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

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AI-SIPM 2024: International Workshop on Artificial Intelligence for Signal, image processing and Multimedia 24

AI-SIPM 2024: International Workshop on Artificial Intellige...

引用

4th Annual International conference on Multimedia Retrieval (ICMR)

作者： Ketcham, Mahasak Phodong, Kanyalag Pramkeaw, Patiyuth Yimyam, Worawut Chumuang, Narumol Songmuang, Pokpong Ganokratanaa, Thittaporn King Mongkuts Univ Technol Dept Informat Technol Management Bangkok Thailand Phranakhon Si Ayutthaya Rajabhat Univ Dept Comp Sci Phra Nakhon Si Ayutthaya Thailand King Mongkuts Univ Technol Thonburi Bangkok Dept Media Technol Program Bangkok Thailand Phetchaburi Rajabhat Univ Dept Business Informat Management Phetchaburi Thailand Muban Chombueng Rajabhat Univ Dept Digital Media Technol Bangkok Thailand Thammasat Univ Dept Comp Sci Bangkok Thailand King Mongkuts Univ Technol Thonburi Bangkok Dept Math Bangkok Thailand

ISBN: (纸本)9798400706028

The International Workshop on Artificial Intelligence for Signal, image processing, and Multimedia (AI-SIPM) aims to provide a platform for researchers, practitioners, and industry professionals to exchange ideas, discuss recent advancements, and explore future directions in the field of artificial intelligence (AI) applied to signal processing, image processing, and multimedia technologies. This workshop will feature presentations of novel research findings, practical applications, and innovative solutions addressing various challenges and opportunities in AI-driven signal and image processing, as well as multimedia analysis and understanding. Researchers and practitioners from academia, industry, and government agencies are invited to submit their original research contributions and participate in discussions that foster collaboration and knowledge sharing across different domains. Through this workshop, we aim to accelerate advancements in AI-driven technologies for signal processing, image analysis, and multimedia applications, contributing to the advancement of research and innovation in this rapidly evolving field.

关键词： algorithms signal processing image processing computer science artificial intelligence communication systems

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A Comprehensive Analysis of image processing Methods for Agricultural Product Quality Control

A Comprehensive Analysis of Image Processing Methods for Agr...

引用

2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Nandhini, E. Vadivu, G. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

In the post-harvest stages of agricultural products, labor shortages and poor-quality control lead to significant market losses. The automated industries for agricultural products that use machine learning are evolving, with a recent focus on deep learning (DL). Computer vision (CV) serves as an essential part in the automation method for fruits and vegetables, where image processing (IP) ensures proper product categorization, addressing the issue of product loss. This paper focuses on IP techniques in sequential procedures, which can improve efficiency and precision in agricultural product sorting and grading. Deep neural networks and algorithms are effective because they achieve high classification accuracy with a low loss percentage. © 2024 IEEE.

关键词： Industries Accuracy Reviews image processing Scalability Quality control Loss measurement IP networks Reliability Sorting

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Multi-Direction Strategy with Honey Badger Algorithm and Convolutional Neural Network for Industrial Robot Visual Servo image processing

Multi-Direction Strategy with Honey Badger Algorithm and Con...

引用

2024 International conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Chen, Hao Jilin Communications Polytechnic Deppartment of Engineering & Mechanics Changchun China

ISBN: (纸本)9798350360660

The Industrial robot visual servo image processing requires highly autonomous and intelligence robotic manipulators, with goal of performing manipulation tasks independently without human interventions. However, limit efficiency for large scale, sensitive to noise in input data which affect classification accuracy. This paper, proposed Multi-Direction Strategy with Honey Badger Algorithm (MHBA) and Convolutional Neural Network (CNN) for classification is effectively explore the hyper parameter space of CNN technique is help achieve classification accuracy. The MHBA ability to adapt and explore multiple directions in the parameter space makes CNN maximum efficient to variation in input data. The MHBA model is applied to large scale CNN model is efficiently narrowing down the search space and balances exploration and exploitation allowing for search space. The Pre-processing using linear transformation such as translation or rotation help in adjusting the image dimension while maintaining the essential content. The proposed MHBA-CNN technique for industrial robot is achieving better outcomes such as Mean Absolute Error (MAE) of 2.03, Mean Absolute Scaled Error (MASE) of 3.45, R2 score of 2.45 on raw dataset. The existing technique such as Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) are evaluated of proposed method. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative evaluation of two algorithmic methods to enhance autonomous vehicle vision through de-raining imagery techniques 47

Comparative evaluation of two algorithmic methods to enhance...

