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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VII"

共 12976 条记录，以下是1021-1030 订阅

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Laplacian of Gaussian Based on Color Constancy Algorithm for Surrounding image Stitching Application

Laplacian of Gaussian Based on Color Constancy Algorithm for...

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IEEE International conference on Consumer Electronics - Taiwan (ICCE-TW)

作者： Lee, Tsung-Han Chou, He-Sheng Chen, Tsung-Yi Lo, Wen-Shen Zhang, Jun-Ting Chen, Chiung-An Lin, Ting-Lan Chen, Shih-Lun Chung Yuan Christian Univ Dept Elect Engn Taoyuan Taiwan Ming Chi Univ Technol Dept Elect Engn New Taipei Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan

ISBN: (数字)9781665470506

ISBN: (纸本)9781665470506

This paper presents a new algorithm for Laplacian of Gaussian (LoG) image processing systems. In order to improve nowadays digital capture system and digital display calibration. Color constancy is added to improve the measurement of object color based on independent light sources. To satisfy the human eye's recognition of colors, the angular error must be corrected to make the light source reflect naturally in the image. In addition, the hardware design of classic white balance algorithms such as Gray-World, Max-RGB, and Gray-Edge with the Minkowski norms is implemented in this study. The proposed algorithm is simulating with a Gaussian filter and an additional Laplacian of Gaussian (LoG) to generate real-time masks that automatically brighten images. Compared with several previous studies, the proposed algorithm shows lower angular errors while maintaining the quality of the graphics. The motivation is to make better quality watches on display facilities and better capture systems.

关键词： White Balance Color Constancy Gaussian filter Laplacian of Gaussian(LoG) Gray-World Max-RGB Gray-Edge Minkowski norm Angular error

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Enhanced K-means Color Clustering Based on SLIC Superpixels Merging incorporated within the Entomology Software: AInsectID

Enhanced K-means Color Clustering Based on SLIC Superpixels ...

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IEEE Workshop on Evolving and Adaptive Intelligent systems (EAIS)

作者： Haleema Sadia Parvez Alam School of Engineering The University of Edinburgh Edinburgh Scotland UK

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

Superpixel-based segmentation is an important pre-processing step for the simplification of image processing. The subjective nature behind the determination of optimal cluster numbers in segmentation algorithms can result in either underor over-segmentation burdens, depending on the image type. Insect wings, with their intricate color patterns, pose significant challenges for the accurate capture of color diversity in clustering algorithms, assuming a spherical and isotropic cluster distribution is used. This paper introduces a hybrid approach for color clustering in insect wings, integrating the Simple Linear Iterative Clustering (SLIC) method to generate the initial superpixels, and a DeltaE 2000 function the precisely discriminated merging of superpixels. Color differences between superpixels serve to measure homogeneity during the merging process. The proposed new algorithm demonstrates enhanced segmentation as it overcomes the issue of over-segmentation and under-segmentation, as evidenced by the results derived from the Boundary Recall, Rand index, Under-segmentation Error, and Bhattacharyya distance using ground truth data. The Silhouette score and Dunn Index are also used to quantitatively evaluate the efficacy of our new proposed clustering technique.

关键词： image segmentation image color analysis Insects Merging Clustering algorithms Software Hybrid power systems

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image Quality Assessment for Computer Vision Based Perception algorithms Using Edge and Structural Features 38

Image Quality Assessment for Computer Vision Based Perceptio...

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38th IEEE Region 10 conference, TENCON 2023

作者： Akshaya, V.G. Rithika, S. Lanciya Angel, A. Amirtha Pravin, K.S.M. Balasubramanian, Karthi Srinivasan, Suganthi Rajegowda, Rakesh Bhattacherjee, Payal Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Coimbatore India R & D-Computer Vision Systems Valeo India Pvt.ltd Comfort Driving Assistance Group Chennai India

ISBN: (纸本)9798350302196

image Quality Assessment (IQA) is a critical task in image processing, computer vision, and other related fields, as it helps in evaluating the effects of various impairments on the Quality of Experience (QoE) of consumers. However, current IQA metrics may have limitations in evaluating image quality accurately for different types of images or under different conditions. To address this, a proposed approach involves calculating an overall image quality score based on linear combination of individual image quality metrics. This method considers multiple image features such as sharpness, contrast, color, and noise, and assigns weights to each feature based on its relative importance in determining overall image quality. By combining and weighting multiple image features, the proposed approach aims to provide a more comprehensive and accurate evaluation of image quality compared to using individual metrics alone. In this study, we focus on the importance of edge features and structure-based metrics in object detection and analyze existing No-Reference (NR) IQA metrics, including Just Noticeable Blur (JNB), Cumulative Probability Blur Detection (CPBD), Visual Quality Assessment (VQA), Blind/Referenceless image Spatial Quality Evaluator (BRISQUE) and No-Reference Low- Light image Enhancement Evaluation (NLIEE), and propose a linear combination formula that combines these metrics to evaluate image. We evaluate the performance of the proposed metric on several datasets by calculating the linear combination of two, three, and all the five metrics. The weights assigned to each metric in the linear combination formula are determined through experimental analysis. The combined metric scores and MOS scores are then used to compute the Spearman's rank order correlation coefficient, which measures the monotonic relationship between two variables. A higher SROCC value indicates a stronger correlation between the combined metric and MOS scores and is used to evaluate the performanc

关键词： image quality

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Real Rainy Scene Analysis: A Dual-Module Benchmark for image Deraining and Segmentation

Real Rainy Scene Analysis: A Dual-Module Benchmark for Image...

