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Quality-Oriented Feature Regression for Robust image Similarity Metric

Quality-Oriented Feature Regression for Robust Image Similar...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Guo, Jingyu Bao, Qiqi Zhu, Rui Yang, Wenming Liao, Qingmin Tsinghua University Shenzhen International Graduate School Department of Electronic Engineering Beijing China City University of London London United Kingdom

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Full-reference image quality assessment aims to predict the perceptual quality of a distorted image based on its similarity to the pristine reference. In this paper, we propose a robust image similarity metric by fully exploring the representation power of deep learning-based features. A convolutional neu-ral network (CNN) is adopted to extract deep features from multiple scales. We show that such CNN features that con-tain multi -scale visual information are comprehensive and ro-bust enough for quality assessment. We further propose a quality-oriented feature regression (QOFR) module based on the multi-layer perceptron architecture. The QOFR module can efficiently integrate hierarchy CNN features and generate the final quality score. Extensive experiments on the bench-mark datasets demonstrate that our method achieves state-of-the-art performance with outstanding robustness and general-ization ability. © 2022 IEEE.

关键词： image quality

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Improving Spherical image Resampling Through Viewport-Adaptivity

Improving Spherical Image Resampling Through Viewport-Adapti...

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IEEE International conference on image processing

作者： Andy Regensky Viktoria Heimann Ruoyu Zhang André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

The conversion between different spherical image and video projection formats requires highly accurate resampling techniques in order to minimize the inevitable loss of information. Suitable resampling algorithms such as nearest neighbor, linear or cubic resampling are readily available. However, no generally applicable resampling technique exploits the special properties of spherical images so far. Thus, we propose a novel viewport-adaptive resampling (VAR) technique that takes the spherical characteristics of the underlying resampling problem into account. VAR can be applied to any mesh-to-mesh capable resampling algorithm and shows significant gains across all tested techniques. In combination with frequency-selective resampling, VAR outperforms conventional cubic resampling by more than 2 dB in terms of WS-PSNR. A visual inspection and the evaluation of further metrics such as PSNR and SSIM support the positive results.

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IRAD: Input-Reference Joint Driven Reconstruction for Unified Anomaly Detection

IRAD: Input-Reference Joint Driven Reconstruction for Unifie...

引用

IEEE visual communications and image processing (VCIP)

作者： Zixin Chen Xincheng Yao Yan Luo Zhenyu Liu Baozhu Zhang Chongyang Zhang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Ningbo Haitang Information Technology Co. Ltd Ningbo China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Unified (multi-class and cross-class) anomaly detection (AD) is a growing area of interest in real-world applications. However, the popular reconstruction-based AD approach usually faces two significant challenges: the "identical shortcut" issue (copying the input as output) and the lack of class adaptability (the AD model cannot be directly applied to new classes). To address these challenges, we propose a novel unified AD method, named IRAD (Input-Reference Joint Driven). Our core insight is to effectively incorporate both input and references into the reconstruction process. Our IRAD consists of three components: 1) A Suspicious Anomaly Substituting module that replaces the potential abnormal regions of input with anomaly-free reference patches to prevent abnormal information leakage, effectively addressing the "identical shortcut". 2) An Input-Reference Fusing module that merges reference embeddings with input, which urges the subsequent Decoder to effectively utilize the normal reference patterns to reconstruct anomaly-free samples, making our model more class-adaptive. 3) A Rich Feature Preserving Decoder that efficiently preserves low-level details, mitigating low-level information degradation during reverse construction from high to low level. In multi-class AD, IRAD achieves better results on Mvtec-AD, BTAD, and VisA. In cross-class AD, IRAD also outperforms the baesline methods on Mvtec-AD and VisA.

关键词： Degradation Adaptation models visual communication Benchmark testing Information leakage Decoding image reconstruction Anomaly detection Faces

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A Recent Advancements in Multi-Modal Medical image Fusion Techniques using SWT, NSST, NSCT, and CNN

A Recent Advancements in Multi-Modal Medical Image Fusion Te...

