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Blind Gaussian Deep Denoiser Network using Multi-Scale Pixel Attention

Blind Gaussian Deep Denoiser Network using Multi-Scale Pixel...

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IEEE visual communications and image processing (VCIP)

作者： Ramesh Kumar Thakur Suman Kumar Maji Dept. of Computer Science & Engineering Indian Institute of Technology Patna Patna India

ISBN: (纸本)9781665475938

Many deep learning networks focus on the task of Gaussian denoising by processing images on a fixed scale or multiple scales using convolution and deconvolution. In certain cases, excessive scaling applied in the network results in the loss of image details. Sometimes, the usage of deeper convolutional networks results in the loss of network gradient. In this paper, to overcome both the problems, we propose a multi-scale pixel attention-based blind Gaussian denoiser network that utilizes a combination of important features at five different scales. The proposed network performs blind Gaussian denoising in the sense that it does not need any prior information about noise. It comprises a central multi-scale pixel attention block together with dilated convolutional layers and skip connections that help in utilizing the full receptive field of the first convolutional layer to the last convolutional layer and is based on residual architecture for propagating high-level information easily in the network. We have provided the code of the proposed technique at https://***/RTSIR/MSPABDN.

关键词： Convolutional codes Deep learning Deconvolution visual communication Convolution AWGN image processing

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Central Perturbation-based Interval Type-2 Fuzzy C-Means Clustering for image Segmentation 4

Central Perturbation-based Interval Type-2 Fuzzy C-Means Clu...

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4th International conference on Natural Language processing, ICNLP 2022

作者： Rong, Lan Na, Qu Tianlong, Hu Feng, Zhao Haiyan, Yu Lu, Zhang University of Posts and Telecommunications School of Communications and Information Engineering Xi'an Xi'an710121 China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

In fuzzy c-means (FCM) clustering algorithm, when assigning membership degree to a data point, the calculation of fuzzy membership degree contains various uncertain factors, such as fuzzifier, distance metric and cluster center, etc. Although the interval type-2 FCM (IT2FCM) clustering algorithm and its improved algorithms consider the uncertainty about the fuzzifier, it suffers from falling into the local optimum because of ignoring the uncertainty of the cluster center. To address the issue, a central perturbation-based interval type-2 fuzzy c-means (CPIT2FCM) clustering algorithm for image segmentation is proposed by representing the uncertainty of the cluster center in this paper. In the proposed algorithm, the traditional crisp center is extended to the interval fuzzy center utilizing central perturbation, and the upper and lower membership functions are constructed by the interval fuzzy center, thus the type-l fuzzy set is extended to the interval type-2 fuzzy set. At the same time, the Mahalanobis distance is introduced into the CPIT2FCM algorithm to improve the clustering performance for the datasets and the color image. Finally, experiments are conducted on synthetic datasets and real images respectively, and the effectiveness of the proposed algorithm was verified by combining visual effects and evaluation indices. © 2022 IEEE.

关键词： image segmentation

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8th International conference on Computer Vision and image processing, CVIP 2023

8th International Conference on Computer Vision and Image Pr...

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8th International conference on Computer Vision and image processing, CVIP 2023

ISBN: (纸本)9783031585340

The proceedings contain 140 papers. The special focus in this conference is on Computer Vision and image processing. The topics include: A Novel Framework for Cognitive Load Estimation from Electroencephalogram Signals Utilizing Sparse Representation of Brain Connectivity;MCCNet: A Multi-scale Cross Connection Network for Low-Light image Enhancement;feature Fusion and Multi-head Attention Based Hindi Captioner;Payload Length and Location Identification Using a Novel CNN and Re-embedding Strategy in Stego images Created by Content Adaptive Steganographic Algorithms;ConvMTL: Multi-task Learning via Self-supervised Learning for Simultaneous Dense Predictions;driver Drowsiness Detection Using Vision Transformer;automated Cricket Commentary Generation for Videos;image Captioning with visual Positional Embedding and Bi-linear Pooling;Cartilage Segmentation from MRI images Towards Prediction of Osteoarthritis;Reinforcement Algorithm-Guided ROI Extraction of Fingerprint Biometric Data;improved Metric Space for Shape Correspondence;an Integrated Approach: Combining GrabCut and Contour-Matching for Hand Gesture Segmentation in Indian Sign Language;identity Preserved Expressive Talking Faces with Synchrony;isolated Sign Language Recognition Using Deep Learning;a Gradient-Based Approach to Interpreting visual Encoding Models;adjust Your Focus: Defocus Deblurring from Dual-Pixel images Using Explicit Multi-Scale Cross-Correlation;Efficient Seizure Prediction from images of EEG Signals Using Convolutional Neural Network;coping with Increased Levels of Label Noise in Facial Expression Recognition;experimental Evaluation of Needle Tip Prediction Using Kalman Filtering Approach;a Deep Face Antispoofing System with Hardware Implementation for Real-Time Applications;Bit Plane Segmentation and LBP-Based Coverless Video Steganography for Secure Data Transmission;a Deep Learning Approach to Enhance Semantic Segmentation of Bacteria and Pus Cells from Microscopic Urine Smear images Using

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No Reference Stereoscopic Video Quality Assessment based on Human Vision System

No Reference Stereoscopic Video Quality Assessment based on ...