引用

conference on Applications of Digital image processing XLVII

作者： Ajami, Hanieh Nigjeh, Mahdi Kargar Nigjeh, Mahsa Kargar Umbaugh, Scott E. Southern Illinois Univ Edwardsville Comp Vis & Image Proc Lab Dept Elect & Comp Engn Edwardsville IL 62026 USA

ISBN: (纸本)9781510679344;9781510679351

The effectiveness of autonomous vehicles relies on clear visual input, which rain can significantly obstruct. Rain streaks degrade the quality of captured images and videos, affecting both user perception and the functionality of outdoor vision systems, such as those in autonomous vehicles. This visual degradation impacts the vehicle's ability to interpret its environment, increasing the risk of driving in rainy conditions. Researchers have responded to this challenge by developing various rain removal algorithms, ranging from single-image to video-based approaches, each with its own strengths and weaknesses. This research aims to develop two novel, efficient single-image rain removal algorithms that strike a balance between high performance and quick execution. The proposed algorithms will address the need for an effective de-raining technique suitable for real-time use in autonomous vehicles. By improving visibility in rainy conditions, this innovation will enhance the performance and safety of autonomous vehicles, contributing to advancements in the field. A survey involving ten image processing experts and professionals, who evaluated the results of both algorithms based on perceived quality and improvement, revealed that Algorithm 1 received a higher average rating (0.58) compared to Algorithm 2 (0.43). Although Algorithm 1 is slightly preferred based on average participant ratings, Algorithm 2's superior edge preservation and image sharpness make it more favorable for applications demanding high accuracy and detailed image retention. Overall, the project meets the demand for real-time rain removal in autonomous vehicles and provides valuable insights into the effectiveness of Algorithm 1 in de-raining images compared to Algorithm

关键词： Autonomous vehicle vision Rain streaks removal Computer vision enhancement CVIPtools Python Algorithmic methods image segmentation

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A UAV-Based Remote Sensing image Automatic Monitoring System Empowered by Artificial Intelligence

A UAV-Based Remote Sensing Image Automatic Monitoring System...

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2024 International conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Liu, Tao Zhang, Bohan College of Computer Science and Technology Heilongjiang Institute of Technology Harbin China School of Information Engineering China University of Geosciences Wuhan Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

ISBN: (纸本)9798350360660

In this study, an automatic monitoring system based on convolutional neural network (CNN) is proposed to address the automation and accuracy of remote sensing image processing. With the maturity and wide application of remote sensing and UAV technologies, efficient and accurate image analysis methods have become particularly important. We developed this system through in-depth research on multiple stages of data preprocessing, feature extraction and model training. In the experimental stage, through the comparison experiments with Support Vector Machine (SVM) and Random Forest (RF) models, it is found that the CNN model has significant advantages in processing speed, anti-interference ability and accuracy. Especially in the processing of urban remote sensing images, CNN exhibits up to 90% accuracy, showing its wide applicability and excellent cross-domain application capability. Overall, this study not only successfully develops an efficient and accurate automatic monitoring system for remote sensing images, but also provides strong theoretical and experimental support for future optimization of CNN architecture in natural environments, improvement of real-time data processing capability, and extension to practical applications such as disaster monitoring and environmental protection. © 2024 IEEE.

关键词： Environmental monitoring

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The Implementation and Optimization of High-Performance image processing algorithms Based on Artificial Intelligence

The Implementation and Optimization of High-Performance Imag...

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2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Ye, Caizeng Wang, Peng Shandong Institute of Commerce and Technology Jinan China Jinan Engineering Polytechnic Jinan China

ISBN: (纸本)9798350315455

image processing (IP) technology has emerged on the basis of AI, digital imaging technology, and multimedia technology, and people began to use computers to process images to improve image quality and improve human vision. With the advancement of computer vision and AI technology, people want to achieve high performance IP. In the field of IP, techniques such as image segmentation, image compression, and image restoration are active research topics in computer vision. In this paper, we propose IP algorithms such as Artificial Bee Colony(ABC) Optimization Algorithm and Searcher Optimization Algorithm (SOA) for this block of image compression, analyze the image compression effect of these two algorithms, and compare the image compression quality by comparing the PSNR of each algorithm, and the results get that the image compression effect based on artificial bee colony(ABC) Algorithm is better and the image compression code book quality is better with the same compression ratio. © 2023 IEEE.

