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

共 15618 条记录，以下是871-880 订阅

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Advancing image processing through Cutting-Edge Optimization Methods: State-of-the-Art Techniques and Applications

Advancing Image Processing through Cutting-Edge Optimization...

引用

Self Sustainable Artificial Intelligence systems (ICSSAS), International conference on

作者： Usman Ahmad Usmani Junzo Watada Mohammed Umar Usmani Universiti Teknologi Petronas Waseda University Shinjuku City Tokyo Japan University of Malaysia Pahang

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

In the image processing domain, the growth of digital data has intensified the need for efficient and robust optimization techniques. This research study aims to develop and evaluate advanced optimization methods tailored specifically for improving the performance of image processing tasks. It explores the latest advancements in optimization algorithms, including evolutionary algorithms, metaheuristic approaches, and deep learning-based optimization techniques. The study provides an in-depth analysis of these methods, elucidating their strengths, weaknesses, and areas of applicability across diverse image processing tasks such as image denoising, image reconstruction, image segmentation, and image enhancement. By comparing their performance through comprehensive experiments, the paper demonstrates substantial improvements in computational efficiency, accuracy, and generalization. These results highlight the potential of optimization methods to significantly enhance the quality and speed of image processing pipelines, opening new avenues for breakthroughs in computer vision, medical imaging, remote sensing, and other domains. Ultimately, this research not only empowers practitioners with cutting-edge tools but also paves the way for future exploration in the application of optimization techniques within image processing.

关键词： image segmentation Computer vision image processing Pipelines Metaheuristics Optimization methods Evolutionary computation Computational efficiency Remote sensing image reconstruction

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Energy Efficient Approximate Bfloat-16 Floating Point Division Hardware for image processing

Energy Efficient Approximate Bfloat-16 Floating Point Divisi...

引用

Smart Electronics and Communication systems (ISENSE), International conference on

作者： M Nagakalyan G. Sujatha Noor Mahammad Sk Dept. of ECE Sri Venkateswara College of Engineering Tirupati India Dept. of CSE IIITDM Kancheepuram Chennai India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

Energy efficient designs are need of the hour and image processing applications are error tolerant application. Where the error present in the computing does not impact the output visual quality. This work proposes an efficient approach to design a Bfloat floating point division. This work proposes three algorithms that are implemented at the hardware level. All the proposed algorithms will produce lesser error in the output and does not impact the output visual quality. The proposed work reduces the energy sianlficantly than the existing works.

关键词： visualization Power demand image coding Error analysis Transform coding Approximation algorithms Hardware Energy efficiency Delays Standards

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Automated Pneumonia Detection from Chest X-rays using Convolutional Neural Networks: Advancements, Optimization, and Performance Analysis 2

Automated Pneumonia Detection from Chest X-rays using Convol...

引用

2nd International conference on Sustainable Computing and Smart systems (ICSCSS)

作者： Bhuria, Ruchika Gill, Kanwarpartap Singh Upadhyay, Deepak Devliyal, Swati Chitkara Univ Chitkara Univ Inst Engn & Technol Rajpura Punjab India Graph Era Hill Univ Comp Sci & Engn Dehra Dun 248002 Uttarakhand India Graph Era Deemed Univ Comp Sci & Engn Dehra Dun 248002 Uttarakhand India

ISBN: (纸本)9798350391558;9798350379990

Pneumonia, an almost fatal bacterial disease generally affecting one or both lungs and brought on by the bacterium Streptococcus pneumonia, can strike any human. The World Health Organisation (WHO) reports that pneumonia accounts for one in three fatalities in India. Chest X-rays are used to detect pneumonia, require the examination of qualified radiotherapists. Establishing an automated approach to identify pneumonia would therefore be advantageous to treat the illness as soon as feasible, particularly in distant places. Convolutional Neural Networks (CNNs) have attracted a lot of interest for disease diagnosis because of the effectiveness of deep learning algorithms in evaluating medical imagery. Higher accuracy, scalability, and automation opportunities are only a few of the benefits of the suggested CNN-based approach over conventional methods. This study proposes to use deep learning methods to find intricate patterns and characteristics from medical imaging data that are suggestive of pneumonia, hence enhancing diagnosis accuracy. Additionally, picture categorization jobs benefit much from features obtained from big datasets using pre-trained CNN models. This study assesses the capability of these models in this work to diagnose abnormal and normal chest X-rays using pre-trained CNN models as feature extractors and various classifiers subsequently. There is an analytical identification of the optimal CNN model for the task.

关键词： Machine Learning image processing CNN model Classifier Evaluation Artificial Intelligence pneumonia Techniques Diseases Population Resources Medical Health

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Realistic and visually-Pleasing 3D Generation of Indoor Scenes from a Single image 7th

Realistic and Visually-Pleasing 3D Generation of Indoor Scen...