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IEEE visual communications and image processing (VCIP)

作者： Xiaofang Zhang Sumei Li International Engineering Institute Tianjin University TianJin China School of Electrical and Information Engineering Tianjin University TianJin China

ISBN: (纸本)9781665475938

In this paper, we propose a no-reference stereoscopic video quality assessment (NR-SVQA) based on human vision system (HVS). Firstly, we build a frequency transform module (FTM), which maps spatial domain to frequency domain by cosine discrete transform (DCT), and selects important frequency components through channel attention mechanism. Secondly, we use dynamic convolution to regionally process the same input. Thirdly, we use convolutional long short term memory (Conv-LSTM) to extract spatio-temporal information rather than just temporal information. Finally, in order to better simulate the visual characteristics of human eyes, we build a optic chiasm module. The experiment results show that our method outperforms any other methods.

关键词： visualization Convolution Stereo image processing Machine vision Frequency-domain analysis Feature extraction Distortion

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MCascade R-CNN: A Modified Cascade R-CNN for Detection of Calcified on Coronary Artery Angiography images

MCascade R-CNN: A Modified Cascade R-CNN for Detection of Ca...

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IEEE visual communications and image processing (VCIP)

作者： Wei Wang Yi Zhang Xiaofei Wang Honggang Zhang Lihua Xie Bo Xu Beijing University of Posts and Telecommunications Beijing China Fu Wai Hospital Beijing China

ISBN: (纸本)9781665475938

Among cardiovascular diseases, coronary artery calcification (CAC) is a high-risk factor for worsening protopathy and increased mortality. However, the coronary artery an-giogram, which is the main approach for CAC diagnosis, suffers from plenty of photographing noise. This brings difficulties to detect calcification from the background. In this paper, a modified Cascade R-CNN (MCascade R-CNN) network is proposed to deal with the problem of calcium detection in angiograms. In the proposed network, we propose an innovative balanced aggregation pyramid structure, integrating multi-level features of every depth in the feature map, based on enhanced propagation of strong semantic features. In addition, a new convolutional attention mechanism is designed to improve the performance of the detector. Experiments show that the proposed method enjoys better performance in detecting and marking CAC in angiograms,

关键词： visual communication Angiography Calcium image processing Semantics Object detection Medical services

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Block Importance Mapping for Video Encoding

Block Importance Mapping for Video Encoding

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IEEE visual communications and image processing (VCIP)

作者： Jack Enhorn Christopher Hollmann Rickard Sjöberg Jacob Ström Per Wennersten Ericsson Reseach Visual Technologies Ericsson AB Kista Sweden

ISBN: (纸本)9781665475938

This paper presents a novel video encoding algorithm called Block Importance Mapping. In this method, blocks are assigned a quantization parameter, QP, offset based on how likely the block samples are to be used as references in encoding of nearby pictures. The reusability estimation is based on a motion search in pictures immediately before and after the current picture in output order. Blocks that are likely to be used as reference blocks are coded with a lower QP value, i.e., higher quality, whereas blocks that are deemed unlikely to be referenced are coded with a higher QP value. This method has been implemented in the VVC reference software VTM and tested on various configurations and contents. It is reported to provide BD-rate savings of 1.93% for PSNR and 3.69% for MS-SSIM in Random Access and 2.26% for PSNR and 4.29% for MS-SSIM in Low Delay coding. For HDR content, wPSNR BD-rate savings of 1.18% in Random Access configuration is reported.

关键词： image coding Quantization (signal) visual communication Estimation Video compression Software Encoding

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Enhancing the Sense of Unity with Performers in VR Space by Mutual Presentation of Electroencephalogram Information 11

Enhancing the Sense of Unity with Performers in VR Space by ...

