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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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An Improved Reversible Data Hiding Scheme Based on Referred Frequencies for a VQ Compressed Index

An Improved Reversible Data Hiding Scheme Based on Referred ...

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machine vision, image processing and Imaging Technology (MviPIT), International conference on

作者： Min-Shiang Hwang Kanza Fatima Yu-Lun Wang Chi-Shiang Chan Chia-Chun Wu Department of Computer Science & Information Engineering Fintech and Blockchain Research Center Asia University Taichung Taiwan Department of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. There are many compression methods, such as Huffman, VQ, ZIP, AMBTC, RAR, JPEG, etc. In this article, we propose an improved data hiding in VQ compression method to achieve better capacity and high quality. Experimental results show that our data-hiding approach is practical.

关键词： Steganography image coding Social networking (online) Vector quantization Bit rate Transform coding Imaging Indexes

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Real-time detection of road hazards for autonomous vehicle systems

Real-time detection of road hazards for autonomous vehicle s...

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International image processing, applications and Systems conference (IPAS)

作者： Máté András Szabó Anita Keszler László Tizedes Machine Perception Research Lab. HUN-REN SZTAKI Budapest Hungary

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Automated detection of road hazards such as speed bumps, has become an important area of research due to its potential to improve road safety in autonomous driving. Various techniques have been introduced to detect these hazards using camera vision and artificial intelligence-based image processing methods. However, estimating their distance is still challenging. To address this problem and to satisfy the requirement for real-time on-board data processing, the proposed system has the following properties: (1) high-accuracy road hazard detection by analyzing mono-images and videos with a re-trained YOLO neural network; (2) precise distance measurement utilizing a LiDAR; and (3) efficient local data processing using ROS, implemented on an NviDIA Jetson AGX Xavier. An important contribution of this paper is introducing multiple classes of road hazards when training the network, instead of only focusing on speed bumps and potholes. Furthermore we have analyzed different LiDAR technologies (standard rotating and non-repetitive circular scanning) to evaluate and compare their precision and to demonstrate that our method can be successfully applied regardless of the scanning pattern of the LiDAR.

关键词： Laser radar Roads image processing Cameras Data processing Hazards Real-time systems Distance measurement Sensors Autonomous vehicles

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Breaking Through Color Casts: Enhancing image Fidelity with machine Learning-Based Correction

Breaking Through Color Casts: Enhancing Image Fidelity with ...

引用

Intelligent Systems for Cybersecurity (ISCS), International conference on

作者： Choppa Jeevan Sai Reddy Bavani Akshay Bange Bhawane Busireddy Parvathammagari Srikanth Reddy Addanki Satish Sree Vidyanikethan Engineering College Tirupati India Dept. of ECE School of Engineering Mohan Babu University Tirupati India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

L Color cast, an aberration common in digital images, poses challenges in various image processing applications, affecting image quality and visual perception. This research investigates diverse methodologies for color cast correction, ranging from traditional algorithms to modern machine learning-based approaches. Leveraging a comprehensive dataset of original and corrected images, the present study evaluates the efficacy of each method using quantitative metrics, including ACMO, BREN, GRAS, LAPM, LAPV, LAPD, and WAVV. Results indicate that while traditional techniques like Gray World Algorithm and White Patch Retinex Algorithm demonstrate moderate effectiveness, the implemented machine learning-based algorithm showcases superior performance across multiple color cast levels. By employing linear regression on RGB values, the method efficiently corrects color cast aberrations, yielding visually appealing and perceptually accurate results. Furthermore, the research highlights the significance of robust color constancy algorithms and their role in mitigating color cast distortions in digital images. This study contributes valuable insights into the field of color cast correction, offering practitioners in image processing and computer vision a comprehensive understanding of effective correction strategies. Future research directions may explore advanced machine learning models and integration with color constancy mechanisms to further enhance color cast correction techniques.

关键词： image quality Computer vision machine learning algorithms Accuracy image color analysis Digital images Computational modeling

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IndianFood-7: Detecting Indian Food Items Using Deep Learning-Based Computer vision 1

引用

International conference on Advances and applications of Artificial Intelligence and machine Learning, ICAAAIML 2022

作者： Agarwal, Ritu Bansal, Nikunj Choudhury, Tanupriya Sarkar, Tanmay Ahuja, Neelu Jyothi Uttarakhand Dehradun248007 India CSE Dept. Symbiosis Institute of Technology Symbiosis International University Maharashtra Lavale Mulshi Pune412115 India Uttarakhand Dehradun248007 India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Department of Food Processing Technology Government of West Bengal Malda Polytechnic Bengal State Council of Technical Education West Bengal Malda732102 India