引用

7th Chinese conference on Pattern Recognition and Computer vision

作者： Li, Jie Wang, Lei Chen, Gongbin Li, Ang Qiu, Yuhao Wu, Jiaji Cheng, Jun Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Chinese Acad Sci CAS Beijing Peoples R China Shenzhen MSU BIT Univ Shenzhen Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9789819785070;9789819785087

Artificial Intelligence Generated Content (AIGC) has experienced significant advancements, particularly in the areas of natural language processing and 2D image generation. However, the generation of three-dimensional (3D) content from a single image still poses challenges, particularly when the input image contains complex backgrounds. This limitation hinders the potential applications of AIGC in areas such as human-machine interaction, virtual reality (VR), and architectural design. Despite the progress made so far, existing methods face difficulties when dealing with single images that have intricate backgrounds. Their reconstructed 3D shapes tend to be incomplete, noisy, or lack of partial geometric structures. In this paper, we introduce a 3D generation framework for indoor scenes from a single image to generate realistic and visually-pleasing 3D geometry shapes, without the requirement of point clouds, multi-view images, depth or masks as input. The main idea of our method is clustering-based 3D shape learning and prediction, followed by a shape deformation. Since more than one objects tend to be existing in indoor scenes, our framework will simultaneously generate multi-objects and predict the layout with a camera pose, as well as 3D object bounding boxes for holistic 3D scene understanding. We have evaluated the proposed framework on benchmark datasets including ShapeNet, SUN RGB-D and Pix3D, and state-of-the-art performance has been achieved. We have also given examples to illustrate immediate applications in virtual reality.

关键词： 3D mesh Reconstruction Deep neural networks

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algorithms for image processing of Micro-Objects of Medical Diagnostics systems

Algorithms for Image Processing of Micro-Objects of Medical ...

引用

2025 International Russian Smart Industry conference, SmartIndustryCon 2025

作者： Jumanov, Isroil I. Safarov, Rustam A. Samarkand State University named after Sharof Rashidov Department of 'Artifical intellegence and information systems' Samarkand Uzbekistan Samarkand State University named after Sharof Rashidov Department of Control Theory and Information Security Samarkand Uzbekistan

ISBN: (纸本)9798331511241

Constructive approaches, principles, models for optimizing the identification of micro-objects based on the use of statistical, dynamic models with mechanisms for filtering noise, foreign particles on images of medical objects and pollen grains have been developed. Methods for optimizing the identification of micro-objects with the selection of characteristic objects in the image, the combination of image models with the mechanisms of segmentation, filtering, transformation, and alignment of the laser image processing zone with the CAD model are proposed. algorithms for selecting the contours of objects on an image using a border detector in the transverse direction to each vector of the CAD model have been developed. Modeling of images of microsamples for medical diagnostics of chest diseases was carried out. The width, straightness of the boundaries of structural objects, local defects, parameters of regulation and synchronization of the components of the vision system are determined. algorithms for image processing under conditions of a priori insufficiency, uncertainty of parameters, and low accuracy of data processing are studied. A mechanism for suppressing impulse noise and noise is implemented based on various filtering methods, preserving the boundaries of objects and small-sized parts. Mathematical expressions for estimating identification errors due to non-stationarity, inadequacy of approximation, interpolation, extrapolation of the image contourare obtained, which are synthesized with cubic, biquadratic, interpolation spline functions and wavelet transforms. A software package for recognition and classification of micro-objects has been developed. © 2025 IEEE.

关键词： image segmentation

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Optical Stealth Algorithm Under Photoelectric Countermeasures and Its Effectiveness Evaluation 18th

Optical Stealth Algorithm Under Photoelectric Countermeasure...

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18th International conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2022

作者： Yang, Zhonglin Jiang, Yutong Li, Junbao Li, Qiong Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China China North Vehicle Research Institute 4 HuaiShuling Fengtai District Beijing China

ISBN: (纸本)9789819901043

With the diversification of reconnaissance equipment and methods in the military field, optical stealth technology has become a hot spot in military research under the operational needs of optoelectronic confrontation. An improved self-adaptive visible light and infrared waveband stealth algorithm is proposed here, and its effectiveness under photoelectric confrontation is evaluated. The current digital camouflage design method is mainly through background color mixing, image segmentation, spot combination, etc. It has the defects of smooth and clear edge contour, fixed color/texture scene, and cannot adapt to complex texture background or multiple types of scenes. An improved digital camouflage design method based on fuzzy C-means clustering (FCM) image processing technology is proposed to obtain the color characteristics of the background image. Then, the color block types of the camouflage spots are acquired to reduce the loss of texture information of the background image caused by clustering or segmentation to avoid the phenomenon of clear boundaries caused by single region segmentation. The results show that the use of adaptive optical stealth algorithms in the visible and infrared bands can reduce the probability of being discovered in optoelectronic countermeasures, thereby gaining a head start. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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NeuroAI: Emerging Artificial Intelligence and image processing Techniques in Neuroscience for Enhanced Medical Diagnosis of Brain Tumor 1

NeuroAI: Emerging Artificial Intelligence and Image Processi...