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11th IEEE Global conference on Consumer Electronics, GCCE 2022

作者： Ide, Kota Horie, Ryota Shibaura Institute of Technology Graduate School of Engineering and Science Department of Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Information and Communications Engineering Tokyo Japan

ISBN: (纸本)9781665492324

Performances in VR space, such as VR music concerts, are less likely to convey audience's reactions than in real space, making it difficult to feel the sense of unity. In this paper, we introduce a system that enhances the sense of unity among performers and audience in VR space by generating visual effects based on the beta-alpha ratio signals obtained from a single-channel electroencephalograph and sharing them with the others. We evaluated the significance of the system by conducting juggling-shows in VR space using the system. The results showed the potential to enhance the sense of unity among the performers and the audience. © 2022 IEEE.

关键词： Electroencephalography

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Dual-stream Self-attention Network for image Captioning

Dual-stream Self-attention Network for Image Captioning

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IEEE visual communications and image processing (VCIP)

作者： Boyang Wan Wenhui Jiang Yuming Fang Wenying Wen Hantao Liu Jiangxi University of Finance and Economics NanChang China Cardiff University Cardiff United Kingdom

ISBN: (纸本)9781665475938

Self-attention based encoder-decoder models achieve dominant performance in image captioning. However, most existing image captioning models (ICMs) only focus on modeling the relation between spatial tokens, while channel-wise attention is neglected for getting visual representation. Considering that different channels of visual representation usually denote different visual objects, it may lead to poor performance in terms of object and attribute words in the captioning sentences generated by the ICMs. In this paper, we propose a novel dual-stream self-attention module (DSM) to alleviate the above issue. Specifically, we propose a parallel self-attention based module that simultaneously encodes visual information from the spatial and channel dimensions. Besides, to obtain channel-wise visual features effectively and efficiently, we introduce a group self-attention block with linear computational complexity. To validate the effectiveness of our model, we conduct extensive experiments on the standard IC benchmarks including MSCOCO and Flickr30k. Without bells and whistles, the proposed model performs new SOTAs containing 135.4 CIDEr score on MSCOCO and 70.8 CIDEr score on Flickr30k.

关键词： visualization image coding visual communication Computational modeling Benchmark testing Integrated circuit modeling Computational complexity

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Point Clouds Registration Algorithm Based on Spatial Structure Similarity of visual Keypoints 16th

Point Clouds Registration Algorithm Based on Spatial Structu...

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16th International conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Gao, Yingshuo Ma, Jie Wu, Bingli Zhang, Tianxiang Yang, Yu National Key Laboratory of Multispectral Information Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811912528

In autonomous driving technology, vehicles use LiDAR to accomplish various tasks. Issues are that the point cloud collected by the LiDAR is too sparse and some objects are mutilated, which is a great challenge for tasks such as detection and recognition. It is necessary to merge multiple frames of point clouds with registration algorithm to make data complete and dense. Previous point cloud registration methods run slowly and have low accuracy when dealing with autopilot field point clouds. It is difficult to find the correspondence of different point clouds. While humans can easily find the exact corresponding through the visual information received by eyes, as it contains color and texture. The visual images acquired by optical cameras can simulate the human eyes well. Therefore, we propose to use visual image assisted point cloud registration. Firstly, we extract the keypoints of visual images, then we obtain their depth by depth completion algorithm and back-project them into 3D space as 3D keypoints. Finally, we screen 3D keypoint pairs by spatial structure similarity to estimate the rigid transformation parameters. After experiments, it is proved that our method achieves substantial improvement in both speed and accuracy. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Optical radar

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Unsupervised image-to-image Translation with Patch Pyramid Dual Contrastive Learning for Cross Domain Object Detection

Unsupervised Image-to-Image Translation with Patch Pyramid D...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Meng, Yunlong Zhao, Lifan Xu, Lin Ai Institute Shanghai Em-Data Technology Co. Ltd. China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Unsupervised image-to-image (UI2I) translation methods aim to learn a mapping between different visual domains with well-preserved content and consistent structure. It has been proven that the generated images are useful for enhancing the performance of computer vision tasks like object detection in a different domain with distribution discrepancies. Current UI2I translation techniques are highly relied on cycle-structure consistency and content/style disentanglement. In-tense focus has been paid on global translation over an en-tire image, instead of the stylistic variations and complex structure among multiple disparate object instances and back-grounds, resulting in unsatisfactory performance gain for the downstream vision tasks. In this work, we propose Patch Pyramid Dual Contrastive Learning Unsupervised image Translation (PPDCLUIT) framework to strengthen the cross domain object detection performance. We present patch pyra-mid dual contrastive learning strategy for coherent associations at each specific location and propose identity loss to further enforce the object instances preservation. We im-plement extensive experiments to demonstrate the efficacy of our PPDCLUIT framework for cross domain object detection and achieve state-of-the-art performance on the benchmarks of Normal-to-Foggy, and Day-to-Night. © 2022 IEEE.

关键词： Object detection

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