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6th International conference on Computer Vision and image processing, CVIP 2021

6th International Conference on Computer Vision and Image Pr...

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6th International conference on Computer Vision and image processing, CVIP 2021

ISBN: (纸本)9783031113482

The proceedings contain 97 papers. The special focus in this conference is on Computer Vision and image processing. The topics include: Automatic Classification of Sedimentary Rocks Towards Oil Reservoirs Detection;signature2Vec - An Algorithm for Reference Frame Agnostic Vectorization of Handwritten Signatures;leaf Segmentation and Counting for Phenotyping of Rosette Plants Using Xception-style U-Net and Watershed Algorithm;fast and Secure Video Encryption Using Divide-and-Conquer and Logistic Tent Infinite Collapse Chaotic Map;visual Localization Using Capsule Networks;detection of Cataract from Fundus images Using Deep Transfer Learning;brain Tumour Segmentation Using Convolution Neural Network;signature Based Authentication: A Multi-label Classification Approach to Detect the Language and Forged Sample in Signature;a Data-Set and a Real-Time Method for Detection of Pointing Gesture from Depth images;Deep-TDRS: An Integrated System for Handwritten Text Detection-Recognition and Conversion to Speech Using Deep Learning;VISION HELPER: CNN Based Real Time Navigator for the visually Impaired;structure-Texture Decomposition-Based Enhancement Framework for Weakly Illuminated images;low Cost Embedded Vision System for Location and Tracking of a Color Object;towards Label-Free Few-Shot Learning: How Far Can We Go?;AB-net: Adult- Baby Net;Polarimetric SAR Classification: Fast Learning with k-Maximum Likelihood Estimator;Leveraging Discriminative Cues for Masked Face Recognition in Post COVID World;pretreatment Identification of Oral Leukoplakia and Oral Erythroplakia Metastasis Using Deep Learning Neural Networks;soft Biometric Based Person Retrieval for Burglary Investigation;a Deep Learning Framework for the Classification of Lung Diseases Using Chest X-Ray images;computer Aided Diagnosis of Autism Spectrum Disorder Based on Thermal Imaging;scene Graph Generation with Geometric Context;deep Color Spaces for Fingerphoto Presentation Attack Detection in Mobile Devices;can

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Semantic Compensation Based Dual-Stream Feature Interaction Network for Multi-oriented Scene Text Detection

Semantic Compensation Based Dual-Stream Feature Interaction ...

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IEEE visual communications and image processing (VCIP)

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

ISBN: (纸本)9781665475938

Due to the various appearances of scene text instances and the disturbance of background, it is still a challenging task to design an effective and accurate text detector. To tackle this problem, in this paper we propose a novel dual-stream scene text detector considering semantic compensation and feature interaction. The detector extracts image features from two input images of different resolution, which improves its perceptive ability and contributes to detecting large and long texts. Specifically, we propose a Semantic Compensation Module (SCM) to aggregate features between the two streams, which compensates semantic information in features at each level via an attention mechanism. Moreover, we design a Feature Interaction Module (FIM) to obtain more expressive features. Experiments conducted on three benchmark datasets, ICDAR2015, MSRA-TD500 and ICDAR2017-MLT, demonstrate that our proposed method has competitive performance and strong robustness.

关键词： image resolution visual communication Semantics Neural networks Detectors Streaming media Benchmark testing

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Geometric-visual descriptor for improved image based localization

Geometric-visual descriptor for improved image based localiz...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Ouni, Achref Royer, Eric Chevaldonne, Marc Dhome, Michel Univ Clermont Auvergne Inst Pascal Clermont Ferrand France

ISBN: (纸本)9781728180687

This paper addresses the problem of image based localization. The goal is to find quickly and accurately the relative pose from a query taken from a stereo camera and a map obtained using visual SLAM which contains poses and 3D points associated to descriptors. In this paper we introduce a new method that leverages the stereo vision by adding geometric information to visual descriptors. This method can be used when the vertical direction of the camera is known (for example on a wheeled robot). This new geometric visual descriptor can be used with several image based localization algorithms based on visual words. We test the approach with different datasets (indoor, outdoor) and we show experimentally that the new geometricvisual descriptor improves standard image based localization approaches.