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Cognitive Computing in Engineering, communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

image fusion is a method used in image processing to provide a more complete representation by amalgamating features and data from many images. Multimodal medical image fusion involves the integration of medical images from many imaging modalities, including computed tomography (CT) scans, positron emission tomography (PET), and magnetic resonance imaging (MRI), into one single dataset. This integration enhances the visualisation of anatomical structures and clinical situations, hence improving diagnostic accuracy by leveraging the strengths of each medium. This study employs MRI, CT, and PET scans as experimental modalities. This review aims to compare various multi modal medical image approach based on Stationary Wavelet Transform (SWT), Non-Subsampled Shearlet Transform (NSST), Convolutional Neural Network (CNN) and NonSubsampled Contourlet Transform (NSCT). This study examines the latest conventional and non-conventional research conducted within these disciplines. It further evaluates these approaches according to diverse image quality metrics and many quantitative assessments. According to this comparison, CNN-based fusion demonstrates superior results, as the overall visual and parametric quality of the fusion outcomes surpasses that of the other approaches evaluated.

关键词： image quality Wavelet transforms visualization Reviews Magnetic resonance imaging Computed tomography Convolutional neural networks Positron emission tomography Medical diagnostic imaging image fusion

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Intelligent Reflection Elimination Imaging Device based on Polarizer

Intelligent Reflection Elimination Imaging Device based on P...

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IEEE visual communications and image processing (VCIP)

作者： Xinru Chen Hang Liu Menghan Hu Lejing Zhang Yunmian Li East China Normal University Ruian Dawan Polaroid Material Technology CO. LTD.

ISBN: (纸本)9781665475938

Glass reflection is a problem when taking photos through glass windows or showcases. As the visual quality of captured image can be enhanced by removing reflection, we develop an intelligent reflection elimination imaging device based on polarizer to minimize reflection effect on the images. The system mainly consists of a polarizing module, an image analysis module and a reflection removal module. The users can hold the device and capture images with minimum reflection whether in the day or night. The demo video is available at: https://***/10.6084/***.19687830.v1.

关键词： visualization image analysis visual communication Imaging Glass Reflection Windows

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Smart Waste Collecting Robot Integration With IoT and Machine Learning 2

Smart Waste Collecting Robot Integration With IoT and Machin...

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2nd International conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Srinivasan, S. Jambulingam, Umamagewaran Purushothaman, K.E. Jegan Amarnath, J. Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chengalpattu India College of Computer Science King Khalid University Saudi Arabia Department of Computer Science and Engineering Kammavari Sangham Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9798350305418

Non-biodegradable waste collecting from a wide area is a time-consuming process. The study aims to create an intelligent robot that can independently collect waste utilizing innovative technology. The study uses the Internet of Things (IoT) to connect and manage the various robot parts. A Raspberry Pi organizes many functions as the system's central processing unit. Real-time visual data, essential for waste detection and identification, is captured by a camera module. Ultrasonic sensors identify obstacles and measure distances so the robot can navigate its environment. The sensor helps the robot avoid collisions and navigate the predetermined space effectively. The robot's robotic arm, which enables it to pick up and collect waste materials physically, is one of its primary features. The Raspberry Pi controls the arm, allowing for accurate and synchronized motions for waste collection. They use machine learning methods to facilitate waste identification and categorization, especially a CNN model. To reliably identify various forms of waste, the CNN model is trained using a collection of waste images. These technologies may be combined to enable the automated waste-collected robot to function independently while effectively locating and collecting rubbish. By seamlessly integrating IoT, machine learning, and robots, this solution helps to save human labor and enhance waste management procedures. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Environmental sustainability image processing Internet of Things (IoT) Waste Collecting Robot

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Exploring Hierarchical Changes in Functional Brain Network Hubs Through Brain-Activity Prediction with Convolutional Neural Networks

Exploring Hierarchical Changes in Functional Brain Network H...

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IEEE International conference on Systems, Man and Cybernetics

作者： Haruka Kawasaki Satoshi Nishida Ichiro Kobayashi Advanced Sciences GS of Humanities and Sciences Ochanomizu University Tokyo Japan Center for Information and Neural Networks (CiNet) Advanced ICT Research Institute National Institute of Information and Communications Technology (NICT) Osaka Japan

This study aims to clarify how functional network hubs change during hierarchical visual processing in the human brain through the estimation of brain states from features extracted using a convolutional neural network (CNN), a hierarchical model of image processing. We used representational similarity analysis for brain states predicted through encoding models based on feature representations at each layer of the CNN, and applied the PageRank algorithm to matrices converted from the generated representational dissimilarity matrices to capture the hub characteristics of brain region-related systems. This succeeded in capturing changes in the hubness of interregional brain coordination during hierarchical information processing in the human cerebral cortex in visual processing. Specifically, we found that the hubness of the occipital visual cortex increased in the early phase of visual processing, and that the hubness of the prefrontal cortex and temporal lobe increased in the late phase of visual processing. From the above, we found that our proposed method allows us to capture hierarchical changes in the hubness of interregional coordination.