关键词： artificial bee colony algorithm artificial intelligence image compression image processing Search optimization algorithm

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A Rule-Based Expert System for Automated Document Editing 1

引用

20th International conference on Computing and Information Technology (IC2IT2)

作者： Varma, Sandeep Shivam, Shivam Roy, Soumya Deep Ray, Biswarup ZS Associates Pune Pune Maharashtra India

ISBN: (数字)9783031585616

ISBN: (纸本)9783031585609;9783031585616

This research paper introduces a Rule-Based Expert System designed for the automated editing of documents in PDF and PPT formats. The system employs a set of predefined rules, extracted from guideline documents using a Large Language Model (LLM), to execute tasks such as redaction of sensitive text/logo detection and annotation of text elements that deviate from prescribed font size guidelines. Following the detection and annotation process, the system further enhances documents by resizing the detected text elements based on the predefined rules. To achieve these editing tasks, the system integrates advanced image processing techniques, leveraging fine-tuned Optical Character Recognition (OCR) for accurate text extraction from document images. Furthermore, Natural Language processing (NLP) algorithms are utilized to analyze and interpret textual content. The combination of image processing, OCR, NLP, and rule extraction using LLM ensures a comprehensive approach to document editing, enhancing efficiency and accuracy. The proposed system addresses the need for automated and rule-driven document editing, contributing to advancements in information security and document standardization.

关键词： Rule-based Expert System image processing Optical Character Recognition Document Sanitization Natural Language processing

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A Dual-Approach to Drone image Enhancement: Software-Based processing and FPGA Implementation

A Dual-Approach to Drone Image Enhancement: Software-Based P...

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2024 International conference on Smart Electronics and Communication systems, ISENSE 2024

作者： Nair, Arun T Varma, Nanditha N Nandakumar, R. National Institute of Electronics and Information Technology Calicut India

ISBN: (纸本)9798331528126

This work offers a thorough method for real-time dehazing of drone-captured images by different filtering techniques with post-processing improvements. Enhancing visibility and picture clarity in hazy situations is the main goal since it is essential for applications like environmental monitoring, navigation, and surveillance. In order to estimate the atmospheric light and transmission map, the suggested methodology makes use of the dark channel. A farrow filter and a guided filter are then applied to improve the transmission. Metrics including the PSNR, SSIM, Execution time, and MSE that are derived from Python-based image processing implementations are used to assess the effectiveness of the dehazing algorithms. The outcomes show notable gains in processing efficiency and image clarity. In order to accelerate the performance, this method was implemented on an FPGA based SoC. Power consumption and throughput of the FPGA - based solution were evaluated, demonstrating its effectiveness and appropriateness for real-time applications. © 2024 IEEE.

关键词： Drones

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Bayesian Spectral Graph Denoising with Smoothness Prior 58

Bayesian Spectral Graph Denoising with Smoothness Prior

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58th Annual conference on Information Sciences and systems (CISS)

作者： Leone, Sam Sun, Xingzhi Perlmutter, Michael Krishnaswamy, Smita Yale Univ Program Appl Math New Haven CT 06520 USA Yale Univ Dept Comp Sci New Haven CT 06520 USA Boise State Univ Dept Math Boise ID 83725 USA Yale Sch Med Dept Genet New Haven CT USA Yale Univ Wu Tsai Inst New Haven CT 06520 USA Meta AI FAIR Paris France Yale Univ Computat Biol & Bioinformat Program New Haven CT 06520 USA

ISBN: (纸本)9798350369298;9798350369304

Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This is motivated in part by settings such as single-cell RNA where the data is very high-dimensional, but its structure can be captured via an affinity graph. This allows us to utilize ideas from graph signal processing. In particular, we present algorithms for the cases where the signal is perturbed by Gaussian noise, dropout, and uniformly distributed noise. The signals are assumed to follow a prior distribution defined in the frequency domain which favors signals which are smooth across the edges of the graph. By pairing this prior distribution with our three models of noise generation, we propose Maximum A Posteriori (M.A.P.) estimates of the true signal in the presence of noisy data and provide algorithms for computing the M.A.P. Finally, we demonstrate the algorithms' ability to effectively restore signals from white noise on image data and from severe dropout in single-cell RNA sequence data.

关键词： denoising graph signal processing estimation

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Adversarial Encoder-Driven Filter for Targeted image Tagging: A Novel Approach to Visual Content Manipulation 6

Adversarial Encoder-Driven Filter for Targeted Image Tagging...

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6th IEEE International conference on image processing, Applications and systems, IPAS 2025

作者： Mckee, Cole Flowers, Dominic Wood, Jesse Shafer, Ethan United States Military Academy Department of Electrical Engineering and Computer Science West PointNY10996 United States

ISBN: (纸本)9798331506520

Computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised significant concerns among researchers. This paper addresses the vulnerability of image tagging algorithms, particularly focusing on misclassifications induced by autoencoders. We present experiments conducted on Amazon Rekognition, where we developed a specialized autoencoder to manipulate the latent space, forcing it to align with specific tags. By integrating this manipulated latent space with other images, we demonstrate the ability to increase the confidence of a specific tag on Amazon Rekognition, leading to more false positives of the chosen tag. Our study showcases a practical method to exploit Amazon's Rekognition image tagging algorithm using a black box approach. © 2025 IEEE.

关键词： Adversarial machine learning

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