关键词： Descriptors Retrieval images Pose estimation Localization

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Semantic Attribute Guided image Aesthetics Assessment

Semantic Attribute Guided Image Aesthetics Assessment

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IEEE visual communications and image processing (VCIP)

作者： Jiachen Duan Pengfei Chen Leida Li Jinjian Wu Guangming Shi School of Artificial Intelligence Xidian University Xi'an China

ISBN: (纸本)9781665475938

image aesthetics assessment (IAA) measures the perceived beauty of images using a computational approach. People usually assess the aesthetics of an image according to semantic attributes, e.g., lighting, color, object emphasis, etc. However, the state-of-the-art IAA approaches usually follow the data-driven framework without considering the rich attributes contained in images. With this motivation, this paper presents a new semantic attribute guided IAA model, where the attention maps of semantic attributes are employed to enhance the representation ability of general aesthetic features for more effective aesthetics assessment. Specifically, we first design an attribute attention generation network to obtain the attention maps for different semantic attributes, which are utilized to weight the general aesthetic features, producing the semantic attribute-enhanced feature representations. Then, the Graph Convolutional Network (GCN) is employed to further investigate the inherent relationship among the enhanced aesthetic features, producing the final image aesthetics prediction. Extensive experiments and comparisons on three public IAA databases demonstrate the effectiveness of the proposed method.

关键词： Databases visual communication image color analysis Computational modeling Semantics Buildings Lighting

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Reducing The Mismatch Between Marginal and Learned Distributions in Neural Video Compression

Reducing The Mismatch Between Marginal and Learned Distribut...

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IEEE visual communications and image processing (VCIP)

作者： Muhammet Balcilar Bharath Bhushan Damodaran Pierre Hellier InterDigital Inc. Rennes France

ISBN: (纸本)9781665475938

During the last four years, we have witnessed the success of end-to-end trainable models for image compression. Compared to decades of incremental work, these machine learning (ML) techniques learn all the components of the compression technique, which explains their actual superiority. However, end-to-end ML models have not yet reached the performance of traditional video codecs such as VVC. Possible explanations can be put forward: lack of data to account for the temporal redundancy, or inefficiency of latent's density estimation in the neural model. The latter problem can be defined by the discrepancy between the latent's marginal distribution and the learned prior distribution. This mismatch, known as amortization gap of entropy model, enlarges the file size of compressed data. In this paper, we propose to evaluate the amortization gap for three state-of-the-art ML video compression methods. Second, we propose an efficient and generic method to solve the amortization gap and show that it leads to an improvement between 2% to 5 % without impacting reconstruction quality.

关键词： image coding visual communication Redundancy Estimation Machine learning Video compression Data models

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Comparing of Modern Methods Used to Assess the Quality of Video Sequences During Signal Streaming with and Without Human Perception

Comparing of Modern Methods Used to Assess the Quality of Vi...

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2022 Systems of Signal Synchronization, Generating and processing in Telecommunications, SYNCHROINFO 2022

作者： Nezhivleva, K.I. Davydova, A.A. Drebuzhan, A.M. Mozhaeva, A.I. Balobanov, Andrei Moscow Technical University of Communications and Informatics Moscow Russia The University of Waikato Hamilton New Zealand

ISBN: (纸本)9781665470643

Nowadays there is a trend of streaming video taking up more and more of the bandwidth of the Internet. Evaluating the quality of compression is an important area of research. Modern information coding technologies imply a trade-off between resources and visual quality. Subjective video quality assessments are time-consuming to experiment with and impossible in real-time applications. Therefore, the interest in developing models for objective video quality estimation has a high growth trend in recent years. In this paper, we present an analysis of the current categories of objective quality metrics used to assess the quality of video sequences in streaming signal transmission. We also, present comparing of the performance of reduce-referenced metrics and full-reference metrics with metric, which correlates with human perception. This work will be useful for researchers in the field of video compression, and will also be useful for creating additional test materials, planning future experiments, and improving adaptive codecs. © 2022 IEEE.

关键词： image compression

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ICMEW 2022 - IEEE International conference on Multimedia and Expo Workshops 2022, Proceedings

ICMEW 2022 - IEEE International Conference on Multimedia and...

