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image Enhancement and Corrosion Detection for UAV visual Inspection of Pressure Vessels 7th

Image Enhancement and Corrosion Detection for UAV Visual Ins...

引用

7th International conference on Life System Modeling and Simulation, LSMS 2021, and 7th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021

作者： Fei, Zixiang Yang, Erfu Yang, Beiya Yu, Leijian Department of Design Manufacturing and Engineering Management University of Strathclyde GlasgowG1 1XJ United Kingdom

ISBN: (纸本)9789811672064

The condition of a pressure vessel is normally checked by human operators, which have health and safety risks as well as low working efficiency and high inspection cost. visual inspection for pressure vessels can be done by an Unmanned Aerial Vehicle (UAV) with the sensing module. image enhancement techniques and image processing techniques are vital in the UAV inspection of pressure vessels. However, there are several issues to be overcome in the UAV visual inspection of pressure vessels. The images captured by the UAV are of low quality under the cluttered environment due to poor lighting, noises and vibrations caused by the UAV. In this research, a system is developed for UAV visual inspection of pressure vessels using image processing and image enhancement techniques. In the developed system, the input image is captured by the UAV first. Next, efficient image enhancement techniques are applied to the images in order to enhance image qualities. After that, the corrosion part is detected and the percentage of the corrosion area in the entire image is measured. The proposed system has the potential to be implemented for the autonomous correction detection with the image enhancement techniques in UAV visual inspection for the pressure vessels. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Fast Inter Prediction Mode Decision Method Based On Random Forest For H.266/VVC

Fast Inter Prediction Mode Decision Method Based On Random F...

引用

IEEE visual communications and image processing (VCIP)

作者： Kundan Xie Jianquan Zhou Saiping Zhang Fuzheng Yang School of Telecommunications Engineering Xidian University Xi'an China

ISBN: (纸本)9781665475938

In H.266/VVC, many new tools are introduced in the inter prediction process. These new techniques enable the encoder in H.266/VVC to predict motion vectors more accurately, but inevitably increase the coding complexity. To solve this problem, in this paper, we propose an early termination algorithm for inter prediction based on random forest which is characterized by the information provided by the temporal co-located block and the spatial adjacent block of the current Coding Unit (CU). Specifically, the random forest is used to predict whether the inter prediction process of the current CU will be terminated in advance. Our proposed algorithm is implemented on Fraunhofer Versatile Video Encoder (VVenC). Experimental results have shown that, in the Random Access (RA) mode, the encoding time of VVenC is reduced by 7.71 % on average while Bjontegaard Delta Bit Rate (BDBR) increases by 1.48 %.

关键词： image coding Correlation visual communication Video sequences Bit rate Forestry Prediction algorithms

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Rate Controllable Learned image Compression Based on RFL Model

Rate Controllable Learned Image Compression Based on RFL Mod...

引用

IEEE visual communications and image processing (VCIP)

作者： Saiping Zhang Luge Wang Xionghui Mao Fuzheng Yang Shuai Wan School of Telecommunications Engineering Xidian University Xi'an China School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781665475938

In this paper, we propose a rate controllable image compression framework, Rate Controllable Variational Autoencoder (RC-VAE), based on the Rate-Feature-Level (RFL) model established through our exploration on the correlation among target rates, image features and quantization levels. Considering that, when meeting the same target rate, different images should be quantized in different levels, we focus on jointly utilizing the target rate and the extracted features of the image to predict the corresponding quantization level and propose the RFL model. Combining the proposed RFL model with a Hyperprior Continuously Variable Rate (HCVR) image compression network, we further propose the RC-VAE. By controlling information loss in quantization process, the RC-VAE can work at the target rate. Experimental results have demonstrated that one single RC-VAE model can adapt to multiple target rates with higher rate control accuracy and better R-D performance compared with the state-of-the-art rate controllable image compression networks.

