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TGGLinesPlus: A Robust Topological Graph-Guided Computer vision Algorithm for Line Detection From images

TRANSACTIONS IN GIS 2025年第1期29卷

作者： Yang, Liping Driscol, Joshua Gong, Ming Slack, Katie Zhang, Wenbin Wang, Shujie Potts, Catherine G. Univ New Mexico Dept Geog & Environm Studies GeoAIR Lab Albuquerque NM 87106 USA Univ New Mexico Ctr Adv Spatial Informat Res & Educ ASPIRE Albuquerque NM 87106 USA Univ New Mexico Dept Comp Sci Albuquerque NM 87106 USA Woodwell Climate Res Ctr Falmouth MA USA Univ Dayton Dept Elect & Comp Engn Dayton OH USA Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL USA Penn State Univ Dept Geog University Pk PA USA Penn State Univ Earth & Environm Syst Inst University Pk PA USA D Wave Quantum Inc Burnaby BC Canada

Line detection is a classic and essential problem in image processing, computer vision, and machine intelligence. Line detection has many important applications, including image vectorization (e.g., document recognition and art design), indoor mapping, and important societal challenges (e.g., sea ice fracture line extraction from satellite imagery). Many line detection algorithms and methods have been developed, but robust and intuitive methods are still lacking. In this paper, we proposed and implemented a topological graph-guided algorithm, named TGGLinesPlus, for line detection. Our experiments on images from a wide range of domains have demonstrated the flexibility of our TGGLinesPlus algorithm. We benchmarked our algorithm with five classic and state-of-the-art line detection methods and evaluated the benchmark results qualitatively and quantitatively, the results demonstrate the robustness of TGGLinesPlus.

关键词： algorithms computational geometry computer vision edge and feature detection graph theory image processing line detection spatial sciences topological graph

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Accelerating Brillouin fiber sensing via destructive-interference-enabled precise raw data acquisition and nonredundant image denoising

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OPTICA 2025年第2期12卷 216-227页

作者： Li, Zonglei Zhou, Yin Hu, Jianqi Yao, Jianping Yan, Lianshan Southwest Jiaotong Univ Ctr Informat Photon & Commun Sch Informat Sci & Technol Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Lab Intelligent Percept & Smart Operat & Maintenan Chengdu 610031 Sichuan Peoples R China Sorbonne Univ Coll France CNRS Lab Kastler BrosselENS Univ PSL 24 Rue Lhomond F-75005 Paris France Univ Ottawa Sch Elect Engn & Comp Sci Microwave Photon Res Lab 25 Templeton St Ottawa ON K1N 6N5 Canada

Distributed Brillouin fiber sensing, based on the linear relationship between Brillouin frequency shift (BFS) and physical quantities applied to sensing fibers, has found numerous applications in the past few decades. Recently, various advanced image denoising methods have been used for performance enhancements in Brillouin fiber sensors. Yet, even though these methods do significantly remove noises contained in raw data, the BFS measurement uncertainty is not reduced-the newly introduced image denoising appears redundant with the conventional signal processing. Here, in order to truly make Brillouin fiber sensing benefit from image denoising, we directly map BFS from the image-denoised data via the slope-assisted analysis of the Brillouin phase-gain ratio. As such, noise reduction resulting from image denoising fully translates into measurement uncertainty reduction. In order to further optimize the performance of image-denoising-enhanced Brillouin fiber sensing, we improve the quality of the raw Brillouin gain and phase data by designing an advanced coherent detection scheme called a microwave-photonic interferometer, which converts some amplitude and phase noises into common-mode noises and further eliminates them through destructive interference. A more than 20-fold sensing speed acceleration compared to the state-of-the-art is experimentally achieved. This remarkable performance enhancement is obtained by only optimizing the signal detection and processing unit, without modifying Brillouin scattering between pump and probe waves. Our method seamlessly connects Brillouin fiber sensing with advanced image denoising methods developed for computer vision and artificial intelligence, and makes imagedenoising-enhanced Brillouin fiber sensing outperform the state-of-the art significantly. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Brillouin scattering Fiber optic sensors Heterodyne detection machine vision Phase noise Raman scattering

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Optimized vision transformer encoder with cnn for automatic psoriasis disease detection

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MULTIMEDIA TOOLS AND applications 2023年第21期83卷 59597-59616页

作者： vishwakarma, Gagan Nandanwar, Amit Kumar Thakur, Ghanshyam Singh Indian Inst Informat Technol Comp Sci & Engn Bhopal Madhya Pradesh India Maulana Azad Natl Inst Technol Comp Sci Engn Bhopal MP India Maulana Azad Natl Inst Technol Dept Comp Applicat Bhopal MP India

Psoriasis is a skin disorder that results in swollen skin cells and red, itchy areas on the skin. 40% of the world's population is currently affected by psoriasis. Nowadays, using skin image analysis technology is the main way for detecting psoriasis. Additionally, a number of academics have identified potential machine learning methods for categorising the psoriasis illness. However, the accuracy and computational efficiency of the model still need to be improved. Thus, in this paper, we present an optimized vision transformer for autonomous psoriasis disease detection. Following pre-processing, feature optimized image is attained using convolutional neural network (CNN) which embeds full image and concatenates to each vision transformer encoder layer. It leads the network to always "retain" the full image at the end of each transformer block output. In parallel, the pre-processed images are cropped into patches and these patches along with its positional encoded information are given as input to the optimized transformer encoder. To enhance the performance of transformer, the hyper-parameters of it are optimized using adaptive rabbit optimization algorithm (AROA). Results of this article confirm that the proposed optimized vision transformer model achieved better classification accuracy of 97.7% and F-Score of 96.5%.

