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Dynamic Template Update Mechanism for visual Object Tracking

Dynamic Template Update Mechanism for Visual Object Tracking

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6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： YongQi Hu ZhongWang Cai ZiJun Nie ZiZheng Yu Ying Cao Zhijian Yin Zhen Yang School of Communications and Electronics Jiangxi Science and Technology Normal University Nanchang China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

Given the requirements for robust target classification and accurate target state estimation in visual tracking, SiamFC++ proposes a set of practical guidelines for designing high-performance general-purpose trackers by considering the special nature of visual tracking problems. Inspired by dynamic modules, We propose an empirical method for integrating a dynamic module into the image input, which is concatenated with the template module after feature maps are extracted by the backbone network. Since the position and shape of the object can change significantly within a video sequence, the added dynamic module can better focus on the target region of the feature map to obtain better similarity maps. Extensive experiments and comparisons demonstrate that our simple and effective method achieves reliable results on the benchmarks of LaSOT, TrackingNet, and GOT-10K and provides a significant speed advantage in real-time.

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Two-Stage Robust Reversible image Watermarking Based on Deep Neural Network 8th

Two-Stage Robust Reversible Image Watermarking Based on Deep...

引用

8th International conference on Artificial Intelligence and Security , ICAIS 2022

作者： Huang, Jie Li, Fengyong Zhang, Lei Qin, Chuan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Computer Science and Technology Shanghai University of Electric Power Shanghai200090 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China Computer Science Department University of Victoria VictoriaBC Canada

ISBN: (纸本)9783031067631

This paper proposes a two-stage lossless robust watermarking scheme based on deep neural network. In the first stage, the encoder, noise layer and decoder network are combined to design an end-to-end network framework, which is used to train the input watermark information and cover image. In this framework, the encoder outputs a robust watermarked image whose visual quality is similar to the cover image, and the decoder can extract the embedded watermark information to evaluate robustness. In the second stage, by using reversible information hiding algorithm with extended prediction error, the auxiliary information that can recover the cover image is embedded into the robust watermarked image to ensure reversibility. If the watermarked image does not undergo attack processing, the hidden watermark information can not only be extracted, but also the cover image can be completely recovered. Even if the watermarked image does undergo attack processing, the embedded watermark information can also be extracted correctly because of the strong robustness in the first stage. Experimental results demonstrate the robustness of proposed scheme in terms of JPEG compression, gaussian filtering noise, rotation and shearing operations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Blind Gaussian Deep Denoiser Network using Multi-Scale Pixel Attention

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

引用

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 ...

引用

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...

引用

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 ...

引用

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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