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1st International conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Bhatt, Stuti Sharma, Sachin Bhadula, Shuchi Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350365016

The combination of Artificial Intelligence (AI) and Neuroscience speaks to an advantageous relationship with significant suggestions. AI, a multidisciplinary field in CS which points to imitate insights in machines, whereas Neuroscience explores the brain's structure and cognitive capacities. The union of these disciplines has driven to shared headways. Neuroscience has altogether affected AI, motivating profound neural systems based on natural neural systems. AI exceeds expectations in assignments such as NLP, object detection, and speech recognition. AI, drawing from neuroscience, utilizes support learning standards watched in people and creatures to create autonomous algorithms. AI's capacity to analyze complex information and reveal unapparent design is important for interpreting neuroscience information. AI interfaces can extricate neural signals from the human brain and interpret them into commands for gadgets like automated arms, supporting those with versatility impediments. AI moreover contributes to restorative areas, analyzing neuroimaging information and supporting early discovery of neurological disarranges. This paper conducts a comprehensive audit and usage of the AI and neuroscience cooperative energy, particularly brain tumor, emphasizing their merging for untimely revelation and forecast of neurological clutters. This paper primarily centers on early identifying and localizing the brain tumor locale utilizing division of patient's MRI pictures. © 2024 IEEE.

关键词： image enhancement

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image processing Based on Inter-frame Color Feature Matching for visible Light Positioning

Image Processing Based on Inter-frame Color Feature Matching...

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Signal processing and Intelligent Computing (SPIC), International conference on

作者： Baozhu Yu Xiangyu Liu Yaoheng Chen Lei Guo Jianbo Bao Jiandong Sun School of Information Science and Engineering Shenyang Ligong University Shenyang China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China State Grid Electric Power Research Institute Nari Group Corporation Nanjing China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The indoor imaging visible light positioning (VLP) technology based on the image sensor (IS) utilizes existing indoor lighting infrastructure to provide high-precision indoor positioning services. However, due to the high complexity of traditional image processing algorithms, existing VLP systems are unable to provide real-time positioning services. In response to the above issue, we propose an image processing mechanism based on the inter-frame color feature matching method, which includes the equidistant sampling processing, low complexity decoding algorithm, and color feature matching-based LED region of interest (ROI) determination method. The results show that the system average positioning delay is 80.2ms, indicating better system real-time performance.

关键词： image sensors image color analysis Education Signal processing algorithms Lighting Signal processing Light emitting diodes Real-time systems Decoding Delays

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Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Cameras 26

Memory-Efficient Graph Convolutional Networks for Object Cla...

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26th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Jeziorek, Kamil Pinna, Andrea Kryjak, Tomasz AGH University of Krakow Embedded Vision Systems Group Department of Automatic Control and Robotics Krakow Poland Sorbonne Universite CNRS LIP6 ParisF-75005 France

ISBN: (纸本)9798350304985

Recent advances in event camera research emphasize processing data in its original sparse form, which allows the use of its unique features such as high temporal resolution, high dynamic range, low latency, and resistance to image blur. One promising approach for analyzing event data is through graph convolutional networks (GCNs). However, current research in this domain primarily focuses on optimizing computational costs, neglecting the associated memory costs. In this paper, we consider both factors together in order to achieve satisfying results and relatively low model complexity. For this purpose, we performed a comparative analysis of different graph convolution operations, considering factors such as execution time, the number of trainable model parameters, data format requirements, and training outcomes. Our results show a 450-fold reduction in the number of parameters for the feature extraction module and a 4.5-fold reduction in the size of the data representation while maintaining a classification accuracy of 52.3%, which is 6.3% higher compared to the operation used in state-of-the-art approaches. To further evaluate performance, we implemented the object detection architecture and evaluated its performance on the N-Caltech101 dataset. The results showed an accuracy of 53.7% mAP@0.5 and reached an execution rate of 82 graphs per second. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Cameras

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A Quality Evaluation Method for images in Adverse Weather Conditions

A Quality Evaluation Method for Images in Adverse Weather Co...

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International conference on

作者： Zhengxi Shao Jifeng Chen Wenxin Lin School of Information and Electromechanical Engineering Hunan International Economics University Changsha China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

By adopting an adaptive image quality evaluation method under adverse weather conditions, an advanced image defogging system has been developed. This system aims to predict the optimal algorithm for achieving the best image defogging results. The system integrates nine efficient defogging algorithms. To comprehensively evaluate the defogging performance of these algorithms, a set of evaluation metrics including PSNR, SSIM, LPIPS, ENL, MSE, and RMSE is employed to establish a comprehensive evaluation method. By calculating the values of these evaluation metrics, the optimal algorithm suitable for the image is predicted, and the defogged image produced by the optimal algorithm is processed to output the best-defogged image finally. All predicted data is stored in a database. When defogging an image, if there is matching data in the database, the corresponding optimal algorithm and image processing are directly selected, thereby enhancing the overall defogging efficiency.

关键词： Measurement image sensors image quality Adaptive systems Databases image processing Prediction algorithms Sensor systems Intelligent sensors Meteorology

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