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An Industrial Instrument Panel Identification System Design Based on an Improved YOLOv7 and UAV 15

An Industrial Instrument Panel Identification System Design ...

引用

15th International conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023

作者： Ye, Miao Huang, Yuejin Jiang, Qiuxiang Wen, Peng Hu, Hongwen Cheng, Jin Guilin University of Electronic Technology School of Information and Communications China

ISBN: (纸本)9798350308693

Industrial production often requires collecting and reading visual information on instrument panels. Traditional manual inspection and identification methods require considerable work, resulting in inefficient and inaccurate inspections. It is important to design an industrial instrument panel inspection and recognition system that can be applied in real time. In this paper, an industrial instrument panel recognition system is designed by combining an unmanned aerial vehicle (UAV) platform and deep learning techniques. For mobile UAVs, an ultrawideband (UWB) positioning module is used to implement the real-time UAV positioning and its fixed-point cruising function at each instrument panel location point. As the UAV moves around the instrument panel, a modified YOLOv7 model, compressed through a combination of pruning and quantization, is loaded to perform initial detection and recognition of the instrument panel image to ensure real-time transmission of the captured raw image to the ground side. On the ground-side server, the original YOLOv7 model is deployed to perform instrument panel detection on the initially captured images, filter out redundant background information, and then extract the tick marks and pointer information on the instrument panel through the U-Net semantic segmentation model, after which the tick marks and pointer information are processed using traditional image-processing methods. Finally, the instrument panel is identified through the angle method. This method can alleviate the accuracy loss caused by a lightweight model on the mobile side, thus ensuring the quality of accuracy. Finally, a web client is designed using Flask technology to display the uploaded instrument panel images and recognition results via a web terminal. A series of test experiments and their results show that the designed industrial instrument panel detection and recognition system is reliable and efficient in reading industrial instrument panel information in real tim

关键词： Instrument panels

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End-to-End Learned Lossy Dynamic Point Cloud Attribute Compression

End-to-End Learned Lossy Dynamic Point Cloud Attribute Compr...

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IEEE International conference on image processing

作者： Dat Thanh Nguyen Daniel Zieger Marc Stamminger André Kaup Chair of Multimedia Communications and Signal Processing Chair of Visual Computing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Recent advancements in point cloud compression have primarily emphasized geometry compression while comparatively fewer efforts have been dedicated to attribute compression. This study introduces an end-to-end learned dynamic lossy attribute coding approach, utilizing an efficient high-dimensional convolution to capture extensive inter-point dependencies. This enables the efficient projection of attribute features into latent variables. Subsequently, we employ a context model that leverage previous latent space in conjunction with an auto-regressive context model for encoding the latent tensor into a bitstream. Evaluation of our method on widely utilized point cloud datasets from the MPEG and Microsoft demonstrates its superior performance compared to the core attribute compression module Region-Adaptive Hierarchical Transform method from MPEG Geometry Point Cloud Compression with $38.1 \%$ Bjontegaard Delta-rate saving in average while ensuring a low-complexity encoding/decoding.

关键词： Point cloud compression Geometry image coding Tensors Convolution Transform coding Transforms

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Minimization of Submesh Boundary Errors In Dynamic Mesh Coding

Minimization of Submesh Boundary Errors In Dynamic Mesh Codi...

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IEEE International conference on image processing

作者： Koki Kishimoto Kei Kawamura Haruhisa Kato Advanced Visual Communication Laboratory XR Division KDDI Research Inc

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The video-based dynamic mesh coding (V-DMC) standard is a cutting-edge technology for the compression of dynamic mesh data. V-DMC enables parallel encoding and partial decoding by introducing submesh frameworks in which dynamic meshes are separated and independently processed. However, V-DMC may raise submesh boundary errors like holes due to misaligning the existence or coordinates of vertices, degrading the objective and subjective qualities of decoded dynamic meshes. To minimize the boundary errors and improve the coding performance, we propose a two-stage boundary error correction method in V-DMC’s preprocessing and encoding/decoding stages. Specifically, the first stage rearranges the preprocessing order to minimize boundary errors, whereas the second stage fills holes based on boundary information. Experimental results show that the proposed method can minimize the boundary errors among the V-DMC decoded meshes, and thus significantly improve the objective and subjective quality compared to the V-DMC reference software.

关键词： image coding Minimization Software Error correction codes Decoding Standards

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