look-Up Tables

In-Loop Filtering via Trained Look-Up Tables

引用

IEEE visual communications and image processing (VCIP)

作者： Zhuoyuan Li Jiacheng Li Yao Li Li Li Dong Liu Feng Wu University of Science and Technology of China (USTC) Hefei China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In-loop filtering (ILF) is a key technology in image/video coding for reducing the artifacts. Recently, neural network-based in-loop filtering methods achieve remarkable coding gains beyond the capability of advanced video coding standards, establishing themselves a promising candidate tool for future standards. However, the utilization of deep neural networks (DNN) brings high computational complexity and raises high demand of dedicated hardware, which is challenging to apply into general use. To address this limitation, we study an efficient in-loop filtering scheme by adopting look-up tables (LUTs). After training a DNN with a predefined reference range for in-loop filtering, we cache the output values of the DNN into a LUT via traversing all possible inputs. In the coding process, the filtered pixel is generated by locating the input pixels (to-be-filtered pixel and reference pixels) and interpolating between the cached values. To further enable larger reference range within the limited LUT storage, we introduce an enhanced indexing mechanism in the filtering process, and a clipping/finetuning mechanism in the training. The proposed method is implemented into the Versatile Video Coding (VVC) reference software, VTM-11.0. Experimental results show that the proposed method, with three different configurations, achieves on average 0.13%∼0.51%, and 0.10% ∼0.39% BD-rate reduction under the all-intra (AI) and random-access (RA) configurations respectively. The proposed method incurs only 1% ∼8% time increase, an additional computation of 0.13 ∼0.93 kMAC/pixel, and 164 ∼1148 KB storage cost for a single model. Our method has explored a new and more practical approach for neural network-based ILF.

关键词： Video coding Training Performance evaluation Interpolation image coding Filtering Artificial neural networks Hardware Table lookup Standards

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3D Tensor Display for Non-Lambertian Content

3D Tensor Display for Non-Lambertian Content

引用

IEEE visual communications and image processing (VCIP)

作者： Armand Losfeld Eline Soetens Daniele Bonatto Sarah Fachada Laurie Van Bogaert Gauthier Lafruit Mehrdad Teratani Laboratory of Image Synthesis and Analysis Université Libre de Bruxelles Brussels Belgium

ISBN: (纸本)9781665475938

A tensor display is a type of 3D light field display, composed of multiple transparent screens and a back-light that can render a scene with correct depth, allowing to view a 3D scene without wearing glasses. The analysis of state-of-the-art tensor displays assumes that the content is Lambertian. In order to extend its capabilities, we analyze the limitations of displaying non-Lambertian scenes and propose a new method to factorize the non-Lambertian scenes using disparity analysis. Moreover, we demonstrate a new prototype of a tensor display with three layers of full HD content at 60 fps. Compared with state-of-the-art, the evaluation results verify that the proposed non-Lambertian rendering method can display a higher quality for non-Lambertian scenes on both simulation and a prototyped tensor display.

关键词： Solid modeling Three-dimensional displays Tensors visual communication image processing Prototypes Glass

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Tire Pattern image Classification using Variational Auto-Encoder with Contrastive Learning

Tire Pattern Image Classification using Variational Auto-Enc...

引用

IEEE visual communications and image processing (VCIP)

作者： Jianning Yang Jiahao Xue Xiaodong Feng Chaoqi Song Yu Hao School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9781665475938

Tire pattern image classification is an important computer vision problem in pubic security, which can guide policeman to detect criminal cases. It remains challenge due to the small diversity within different classes. Generally, a tire pattern image classification system may require two characteristics: high accuracy and low computation. In this paper, we first assume that capturing rich feature representation will benefits tire classification and learning through a lightweight network will improve computing efficiency. We then propose a simple yet efficient two-stage training mechanism: 1) We learn a feature extractor using a V ariational Auto-Encoder framework constrained by contrastive learning, projecting images to latent space owing rich feature representation. 2) We train a single-layer linear classification network depend on the features extracted by the previous trained encoder. The Top-1 and Top-5 accuracy on tire pattern dataset is 89.8% and 96.6% respectively, validating the effectiveness of our strategy.

关键词： Training Computer vision visual communication Feature extraction Tires Security image classification

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Cheque Information processing and Validation using Deep Learning

Cheque Information Processing and Validation using Deep Lear...

引用

communications and Information Technologies (GCCIT), Global conference on

作者： Suganthi K Darshan N Shenoy School of Electronics Engineering Vellore Institute of Technology Chennai

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Understanding document images, such as invoices, is a difficult task. It necessitates reading the text as well as comprehending the document’s general structure. Conventional techniques first extract text using Optical Character Recognition (OCR), yet this method has drawbacks. In addition to being computationally costly, OCR can have trouble converting between various languages and document formats and introduce errors that can compound throughout the analysis *** is an OCR-free VDU (visual Document Understanding) model that challenges this paradigm. It does not even bother with the text extraction stage; it analyzes the document image straight. This creative strategy produces notable advancements. Donut performs at the cutting edge, which means it is quick and precise. This is a significant development in the realm of visual document understanding since it enables Donut to handle a greater variety of languages and document types. The model has been tested using BLEU scores and ROUGE scores over 3 parameters namely – payee name, amount in words and amount in figure. The model has also been tested over variety of sample sizes ranging from 50 to 1000.

关键词： Training Productivity visualization Target recognition Optical character recognition Transforms Streaming media Multilingual Data mining Testing

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