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2022 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2022

ISBN: (纸本)9781665472180

The proceedings contain 69 papers. The topics discussed include: a deep drift-diffusion model for image aesthetic score distribution prediction;augmented-training-aware Bisenet for real-time semantic segmentation;low-power semantic segmentation on embedded systems for traffic in Asian countries;summary of the 2022 low-power deep learning semantic segmentation model compression competition for traffic scene in Asian countries;transformer based multimodal scene recognition in soccer videos;package theft detection from smart home security cameras;watermarking protocol for deep neural network ownership regulation in federated learning;a transformer-based approach for metal 3D printing quality recognition;diversity-based media search;a unified video summarization for video anomalies through deep learning;efficient topology coding and payload partitioning techniques for neural network compression (NNC) standard;and multi-augmentation for efficient self-supervised visual representation learning.

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CSC-Net: A Lightweight Model for Remote Real-Time Monitoring of Infrared Small Targets

CSC-Net: A Lightweight Model for Remote Real-Time Monitoring...

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Future communications and Networks (FCN), International conference on

作者： Mingjin Zhang Nan Zhou Xiaofeng Li Xiaogang Dong Luchang Yang Yunsong Li Xidian University Xi’an China Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798350396034

ISBN: (纸本)9798350396041

Infrared small target detection (IRSTD) is an important subject in many fields such as real-time monitoring and drone applications. Drones typically require real-time processing and transmission of infrared image data during flight. Lightweight models can reduce computational and communication burdens. While existing methods perform target detection effectively, resource-intensive demands hinder remote real-time detection. To provide high-performance target detection and analysis in resource-constrained environments, we propose a cross-sensing compression network (CSC-Net) for IRSTD in this paper. Specifically, the compression mixer module (CMM) enhances the hybrid scheme’s adaptability to different locations by exploring both semantic and spatial dimensions, thus improving image association flexibility. The filter perception module (FPM) dynamically adjusts input contributions for enhanced cross-sensing, emphasizing the benefits of multilayered semantics. Experiments on SIRST and IRSTD-1K show that the proposed CSC-Net can achieve accurate target location and segmentation. In particular, its visual effects and resource consumption can meet the remote real-time monitoring scenario.

关键词： Adaptation models image segmentation image coding Computational modeling Semantics Object detection visual effects

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Texture-aware Network for Smoke Density Estimation

Texture-aware Network for Smoke Density Estimation

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IEEE visual communications and image processing (VCIP)

作者： Xue Xia Kun Zhan Yajing Peng Yuming Fang Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9781665475938

Smoke density estimation, also termed as soft segmentation, was developed from pixel-wise smoke (hard) segmen-tation and it aims at providing transparency and segmentation confidence for each pixel. The key difference between them lies in that segmentation focuses on classifying pixels into smoke and non-smoke ones, while density estimation obtains inner transparency of smoke component rather than treat all smoke pixels as an equal value. Based on this, we propose a texture-aware network being able to capture inner transparency of smoke components rather than merely focus on general smoke distribution for pixel-wise smoke density estimation. Besides, we adapt the Squeeze-and-Excitation (SE) layer for smoke feature extraction by involving max values for robustness. In order to represent inhomogeneous smoke pixels, we proposed a simple yet efficient attention-based texture-aware module that involves both gradient and semantic information. Experimental results show that our method outperforms others in both single image density estimation or segmentation and video smoke detection.

关键词： image segmentation visual communication Semantics Estimation Feature extraction Nonhomogeneous media Robustness

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End-to-end image Compression with Swin-Transformer

End-to-end Image Compression with Swin-Transformer

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IEEE visual communications and image processing (VCIP)

作者： Meng Wang Kai Zhang Li Zhang Yue Li Junru Li Yue Wang Shiqi Wang Department of Computer Science City University of Hong Kong Hong Kong China Bytedance Inc. San Diego CA USA Beijing Bytedance Technology Co Ltd Beijing CN

ISBN: (纸本)9781665475938

In this paper, we propose an end-to-end image compression framework, which cooperates with the swin-transformer modules to capture the localized and non-localized similarities in image compression. In particular, the swin-transformer modules are deployed in the analysis and synthesis stages, interleaving with convolution layers. The transformer layers are expected to perceive more flexible receptive fields, such that the spatially localized and non-localized redundancies could be more effectively eliminated. The proposed method reveals the excellent capability of signal conjunction and prediction, leading to the improvement of the rate and distortion performance. Experimental results show that the proposed method is superior to the existing methods on both natural scene and screen content images, where 22.46% BD-Rate savings are achieved when compared with the BPG. Over 30% BD-Rate gains could be observed with screen content images when compared with the classical hyper-prior end-to-end coding method.

关键词： image coding Convolution visual communication Termination of employment Rate distortion theory Distortion Transformers

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