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检索条件"任意字段=3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023"

共 1699 条记录，以下是121-130 订阅

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image processing Algorithm Design for Low-Light EBCMOS Devices Based on FPGA 3

Image Processing Algorithm Design for Low-Light EBCMOS Devic...

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3rd international conference on Electronic Information engineering and Computer Communication, EIECC 2023

作者： Cao, Yi Wei, Na Zhu, Xiangping Ma, Jun Institute of Optics and Precision Mechanics University of Chinese Academy of Sciences State Key Laboratory of Transient Optics and Photonics Xi'an China Xi'An Institute of Atomic Precision Manufacturing Technology Co. Ltd. Xi'an China

ISBN: (纸本)9798350359961

In this study, a hardware platform based on the Xilinx 7-series Field-Programmable Gate Array (FPGA) is employed. The design of a Static Storage Control Module and a Median Filtering Module is accomplished through programming in the Verilog hardware description language using Vivado software. The functionality of these modules is rigorously verified through logical validation using Modelsim software. The algorithm is applied to process low-light images captured by a custom EBCMOS image sensor. Comparative analysis with software testing platforms reveals a substantial improvement in image processing speed when utilizing the FPGA-based hardware resources. This improvement allows the processing of 1 pixel per clock cycle. As a result, this work lays the foundation for the design of lightweight, integrated data acquisition systems for the nextgeneration night vision helmet systems. © 2023 IEEE.

关键词： Algorithm Architecture EBCMOS Field-Programmable Gate Array (FPGA) Hardware Acceleration Hardware Description Language image processing

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image processing-Based Droplet Size Detection 3

Image Processing-Based Droplet Size Detection

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3rd IEEE international conference on Social Sciences and Intelligence Management, SSIM 2023

作者： Yang, Kaikai Gan, Shukun Lv, Xuefei Liu, Zile Ma, Leiming Chang, Yumeng School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China Jilin Institute of Chemical Technology Engineering Training Center Jilin China School of Electrical and Mechanical Engineering Jilin Institute of Chemical Technology Jilin China Jilin Power Supply Company State Grid Corporation of Jilin Province Jilin China

ISBN: (纸本)9798350327786

To solve the problem of uneven illumination, overlapping droplets, and other factors affecting the detection accuracy in detecting the droplet size by a visual image, an image processing for droplet size image detection was developed by comparing the detection performance of commonly used edge detection algorithms. The experimental results showed that this improved method effectively overcame the effects of insufficient light, noise interference, and other factors. The detection results were better than those of the traditional detection method using the laser particle sizer © 2023 IEEE.

关键词： Particle size

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Enhancing Text Detection in Natural Scenes: A Hybrid Approach with MSER, Connected Components, and Norm-CLAHE 3

Enhancing Text Detection in Natural Scenes: A Hybrid Approac...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Selvam, Prabu Nevgi, Akshaj Gunasundari, C. Sowrish, V.K. Natarajan, B. Jessika, S. Sharon School of Computer Science and Engineering VIT University Chennai India School of Computer Science and Engineering VIT Chennai Vellore India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India VIT University Department of Computer Science and Engineering Vellore India School of Computer Science and Engineering VIT Vellore Vellore India

ISBN: (纸本)9798350317022

The textual information present in natural scene images is a valuable clue for many vision-based applications. Locating text areas becomes particularly challenging due to issues like varying lighting conditions, complex backgrounds, non-uniform text sizes, and more. Detecting curved text, in particular, is a demanding task in the fields of computer vision and pattern recognition. This research work proposes a robust approach for the detection of text in natural scene images with different orientations. The proposed method is divided into three key stages: (i) Pre-processing: The original image is converted to grayscale, and this research propose a new Norm-Contrast-limited Adaptive Histogram Equalization (CLAHE) to enhance the contrast in the grayscale image by limiting noise amplification and prevents saturation in dark and bright text regions. (ii) Text Region Extraction: this research combined Otsu's thresholding, Maximally Stable Extremal Region (MSER) and Sobel edge de-tector to extract text regions and enhance edges in the image. (iii) Non-Text Region Removal: In this stage, this research combine Connected Component (CC), Morphological operations, MSER region filters, and basic text geometric properties to create a framework for removing non-text regions and drawing rectangle bounding boxes around the detected text regions. this research assessed the performance of the proposed method utilizing the ICDAR 2017 text dataset, which yielded an overall accuracy of 93.47%, demonstrating highly satisfactory results. © 2023 IEEE.

关键词： connected component edge detector image processing MSER Text detection

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Optimizing Rate Control Model for Screen Content Video in VVC 3

Optimizing Rate Control Model for Screen Content Video in VV...