ISBN: (数字)9789819959747

ISBN: (纸本)9789819959730

Indian Food Dishes are famous around the globe. Object detection in an image is a well-known task in computer vision. Indian Food Dishes detection using deep learning-based models will maximize the impact of computer vision-based models in the food domain. Deep learning-based model usage is still limited in recognizing and detecting Indian Food Items due to the lack of datasets on Indian Food. We introduce the IndianFood-7 Dataset, which contains images of more than 800 and having 1700 + annotations spreading beyond seven special Indian food items. We report the comparative study of numerous variants which are having current Avant-grade for object detection models, YOLOR and YOLOv5. Furthermore, we have inspected and evaluated the model performance by revising the predictions verified on the images of the test dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Object detection

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A Fast and Flexible Turret Based Automated vision Inspection (Avi) to Inspect 6 Sides of Small Discrete Components 1

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11th International conference on Robotics, vision, Signal processing and Power applications, RoviSP 2021

作者： Loo, Kean Li Saw, Chong Keat Ibrahim, M.H. Micro Modular System Sdn. Bhd. Bayan Lepas11900 Malaysia School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru80990 Malaysia

ISBN: (数字)9789811681295

ISBN: (纸本)9789811681288

Smart devices components come in a variety of shapes, sizes and texture. To perform a high throughput inspection on all the six sides of the surface of the component is challenging due to tolerances of product dimension and the multiple planes on its surface. The image acquisition system for surface inspection comprises of a CMOS camera, telecentric lens, LED light source and motorized actuator. HALCON vision library was used for image processing. One of the most important requirements of an effective Automated vision Inspection (Avi) is consistent part presentation. Thus, to perform the positioning correction we propose two position recognition method using 2D-metrology and Smallest Rectangle Segmentation. The result of the position recognition was fed to XY rotary table for offset correction before picked up by turret fingers. Actuators for camera focusing were added to increase the flexibility of the system to inspect various component sizes. The Exposure End event of a modern CMOS camera was used for parallelization to optimize the machine vision cycle time. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Autofocus optical imaging system based on image processing 14

Autofocus optical imaging system based on image processing

引用

14th International conference on Information Optics and Photonics, CIOP 2023

作者： Teng, Teng Chen, Guan Xiaofei, Wang Hua, Fan Dongjie, Ju Zhaojuan, Chi Jinan250000 China Shandong JINGWEI Intelligent Equipment Co. Ltd Weifang261000 China

ISBN: (纸本)9781510671744

Autofocus optical imaging systems are widely used in industrial inspection, medical diagnosis, drone photography, machine vision and other fields. image-based autofocus algorithms typically consist of two key steps: image sharpness evaluation and search strategy. In this study, we propose an advanced image-based autofocus algorithm specifically designed for industrial image measurement. In order to improve the accuracy of the clarity assessment, we improve the existing method by introducing Gaussian filtering and threshold controlled Laplace operators. This improvement effectively reduces the effects of noise and light intensity changes, improving the reliability of clarity measurements. In addition, we propose a novel distance adjustment strategy combining coarse and fine tuning as part of the search strategy. This strategy reduces the interference of local peaks, allowing the algorithm to accurately identify the best image focus. The proposed image-based autofocus algorithm has several advantages, e.g. high focusing accuracy, high repeatability, and stability under light intensity changes2. These advantages make it ideal for industrial image measurement applications. To verify the effectiveness of our method, we build an experiment setup in a lab using ADLINK PCI-9114-DG board and a custom-designed lens. The results show that the sensitivity and performance of the autofocus system meet certain requirements. In summary, the image-based autofocus algorithm that we developed enables accurate and reliable focusing. It overcomes some challenges such as noise and light intensity changes to ensures optimal image focus, and improves overall image quality. The successful implementation of our autofocus system can benefit industrial inspection, medical diagnostics and many other applications. © 2023 SPIE.

关键词： Diagnosis

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Low-Textural image Registration: Comparative Analysis of Feature Descriptors 7th

Low-Textural Image Registration: Comparative Analysis of Fea...

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7th International conference on Computer vision and image processing, CviP 2022

作者： Subramanyam, Vasanth Kumar, Jayendra Singh, Shiva Nand Kumar, Roshan Yadav, Arvind R. Tata Steel Ltd Jharkhand Jamshedpur India National Institute of Technology Jharkhand Jamshedpur India Miami College of Henan University Henan China Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India

