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Color Imaging XXVii: Displaying, processing, Hardcopy, and applications

Color Imaging XXVII: Displaying, Processing, Hardcopy, and A...

IS and T International Symposium on Electronic Imaging: 27th Color Imaging: Displaying, processing, Hardcopy, and applications, COLOR 2022

The proceedings contain 154 papers. The topics discussed include: feature-driven 3d range geometry compression via spatially-aware depth encoding;open source deep learning inference libraries for autonomous driving systems;problems in image target-based color correction;improvement of aerial image by simulations;recognition-aware learned image compression;artist-specific style transfer for semantic segmentation of paintings: the value of large corpora of surrogate artworks;data visualization of crime data using immersive virtual reality;a comparison of non-experts and experts using DSIS method;contrast enhancement: cross-modal learning approach for medical images;a continuous bitstream-based blind video quality assessment using multi-layer perceptron;correspondences for image and video reconstruction;design and analysis on low-power and low-noise single slope ADC for digital pixel sensors;incremental two-network approach to develop a purity analyzer system for canola seeds;advantage of machine learning over maximum likelihood in limited-angle low-photon x-ray tomography;image montage detection based on image segmentation and robust hashing techniques;chatbot integrated with machine learning deployed in the cloud and performance evaluation;and the relationship between vision and simulated remote vision system air refueling performance.

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Roberta with Low-Rank Adaptation and Hierarchical Attention for Hallucination Detection in LLMs

Roberta with Low-Rank Adaptation and Hierarchical Attention ...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Jiaxin Lu Siyue Li Trine University Phoenix USA Northeastern University Santa Clara USA

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

The prevalence of hallucinations in responses generated by large language models (LLMs) poses significant challenges for the reliability of natural language processing applications. This study addresses the detection of such hallucinations through an enhanced Roberta-base model, specifically targeting hallucination responses produced by the Mistral 7B Instruct model. By implementing Low-Rank Adaptation (LoRA) for fine-tuning and incorporating hierarchical multi-head attention and multi-level self-attention weighting mechanisms, we aim to improve both the accuracy of hallucination detection and the interpretability of the model’s decisions. Our experimental results demonstrate that the proposed model significantly outperforms baseline models across various metrics, including accuracy, precision, recall, and area under the curve (AUC). Future research directions will explore the integration of larger-scale models and additional fine-tuning techniques to further bolster the model’s capacity for detecting hallucinations, thereby enhancing the reliability of LLM outputs.

关键词： Measurement Adaptation models Computer vision Accuracy Attention mechanisms Large language models Computational modeling image processing Natural language processing Reliability

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Degradation Aware Multi-Scale Approach to No Reference image Quality Assessment 14

Degradation Aware Multi-Scale Approach to No Reference Image...

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14th Indian Conference on Computer vision, Graphics and image processing, ICVGIP 2023

作者： Roheda, Siddharth Unde, Amit Rashid, Loay Ameta, Abhishek Samsung Research Institute Bangalore India

ISBN: (纸本)9798400716256

With the advent of smartphones and social media, images have become a popular medium for sharing information. As a result understanding the perceived quality of images has gained importance. In the recent years No-Reference image Quality Assessment (NR-IQA) has gained prominence due to the abundance of User Generated Content (UGC) being consumed. applications ranging from photo capture assistance, suggestion of best images for sharing, creation of stories from user media gallery, and enhancement of images for best user experience require NR-IQA. This paper presents a novel Degradation Aware Multi-Scale NR-IQA technique that leverages the multi-scale nature of feature extraction in the Human Visual System (HVS) and also aims at understanding the perceived quality of images across various distortions. This is achieved via learning of two auxiliary tasks: (i) Identification of distortions present in images;(ii) Learning to rank images across distortions. Additionally, a unique data generation strategy is introduced to generate realistic distortions for learning cross-distortion ranking of image quality. The proposed approach achieves State-Of-The-Art (SOTA) performance on multiple IQA datasets and demonstrates generalization in cross-dataset testing. The results showcase the efficacy of the proposed method in identifying and ranking the quality of images across various distortions, making it a promising approach for NR-IQA in real-world applications. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image enhancement

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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...