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3rd international conference on Signal image processing and Communication, ICSIPC 2023

作者： Liu, Wei Chen, Jing Shen, Xueyuan Wang, Shiping Zhou, Tingkai College of Information Science and Engineering Huaqiao University Xiamen361021 China

ISBN: (纸本)9781510670945

Screen content video (SCV) distinguished from traditional natural video by its unique features, such as rich texture edges and flat areas. However, existing rate control algorithms are better fit for natural video than for SCV. A novel rate control (RC) algorithm for screen content coding (SCC) in Versatile Video Coding (VVC) is proposed in this paper. Specifically, spatial-temporal features at both CTU and frame levels of adjacent original and reconstructed frames are calculated based on 3D-LOG filter to guide bit allocation in CTU level. Considering the visual characteristics of human vision system, the rate distortion (R-D) model is redesigned. Extensive experimental results demonstrate the effectiveness of the proposed method, which improves the R-D performance and the accuracy of the bit-rate control. © 2023 SPIE.

关键词： image coding

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A Systematic Review: How Computer vision is Transforming Agriculture in Economic Growth 3rd

A Systematic Review: How Computer Vision is Transforming Agr...

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3rd international conference on Artificial Intelligence and Knowledge processing (AIKP)

作者： Karanam, Santoshachandra Rao Kumar, A. B. Pradeep Yandrapati, Prakash Babu Tangudu, Naresh Peddada, Nagamani Bollipelly, PruthviRaj Goud GITAM Deemed Univ CSE Dept Hyderabad India Aditya Inst Technol & Management CSD & MCA Dept Tekkali Srikakulam India Anurag Univ IT Dept Hyderabad India

ISBN: (纸本)9783031686160;9783031686177

Recent years have seen the emergence of computer vision, a subfield of artificial intelligence (AI), as a technology that has the potential to revolutionize agricultural practices. This might have an impact on many agricultural practices and crop management techniques. This is due to the fact that computer vision can examine pictures and identify patterns of data. This article provides a summary of the uses of computer vision in agriculture as well as the consequences such applications have had. The issues of precision agriculture, disease diagnosis, crop monitoring, and yield computation may be overcome with the use of computer vision technologies such as image recognition, object detection, and pattern analysis. Specifically, it investigates the ways in which these strategies are useful. In addition to this, it investigates the benefits, drawbacks, and possible future applications of computer vision in agriculture, with a particular emphasis on the potential for the sector to improve its levels of productivity, sustainability, and profitability. According to this in-depth analysis, computer vision has revolutionized the agricultural industry and contributed significantly to economic growth. In order to evaluate the financial impacts that computer vision has had on the agricultural industry, this study looks at a wide range of academic papers, publications, and reports. The findings highlight the advancements, benefits, challenges, and future opportunities presented by computer vision technology in the areas of crop monitoring, precision farming, animal management, and harvesting. According to the evaluation, computer vision has the potential to improve farming in terms of productivity, resource allocation, cost reduction, and sustainable practices.

关键词： Computer vision Artificial Intelligence Economic Growth Agriculture Management Agricultural Automation Machine Learning

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A Global Multi-Temporal Dataset with STGAN Baseline for Cloud and Cloud Shadow Removal

A Global Multi-Temporal Dataset with STGAN Baseline for Clou...

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3rd international conference on image processing and vision engineering, improve 2023

作者： Zhu, Morui Liu, Chang Szirányi, Tamás Kende u. 13-17 BudapestH-1111 Hungary Department of Networked Systems and Services Budapest University of Technology and Economics BME Informatika épület Magyar tudósok körútja 2 Budapest Hungary Muegyetem rkp. 3 BudapestH-1111 Hungary

ISBN: (纸本)9789897586422

Due to the inevitable contamination of thick clouds and their shadows, satellite images are greatly affected, which significantly reduces the usability of data from satellite images. Therefore, obtaining high-quality image data without cloud contamination in a specific area and at the time we need it is an important issue. To address this problem, we collected a new multi-temporal dataset covering the entire globe, which is used to remove clouds and their shadows. Since generative adversarial networks (GANs) perform well in conditional image synthesis challenges, we utilized a spatial-temporal GAN (STGAN) to eliminate clouds and their shadows in optical satellite images. As a baseline model, STGAN demonstrated outstanding performance in peak signalto-noise ratio (PSNR) and structural similarity index (SSIM), achieving scores of 33.4 and 0.929, respectively. The cloud-free images generated in this work have significant utility for various downstream applications in real-world environments. Dataset is publicly available: https://***/zhumorui/SMT-CR. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Generative adversarial networks

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Research on image Art Creation System of Integrated 3D Computer image vision Digital Media 3

Research on Image Art Creation System of Integrated 3D Compu...