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2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Zhao, Shilei Huang, Wenxin Yang, Mingxuan Liu, Wenxuan Wuhan Dashuiyun Technology Co. Ltd China School of Computer Science and Information Engineering Hubei University China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798350313154

Scene understanding in adverse weather conditions (e.g., rainy and foggy days) has drawn increasing attention, raising some specific benchmarks and algorithms. However, rain streaks in images and videos can significantly degrade visual quality and reduce the effectiveness of computer vision algorithms. Concerning on lack of rainy-clean pairs training samples in real rainy scenes, we propose an unsupervised deraining method that does not require any explicit rain-clean image pairs for training. Instead, our approach leverages the statistical properties of rain streaks and clean regions to learn a rain removal model. Specifically, we choose contrastive learning that encourages the generator to produce derained images that are indistinguishable from clean images while the discriminator distinguishes between real clean images and rain streak patterns. Furthermore, we incorporate a semi-supervised mechanism during segmentation module that exploits low-confidence Pseudo-label to be treated as negative samples for certain classes to facilitate contrastive learning. Our experiments demonstrate that our integrated framework achieve better performance on the given challenging scenarios such as heavy rain and dynamic scenes. © 2023 IEEE.

关键词： Rain

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Glaucoma Stage Classification Using image Empirical Mode Decomposition (IEMD) and Deep Learning from Fundus images 4th

Glaucoma Stage Classification Using Image Empirical Mode Dec...

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4th EAI International conference on Cognitive Computing and Cyber Physical systems, IC4S 2023

作者： Shankar, D. Harsha, I. Sri Madhuri, P. Shyamala Naidu, J. N. S. S. Janardhana Madhuri, P. Krishna Cherukuvada, Srikanth Department of Computer Science and Engineering Vishnu Institute of Technology Kovvada India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031488870

Glaucoma is an ocular pathology characterized by the gradual deterioration of neural cells in the eye, which is attributed to elevated intra ocular pressure within the retina. Glaucoma takes the second spot in terms of its prevalence as a neurodegenerative eye disease, failure to diagnose glaucoma at an early stage can lead to complete blindness. This underlying issue requires a streamlined system that uses experienced medical experts, little equipment, and less time. To categories the stages of glaucoma, a Computer-Aided Diagnosis (CAD) method is used called image Empirical Mode Decomposition (IEMD). Segmentation-based algorithms utilizing features like cup-to-disc ratio (CDR) and textural characteristics close to the optic disc region can distinguish glaucoma. To capture pixel variations for this investigation, the pre-processed fundus images are separated and transformed into a diverse range of intrinsic mode function (IMFs). The study employs deep learning-based framework for finding the optic nerve head from coloured fundus photographs, to transform the fundus images into a delimited region of interest (ROI) and utilizes multiple deep networks like RFCN and RCNN classifiers identified glaucoma stages separately. The CAD system serves as an automated tool for retinal image processing and demonstrates superior performance over the RFCN classifier, achieving an impressive accuracy of 96% with the RCNN classifier in classifying glaucoma stages. © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Computer aided diagnosis

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Research on Graph Neural Network Algorithm and image Recognition Application 20

Research on Graph Neural Network Algorithm and Image Recogni...

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20th International Computer conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Xu, Jia-Hao Li, Jian-Ping School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798350318982

The emergence of graph neural networks makes up for the limitations of traditional neural networks in dealing with complex graph data, and they are widely used in the fields of social networks and recommender systems, but at the same time, there are some defects in themselves. This paper introduces the basics of graph neural networks, and also summarizes the advantages and disadvantages of GSN, GraphSAGE, DeepWalk and Node2Vec algorithms. Finally, it also discusses the application of graph neural networks in the field of image recognition, summarizes its deficiencies in practical applications, and puts forward the prospect of the future development of graph neural networks. © 2023 IEEE.

关键词： image recognition

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Deep Learning based Parts Classification in a Cognitive Robotic Cell System 26

Deep Learning based Parts Classification in a Cognitive Robo...