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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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applications of optical flow methods and computer vision in structural health monitoring for enhanced modal identification

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STRUCTURES 2024年 69卷

作者： Haciefendioglu, Kemal Kahya, Volkan Limongelli, Maria Giuseppina Okur, Fatih Yesevi Altunisik, Ahmet Can Aslan, Tunahan Pembeoglu, Selenay Duman, Cemile Bostan, Aysecan Aleit, Houssein Karadeniz Tech Univ Dept Civil Engn TR-61080 Trabzon Turkiye Politecn Milan Dept Architecture Built Environm & Construct Engn Milan Italy Gazi Univ Fac Engn Dept Civil Engn Ankara Turkiye

This study introduces a novel nondestructive approach to Structural Health Monitoring (SHM) using computer vision and optical flow methods to analyze structural vibrations. It combines advanced image processing techniques, like the Lucas-Kanade Optical Flow method, with spectral analysis tools including the Autoregressive Moving Average (ARMA) model and Enhanced Frequency Domain Decomposition (EFDD) for assessing structural integrity. The research comprises two main components: (i) the development of a vibration monitoring system with industrial cameras and open-source image processing techniques, and (ii) the application of specialized image processing software. Key aspects of the study include the use of displacement sensors with template matching for laboratory and field measurements, a comprehensive vision sensor system with high-grade hardware, and camera calibration to correlate camera images with real-world measurements. The methodology focuses on target tracking through optical flow estimation which is crucial in calculating displacements and analyzing structural movements. Experimental verification was conducted on three models such as a single-story steel planar frame, a single-story space shear frame, and a real-scale steel footbridge. The results show good agreement between the frequencies obtained from video camera-based measurements and those retrieved from numerical models, which validates the experimental approach's reliability. The study emphasizes the potential of computer vision in SHM especially for inaccessible or restricted structures. It highlights the cost-effectiveness and efficiency of the considered non-contact methods. Overall, the research demonstrates significant advancements in remote, nondestructive structural analysis, offering promising implications for predictive maintenance and the safety of engineering structures.

关键词： Structural health monitoring (SHM) Computer-vision Optical flow methods Vibration Non-contact measurement

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Advanced Techniques for Chinese image Captioning: Investigating Attention Mechanisms Based on Object Detection for Chinese image Caption Generation

Advanced Techniques for Chinese Image Captioning: Investigat...

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machine Learning and Computer Application (ICMLCA), International Conference on

作者： Yongbin Hua Pei'ang Li Xiangjin Zeng Hong Xu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan Hubei China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

The task of image caption generation aims to automatically produce natural language descriptions that match the content of images, integrating the fields of machine vision and natural language processing, which holds significant theoretical and practical value. Inspired by top-down attention mechanisms, this paper proposes an innovative attention model. Utilizing the output of pretrained object detection networks as prior knowledge for images, the model guides the generation of natural language descriptions. By directly incorporating the results of object detection as attention inputs into the text generation network, the model effectively focuses on key descriptive regions of images, thereby significantly enhancing performance. On public Chinese image captioning datasets, this model demonstrates substantial advantages in metrics such as BLEU-4 and METEOR.

关键词： Measurement Knowledge engineering Attention mechanisms Computational modeling machine vision Object detection Computer applications Data models Natural language processing Meteors

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Road Condition Detection and Crowdsourced Data Collection for Accident Prevention: A Deep Learning Approach 12

Road Condition Detection and Crowdsourced Data Collection fo...

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12th International Conference on image processing Theory, Tools and applications, IPTA 2023

作者： Jahan, Md Saroar Islam, Mominul Hossain, Md Sanjid Kabir Mim, Jhuma Oussalah, Mourad Akter, Nasrin Oulu Finland Daffodil International University Dept. of Cse Dhaka Bangladesh Lut Univerity Computer Vision and Pattern Recognition Lappeenranta Finland University of Oulu Cmvs Faculty of Itee Oulu Finland

ISBN: (纸本)9798350325416

Bangladesh is one of the countries struggling to prevent road accidents, which is a global cause for concern. An early warning system that indicates road conditions can contribute to the prevention task. For this purpose, a deep-learning based approach using a Convolutional Neural Network (CNN) to learn from random road images the safety factor is developed. This results in a three-class categorization: (i) Severely risky roads, (ii) Mildly risky roads, and (iii) Normal roads. The application of deep learning techniques in this study yields an accuracy of 95.5% in detecting problematic road conditions. Furthermore, based on the study's findings, a mobile application has been developed. The app enables real-time crowdsourced data collection of road conditions and provides a platform for users to share this information in real-time with other drivers, thereby, contributing to prevent accidents and raise awareness among drivers and users by pinpointing the location of the risky road. Finally, crowdsourced data has been reused to update the trained model, which further improves the classifier accuracy. © 2023 IEEE.