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3rd IEEE international conference on Electrical engineering, Big Data and Algorithms, EEBDA 2024

作者： Liu, Yang Guo, Dongqi Institute of Arts and Media Guangzhou Vocational University of Science and Technology Guangzhou China Jeonbuk National University Department of Industrial Design Jeonju Korea Republic of

ISBN: (纸本)9798350380989

The accuracy and real-time performance of existing image labeling algorithms are not high. This project intends to study the video sequence feature labeling algorithm for digital multimedia images. A digital multimedia image segmentation method based on wavelet transform is proposed. Then the system adopts the color video segmentation method based on edge detection. Based on visual salience features, the model combines color features and gradient features to describe local image quality. The method fuses the brightness channels of the reference image and the corresponding distorted image, and calculates the similarity between the gradient map of the channel and the gradient map of the reference/distorted image. Target detection is realized by matching the target image with the gray information. Then the color feature and gradient feature are combined organically by means of the mean offset weight method, and a better image quality evaluation effect is obtained. The performance of many evaluation algorithms and has some advantages compared with the existing evaluation methods. © 2024 IEEE.

关键词： 3D image recognition media image digital processing visual feature

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Intelligent video surveillance technology based on Mean Shift 3

Intelligent video surveillance technology based on Mean Shif...

引用

3rd international conference on Signal image processing and Communication, ICSIPC 2023

作者： Xiaoqing, Xue Bo, Chen Ting, Liu School of Physics & Electronic Engineering Xianyang Normal University Shaanxi Xianyang712000 China

ISBN: (纸本)9781510670945

In order to monitor the video state changes of safe human settlements in real time, a Mean Shift algorithm is proposed. The video monitoring images collected in real time were preprocessed with enhancement and denoising, and the gray distribution was extracted to form the feature matrix of the image. The monitoring images collected in real time by the monitoring system and the monitoring images in normal state were classified by Mean Shift algorithm. Experiments show that Mean Shift algorithm has fast convergence speed and high accuracy, and can effectively screen out abnormal images and improve retrieval efficiency. © 2023 SPIE.

关键词： image enhancement

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Research on the surface topography inspection of sandpaper based on machine vision 3

Research on the surface topography inspection of sandpaper b...

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3rd international conference on Innovations and Development of Information Technologies and Robotics, IDITR 2024

作者： Zheng, Huifeng Liu, Jinru Chen, Ge Xiao, Lei College of Mechanical Engineering Donghua University Shanghai China

ISBN: (纸本)9798350385694

To solve the problems of low efficiency and high subjectivity of existing sandpaper morphology parameter detection methods, machine vision methods are used to detect the surface morphology characteristics of sandpaper. A weighted decision matrix is established to select the important parameters that can reflect the topography of the sandpaper surface;Zeiss Smartproof5 fast turntable confocal microscope equipment is used to obtain the true-color view of different sandpaper samples, the pseudo-color view based on the height, and the XYZ three-dimensional point cloud of the sandpaper surface;the image is preprocessed by the methods of threshold segmentation and morphology processing, and then segmented by watershed segmentation;and the key topographic parameters of different sandpaper samples are calculated and compared. The key morphological parameters of different sandpaper samples were calculated and compared. The results show that the number of abrasive grains, abrasive grain size and other parameters increase with the increase of mesh size, while the uniformity of sandpaper distribution decreases with the increase of mesh size. The results of this study can provide a theoretical reference for the realization of automated inspection of sandpaper surface topography. © 2024 IEEE.

关键词： Computer vision

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Colour Correction and Detail Enhancement in Underwater images using Hybrid Real-ESRGAN 3

Colour Correction and Detail Enhancement in Underwater Image...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Chandar, Vijay Nair, Vishnu G Rajeswari, D. SRM Institute of Science and Technology Kattankulathur School of Computing College of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350343632

The motive of underwater image enhancement is to improve and enhance the quality of photographs taken underwater. Various traditonal image processing techniques and deep learning models have been developed to work on this task. But most often the deep learning models and conventional methods of image processing work in silos, leading to subpar results. This research proposes a strategy that utilizes a combination of both approaches to solve this issue. Initially, traditional image processing techniques like colour correction and image stretching are used to enhance the colour of the images. The enhanced images are then fed into a custom Real-ESRGAN model that has been fine-tuned on an underwater image dataset to optimize performance in this context. This hybrid approach has the potential to outperform the models utilizing either traditional or deep learning methods in isolation by leveraging the strengths of both. In addition, batch processing is used in the pipeline to efficiently handle multiple images at the same time. This effectively addresses the scalability problem that arises frequently in image enhancement tasks. With eye-catching visuals, the proposed methodology achieves a Peak Signal-to-Noise Ratio (PSNR) of 23.66 and a Mean Squared Error (MSE) of 0.28, suggesting excellent image restoration and noise reduction. The qualitative assessments show that the improved image graphs have better colour, contrast, and clarity than the originals. © 2023 IEEE.

关键词： Batch processing image Enhancement image Stretching MSI PSNR Real-ESRGAN

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