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26th International conference on System Theory, Control and Computing (ICSTCC)

作者： Rosioru, Sabin Stamatescu, Grigore Stamatescu, Julia Fagarasan, Ioana Popescu, Dan Asti Automat Bucharest Romania Univ Politehn Bucuresti Automat & Ind Informat Bucharest Romania

ISBN: (数字)9781665467469

ISBN: (纸本)9781665467469

Advanced manufacturing systems increasingly rely on intelligent algorithms to discriminate, model and predict system behaviours that lead to increased productivity. Edge intelligence allows the industrial systems to collect, compute and act based on process data while reducing the latency and cost associated to an hierarchical control system in which complex decisions are generated in the upper layers of the automation hierarchy. Greater local computing capabilities allow the online operation of such algorithms while accounting for increased performance requirements and lower sampling periods of the control loops. In this work we present the concept of a cognitive robotic cell that collects, stores and processes data in situ for enabling the control of a robotic arm in a production setting. The main features that characterise the robotic cell are embedded computing, open interfaces, and standards-based industrial communication with hardware peripherals and digital twin models for validation. An application of part classification is presented that uses the YOLOv4 image processing algorithm for real-time and online assessment that guides the control of an ABB IRB120C robotic arm. Results illustrate the feasibility and robustness of the approach in a real application. Quantitative evaluation underlines the performance of the implemented system.

关键词： robot control deep learning part classification YOLOv4 image processing

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Transform-domain image Enhancement for Underwater Visual SLAM based on Homomorphic Filter

Transform-domain Image Enhancement for Underwater Visual SLA...

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image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Shuo Zhang Chaoyu Zhang Wei Wang Xinrui Zeng Bin Luo State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China China Nuclear Power Operation Technology Corporation LTD Wuhan China CNNC Engineering Research Center of Nuclear Industry Special Robot Wuhan China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Underwater visual SLAM algorithms play a vital role in autonomously localizing Autonomous Underwater Vehicles (AUV) in challenging underwater environments. The low-light conditions underwater necessitate the use of artificial lighting sources. However, this can lead to issues such as overexposed or underexposed images, resulting in reduced image contrast and information content. This study introduces a transform-domain image enhancement technique aimed at bolstering the resilience of underwater visual simultaneous localization and mapping (SLAM) systems. Specifically, a homomorphic filtering method is implemented to alleviate the impact of low-frequency lighting variations on images, thereby enhancing overall image clarity and bolstering the performance of visual SLAM systems. The efficacy of this filtering approach is validated through experiments conducted on the AQUALOC dataset. Our findings demonstrate that this method enhances the success rate of visual SLAM tracking, thereby enhancing its utility in AUV localization tasks.

关键词： Location awareness image quality Visualization Autonomous underwater vehicles Simultaneous localization and mapping Filtering Filtering algorithms Feature extraction Artificial light image enhancement

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Optimizing FPGA Hardware Design for Temporal Filtering of High-Definition Video

Optimizing FPGA Hardware Design for Temporal Filtering of Hi...

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International conference on Computers, Communications, and systems (ICCCS)

作者： Xingjian Fu College of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

In daily life, noise reduction is an inevitable aspect of video or image processing because noise can affect the subjective quality of viewing. Therefore, video denoising filtering is a crucial operation in video image processing. Among various denoising methods, motion-compensated temporal filtering is a typical and effective filtering technique. However, there is still room for further research in the hardware implementation of this method. Hence, this paper investigates the Multi-hypothesis Motion-Compensated Filter algorithm and proposes an efficient hardware structure for the filtering algorithm. By parallelizing or pipelining various modules, hardware efficiency is effectively improved. By FPGA evaluation, the final experiments demonstrate that the hardware structure for motion-compensated temporal weighted filtering presented in this paper can meet the requirements of video denoising filtering with a resolution of 1920xl080@60fps.

关键词： Filtering Communication systems image processing Noise reduction Noise Filtering algorithms Hardware

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A Comprehensive Analysis of Dithering algorithms and GPU Implementations

A Comprehensive Analysis of Dithering Algorithms and GPU Imp...

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Applied Artificial Intelligence and Computing (ICAAIC), International conference on

作者： Radhika V. Kulkarni Aaditya Deshpande Pratik Dagale Devyani Manmode Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This research paper conducts a thorough comparative analysis of various dithering algorithms, prioritizing the GPU-optimized Floyd-Steinberg algorithm. Dithering plays a crucial role in improving image quality in constrained display scenarios. The study evaluates popular algorithms, including Bayer matrix, Ordered dithering, and Error Diffusion, while specifically exploring enhancements in the Floyd-Steinberg algorithm through GPU optimization. Quantitative metrics such as MSE, PSNR, and SSI are employed, alongside subjective assessments, to gauge algorithmic performance. The research delves into adaptability across color spaces and image types, providing insights for algorithm selection. Emphasizing real-time applications, the study offers developers valuable guidance in choosing efficient dithering techniques. The GPU-optimized Floyd-Steinberg algorithm emerges as a promising solution, showcasing its potential for accelerated computing environments and resource-intensive tasks.

关键词： image quality Measurement image color analysis Graphics processing units Focusing Parallel processing Real-time systems

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