关键词： Adaptation models image coding Quantization (signal) Costs Correlation visual communication Process control

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A Lightweight Tire Tread image Classification Network

A Lightweight Tire Tread Image Classification Network

引用

IEEE visual communications and image processing (VCIP)

作者： Fenglei Zhang Da Li Shenghua Li Weili Guan Meng Liu Shandong Jianzhu University Jinan China Monash University elbourne Australia

ISBN: (纸本)9781665475938

VCIP 2022 “Tire pattern image classification based on lightweight network challenge” aims to design lightweight networks that correctly classify tire surface tread patterns and indentation images using less overhead. To this end, we present a novel lightweight tire tread classification network. Concretely, we adopt the ShuffleNet-V2-x0.5 network as our backbone. To reduce the computation complexity, we introduce the Space-To-Depth and Anti-Alias Downsampling modules to pre-process the input image. Moreover, to enhance the classification ability of our model, we adopt the knowledge distillation strategy by considering Vision Transformer as the teacher network. To ensure the robustness of our model, we pre-train it on imageNet and fine-tune the training set of the challenge. Experiments on the challenge dataset demonstrate that our model achieves supe-rior performance, with 99.00% classification accuracy, 25.51M FLOPs, and 0.20M parameters.

关键词： Knowledge engineering Training visual communication Computational modeling Transfer learning Transformers Tires

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Learning from the NN-based Compressed Domain with Deep Feature Reconstruction Loss

Learning from the NN-based Compressed Domain with Deep Featu...

引用

IEEE visual communications and image processing (VCIP)

作者： Liuhong Chen Heming Sun Xiaoyang Zeng Yibo Fan Fudan University Shanghai P.R. China Waseda University Tokyo Japan JST PRESTO Saitama Japan

ISBN: (纸本)9781665475938

To speedup the image classification process which conventionally takes the reconstructed images as input, compressed domain methods choose to use the compressed images without decompression as input. Correspondingly, there will be a certain decline about the accuracy. Our goal in this paper is to raise the accuracy of compressed domain classification method using compressed images output by the NN-based image compression networks. Firstly, we design a hybrid objective loss function which contains the reconstruction loss of deep feature map. Secondly, one image reconstruction layer is inte-grated into the image classification network for up-sampling the compressed representation. These methods greatly help increase the compressed domain image classification accuracy and need no extra computational complexity. Experimental results on the benchmark imageNet prove that our design outperforms the latest work ResNet-41 with a large accuracy gain, about 4.49% on the top-1 classification accuracy. Besides, the accuracy lagging behinds the method using reconstructed images is also reduced to 0.47 %. Moreover, our designed classification network has the lowest computational complexity and model complexity.

关键词： image coding image recognition visual communication Artificial neural networks Linear programming Proposals Computational complexity

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A Deep Learning Based System for Automatic Sorting and Quality Grading of Citrus Fruits

A Deep Learning Based System for Automatic Sorting and Quali...

引用

International conference on Emerging Trends in Networks and Computer communications (ETNCC)

作者： Nidhi Kundu Sonam Geeta Rani Vijaypal Singh Dhaka Computer Science Sri Karan Narendra Agriculture University Jobner Jaipur India Computer Science Engg. Manipal University Jaipur Jaipur India Computer and Communication Engg. Manipal University Jaipur Jaipur India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The citrus industry plays a pivotal role in the global agricultural sector, contributing significantly to the economy. Approximately 161 million tonnes of citrus fruits were produced worldwide. One third of the total production is used in processing industry. The price of citrus fruits is highly dependent on its quality parameters such as size, citrus color index, maturity index, diseases etc. Therefore, grading and sorting of citrus fruits based on the quality parameters is demand of the market to maximize the profit. Manual grading and sorting are labour-intensive, time-consuming, and often prone to human error. Automatic grading and sorting of citrus fruits can resolve the above-stated issues. In this paper, we utilize the pattern recognition and image processing potential of deep learning techniques and propose a deep learning based automated system to revolutionize the citrus fruits grading process. The system utilizes Convolutional Neural Networks (CNNs) to extract high-level features from lemon, orange images, allowing for accurate classification based on quality parameters such as size, color, and visual defects. We curated a dataset comprising diverse lemon, orange samples from Kaggle to evaluate the performance of the developed model. Additionally, our system incorporates real-time processing capabilities, ensuring a swift and continuous flow of fruits through the sorting process. Thus, it offers a robust and scalable solution for the citrus industry to enhance profit in the global citrus market.