关键词： Safety factor

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Automatic extraction of data from infra-red thermometers with computer vision 22

Automatic extraction of data from infra-red thermometers wit...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Marais, Henri Northwest Univ Unit Energy & Technol Syst Fac Engn ZA-2531 Potchefstroom South Africa

ISBN: (纸本)9781713872344

Contactless infrared thermometers (NCITs) have been widely used as a rapid screening tool for patients with fever, particularly during the peak of the Covid-19 pandemic in Africa and other developing countries. The screening information that can be gathered in this manner is difficult to leverage from a pandemic response perspective as common handheld thermometers have no means of automatically exporting measurement data. An algorithm for a mobile phone-based application is developed that is capable of automatically extracting the temperature information from common NCITs by making use of machine vision techniques and partitioning tasks between the screening operator and the mobile application. Temperature readings could accurately be captured from a range of devices with varying text characteristics and backlights in a range of lighting conditions. Additionally the methodology proposed is capable of running on low-end devices without a data connection and without extensive training requirements for different NCITs being required. The use of a low-cost smart-phone as the capturing device together with the involvement of the operator has been show to be a feasible alternative to fully automating processes. Results indicate that similar methods could be used in a range of applications where numerical data needs to be digitised from existing field instrumentation, particularly where such instrumentation feature segmented displays.

关键词： human operator support image processing automatic identification screening system.

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Using of AI Supported Computer vision on UAV’s*

Using of AI Supported Computer Vision on UAV’s*

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Human-Computer Interaction, Optimization and Robotic applications (HORA), International Congress on

作者： Alpaslan Durmuş Abdulhamit Sevgi Ahmet Murat Kadioğlu Erol Duymaz Department of Unmanned Aerial Vehicles and Operatorship Vocational Education School OSTİM Technical University Ankara Turkey Department of Mechatronic Vocational Education School OSTİM Technical University Ankara Turkey Internation Relationship Office OSTİM Technical University Ankara Turkey Ankara Turkey Department of Biomedical Device Tech Vocational Education School OSTİM Technical University Ankara Turkey

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Computer vision (CV) provides computer systems with the ability to perceive, analyze and understand the content of images and videos. Computer vision systems are used in many different areas, from medical diagnosis to industrial quality control. Different technologies and techniques are used to extract relevant information from a large number of images obtained through CV and machine vision (MV) components. The use of artificial intelligence-supported computer vision applications in unmanned aerial vehicles has significantly contributed to the use of UAV systems in different missions. The expansion of the application areas of unmanned aerial vehicles has increased the need for cameras and detection algorithms to be used for diagnosis and detection. Developing UAV systems and artificial intelligence technologies have made significant contributions to the use of UAV systems in defense, search and rescue in disasters, reconnaissance and observation, and also agricultural applications. image processing and artificial intelligence techniques include the capture, digitization and improvement of images step by step with image processing methods. In the study, a road map was created by examining the application of image processing techniques to a fixed-wing unmanned aerial vehicle, the development of its algorithms and the reactions of the unmanned aerial vehicle to this method.

关键词： Training Computer vision Visualization Atmospheric modeling Object detection Autonomous aerial vehicles Real-time systems Artificial intelligence Vehicle dynamics Videos

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Correction of Gridded Precipitation Data Based on machine Learning

Correction of Gridded Precipitation Data Based on Machine Le...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Chengbin Wei Chuandong Zhu Long Wen Wentao Hu School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China Hubei Meteorological Service Center CMA Basin Heavy Rainfall Key Laboratory/Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research Institute of Heavy Rain CMA Wuhan China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Gridded precipitation data play a crucial role in hydrological modeling and climate research, but biases between these data and actual ground observations limit their accuracy and reliability. Correcting these biases is essential to improve the accuracy of gridded precipitation data for practical applications. In this study, we developed the MR-interaction model based on single-day and multi-day gridded precipitation data and compared its performance with five machine learning models to achieve optimal predictive accuracy. By applying the RM-CONS correction method to the gridded data, we significantly reduced accuracy errors, resulting in corrected gridded data that more closely match the observed data distribution.

关键词： Precipitation Accuracy Computational modeling image processing Linear regression machine learning Predictive models Data models Reliability Regression tree analysis

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