ISBN: (纸本)9783031314162

Industrial machine-vision (MV) applications require high-speed stitching of low-textural images from multiple high-resolution cameras for Field-of-view expansion. The most vital step in the stitching process is the effective and efficient extraction of features, which becomes challenging for low-textural images. This paper presents a comparative study of five popular feature descriptor algorithms for image stitching viz. Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF), Oriented Fast and Rotated BRIEF (ORB), Binary Robust invariant scalable keypoints (BRISK), and Accelerated-KAZE (AKAZE). The focus of this paper is to present a study of the performance comparison among these feature extraction methods for low-textural images from real-time steel surface inspection systems. Primarily, synchronized images of steel rolled at room temperatures are obtained from a two-camera network with overlapping regions. Feature descriptor algorithms extract features from two images with an overlapping area and further match the features using K-Nearest Neighbour (KNN) algorithm. The performance of the five feature descriptor algorithms is evaluated using a low-textural dataset that consists of a set of 177 images captured from two cameras placed at a fixed distance from each other. The efficiency of these algorithms is quantitatively and qualitatively evaluated using execution time, sensitivity, and specificity. Finally, this paper provides guidelines for future research on problems with FOV expansion in industrial scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Nearest neighbor search

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Robust Optimization-based Neural Architectures Against Adversarial Attacks

Robust Optimization-based Neural Architectures Against Adver...

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IEEE SSD International Multi-conference on Systems, Signals and Devices

作者： Khaoula Ben Ali Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Noureddine Liouane Laboratory of Automatic Signal Image Processing National Engineering School Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Recent years have seen a rapid development in machine Learning, which has profoundly influenced many areas of science and engineering. Among them, computer vision takes the leading place, where important tasks are image classifications powered by CNNs. Despite the great performance of CNNs in complicated scenarios, they remain sensitive to so-called adversarial attacks, and deliberate perturbations leading them to incorrect predictions. Besides more innocuous consequences, this has serious security implications for critical applications, in-cluding medical diagnostics, where misclassifications might result in disastrous outcomes. This research work discusses adversarial attacks on CNNs and other DNNs in computer vision, studying a full range of the generation and detection methods with details while discussing intrinsic vulnerability and robustness. It also proposes a learning framework that will enhance the robustness and security of DNNs and CNNs against such adversarial perils. The ultimate goal is directed to an improvement in the reliability of such models in absolutely critical scenarios for safe deployment into applications where accuracy is crucial.

关键词： Computer vision Accuracy Perturbation methods Computer network reliability machine learning Robustness Security Medical diagnosis Resilience image classification

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Identification and Categorization of Paddy Leaf Diseases Using Deep Learning for Efficient Agricultural Practices

Identification and Categorization of Paddy Leaf Diseases Usi...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International conference on

作者： Annie Sunil George S. vinita Sushila Devi Department of Computer Applications Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Plant diseases can be quickly detected to lessen the harm they cause to crops. Convolutional neural networks are widely used in deep learning, specifically for problems related to pattern recognition and machine vision. This paper aims to provide a comprehensive overview of the numerous image processing and machine learning strategies for rice plant disease diagnosis. It does this by examining the various algorithms and machine learning techniques used to diagnose illnesses in rice plants using photographs of diseased rice plants. This study discusses several methods and offers a concise synopsis of important concepts in the fields of machine learning and image processing in relation to the classification and identification of illnesses in rice plants. This study examines and contrasts various algorithmic models and methods for classifying and identifying diseases that impact rice plants. It includes scientific results that over the last five years have been published in international review publications. In addition, this paper presents an overview and analysis of various methods used to identify agricultural illnesses, leaf health, and the image processing techniques used for the same. The framework mentioned here, can be used to more effectively detect and diagnose various rice crop diseases, which can cause the agriculture industry to experience large losses owing to rice plant diseases. These frameworks are efficient enough to safely automate repeated processes, generating enough data for additional study.

关键词： Deep learning Plant diseases machine learning algorithms Reviews machine vision Crops Classification algorithms

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Self-Adaptive Logit Balancing for Deep Learning Robustness in Computer vision 21st

Self-Adaptive Logit Balancing for Deep Learning Robustness i...

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21st International conference on image Analysis and processing (ICIAP)

作者： Wei, Jiefei Meng, Qinggang Yao, Luyan Loughborough Univ Epinal Way Loughborough LE11 3TU Leics England Univ Nottingham Univ Pk Nottingham NG7 2RD England

ISBN: (纸本)9783031064272;9783031064265

With wide applications of machine learning algorithms, machine learning security has become a significant issue. The vulnerability to adversarial perturbations exists in most machine learning algorithms, including cutting-edge deep neural networks. The standard adversarial perturbation defence techniques with adversarial training need to generate adversarial examples during the training process, which require high computational costs. This paper proposed a novel defence method using self-adaptive logit balancing and Gaussian noise boost training. This method can improve the robustness of deep neural networks without high computational cost and achieve competitive results compared with the adversarial training methods. Meanwhile, this defence method enables deep learning systems to have proactive and reactive defence during the operation. A sub-classifier is trained to determine whether the system is under attack and detect attack algorithms via the patterns of the Log-Softmax values. It can achieve high accuracy for detecting clean inputs and adversarial examples created by seven attack methods.

关键词： machine learning security Adversarial robustness Adversarial examples Deep neural networks

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