关键词： Industries Deep learning visualization Accuracy image color analysis Real-time systems Convolutional neural networks Reliability Indexes Sorting

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image Inpainting with Frequency Domain Wavelet Convolution

Image Inpainting with Frequency Domain Wavelet Convolution

引用

IEEE visual communications and image processing (VCIP)

作者： Jain-Kai Huang Tsung-Jung Liu Kuan-Hsien Liu Dept. of Computer Science and Engineering National Chung Hsing University Taiwan Dept. of Electrical Engineering Graduate Institute of Communication Engineering National Chung Hsing University Taiwan Dept. of Computer Science and Information Engineering National Taichung University of Science and Technology Taiwan

ISBN: (纸本)9781665475938

This paper used Time-Frequency Analysis (TFA) techniques for signal processing on tasks of computer vision. Our main idea is as follows: To build a simple network architecture without two or more convolutional neural networks (CNNs), ana-lyze hidden features by Discrete Wavelet Transform (DWT), and send them into filters as weights by convolutions, transformers or other methods. And we do not need to build the network with 2 or more stages to accomplish this idea. Actually, we try to directly use TFA skills on CNN to build one-stage network. Networks which build by this way not only keep their outstanding performance, but also cost lower computing resources. In this paper, we mainly use DWT on CNN to solve image inpainting problems. And the results show that our model can work stably in frequency domain to realize free-form image inpainting.

关键词： Time-frequency analysis Wavelet domain Convolution visual communication Computational modeling Transforms Transformers

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A Marked Point Process Model For visual Perceptual Groups Extraction

A Marked Point Process Model For Visual Perceptual Groups Ex...

引用

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

作者： Mbarki, Amal Naouai, Mohamed Univ Tunis EL MANAR Fac Sci Tunis Tunis Tunisia

ISBN: (纸本)9781728180687

Perceptual organization is the process of assigning each part of a scene to a specified association of features to be a part of the same organization. In the twenty century, Gestalt psychologists formalized how image features tend to be grouped by giving a set of organizing principles. In this paper, we propose an approach for the detection of perceptual groups in an image. We are mainly interested in features grouped by the proximity law of Gestalt. We conceive an object-based model within a stochastic framework using a marked point process (MPP). We use a Bayesian learning method to extract perceptual groups in a scene. The proposed model tested on synthetic images proves the efficient detection of perceptual groups in noisy images.

关键词： visual perception perceptual organisation image processing marked point process

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An Improved image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy 8th

An Improved Image Super-Resolution Algorithm for Percutaneo...

引用

8th International conference on Cognitive Systems and Information processing, ICCSIP 2023

作者： Li, Xue Zhou, Zihan Wang, Kaifeng Liu, Haiying Qian, Yalong Duan, Xingguang Li, Changsheng Beijing Institute of Technology 5 South Zhong Guan Cun Street Haidian District Beijing China The Department of Spinal Surgery Peking University People’s Hospital No. 11 Xizhimen South Street Xicheng District Beijing China

ISBN: (纸本)9789819980178

High-resolution (HR) spinal endoscopic images are essential to enhance the surgeon’s visual presence for the guidance of surgical procedures. However, available image super-resolution methods, especially deep learning methods, are mostly trained with open-source life scene datasets which possess limited medical image features. To address this issue, we have proposed an improved SRGAN model for the visual enhancement of percutaneous endoscopic lumbar discectomy (PELD) surgical images. Specifically, a residual dense block (RDB) and a dynamic RELU function are introduced. We validate the proposed method on PELD datasets. Quantitative and qualitative comparisons are carried out by comparing methods. The method proposed in this paper improves PSNR by 2.8% and SSIM by 6% compared with the original SRGAN, which proves the superiority of this methods. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Endoscopy

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High-Performance image visual Feature Detection and Matching

High-Performance Image Visual Feature Detection and Matching

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International conference on communications, Signal processing and VLSI, IC2SV 2019

作者： Sambamurthy, N. Kamaraju, M. Department of ECE Gudlavalleru Engineering College Gudlavalleru India

ISBN: (纸本)9789813340572

The fundamental task in high-speed and high-accuracy CCTV video surveillance system is attention-based image visual point detection and matching. The image viewpoints are different at different instants of time, so that the image recognition and matching systems are having large computational complexity. The designed work is to present an ROI-based SIFT image detection and BRIEF matching algorithms with a processing speed of 44 ns, and it meets the real-time requirement. The performance of the designed system is improved with a hybrid sorting-based median filter. These algorithms are able to establish accurate feature extraction and matching at 30fps for 640 Pixel video. This can be implemented using MATLAB and Xilinx tools. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Median filters

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