关键词： CNN vision transformer AROA Psoriasis

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Quantum visual Computing

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IEEE COMPUTER GRAPHICS AND applications 2024年第5期44卷 10-13页

作者： Mueller-Roemer, Johannes S. Golyanik, vladislav Birdal, Tolga Fraunhofer IGD D-64283 Darmstadt Germany Tech Univ Darmstadt D-64283 Darmstadt Germany Max Planck Inst Informat D-66123 Saarbrucken Germany Imperial Coll London London SW7 2AZ England

Quantum computing is emerging as a transformative force in computer science, offering significant advantages in speed and efficiency over classical computing methods. Despite this promise, the practical application of quantum computing to visual computing faces numerous challenges, including the complexity of quantum algorithms and the limitations of current quantum hardware. These challenges underscore the necessity for focused research and collaboration in this interdisciplinary area. This Special Issue of IEEE Computer Graphics and applications, "Quantum visual Computing," aims at drawing attention to these challenges and bringing together pioneering research at the intersection of quantum and visual computing. By fostering dialogue and innovation between these fields, we hope to inspire new solutions and advance the state of the art in both domains.

关键词： machine Learning image processing Computer vision Computer Science Quantum Computing Interactive Tool Computer Graphics Computer applications Advantages Of Speed Use Of Registers Quantum Algorithms Interdisciplinary Area Current Hardware Quantum applications Quantum machine image Segmentation Software Development Articles In Issue Quantum State Game Development Quantum Phenomena applications In Quantum Computing

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image Forgery Detection Using machine Learning: Exploring image Quality Assessment and Sharpness estimation 13

Image Forgery Detection Using Machine Learning: Exploring Im...

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13th International Conference on image processing Theory Tools and applications

作者： Zeglazi, Oussama Mohtaram, Noureddine Mohammed V Univ Rabat Fac Sci LRIT Rabat Morocco

ISBN: (纸本)9798331541859;9798331541842

image forgery techniques pose a significant challenge in the digital era, particularly when it comes to detecting copy-move forgery, which is crucial for ensuring the credibility of digital evidence. This paper introduces an innovative algorithm that uses the image quality assessment metric and sharpness estimation extracted from images to uncover tampering artifacts, such as irregular tampered boundaries and discrepancies in noise patterns between original and manipulated areas. Additionally, the paper utilizes the XGBoost model to effectively distinguish between original and tampered regions, enhancing the accuracy of forgery detection. The proposed approach is evaluated on the MICC-F2000 dataset, consisting of 2000 images, with 1300 original and 700 forged ones. The experimental results, achieving an impressive accuracy of 97.50%, underscore the potential merits of our approach in advancing the domain of image forgery detection.

关键词： image forgery detection MICC-F2000 BRISQUE Sharpness machine learning

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FAFNet: Fully aligned fusion network for RGBD semantic segmentation based on hierarchical semantic flows

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IET image processing 2023年第1期17卷 32-41页

作者： Chen, Jiazhou Zhan, Yangfan Xu, Yanghui Pan, Xiang Zhejiang Univ Technol Sch Comp Sci & Technol 288 Liuhe Rd Hangzhou Peoples R China

Depth maps are acquirable and irreplaceable geometric information that significantly enhances traditional color images. RGB and Depth (RGBD) images have been widely used in various image analysis applications, but they are still very limited due to challenges from different modalities and misalignment between color and depth. In this paper, a Fully Aligned Fusion Network (FAFNet) for RGBD semantic segmentation is presented. To improve cross-modality fusion, a new RGBD fusion block is proposed, features from color images and depth maps are first fused by an attention cross fusion module and then aligned by a semantic flow. A multi-layer structure is also designed to hierarchically utilize the RGBD fusion block, which not only eases issues of low resolution and noises for depth maps but also reduces the loss of semantic features in the upsampling process. Quantitative and qualitative evaluations on both the NYU-Depth v2 and the SUN RGB-D dataset demonstrate that the FAFNet model outperforms state-of-the-art RGBD semantic segmentation methods.

关键词： cross-modality fusion image segmentation multilayers FAFNet model image enhancement hierarchical semantic flows attention cross fusion module geometric information image sampling semantic networks RGBD semantic segmentation methods NYU-Depth v2 dataset Knowledge representation color image analysis applications image fusion Optical, image and video signal processing Computer vision and image processing techniques semantic flow features SUN RGB-D dataset fully aligned fusion network multilayer structure image resolution image colour analysis RGBD fusion block

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machine Learning applications: From Computer vision to Robotics

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2024年

作者： Chatterjee Indranath

ISBN: (数字)9781394173341;9781394173334

ISBN: (纸本)9781394173327

machine Learning applications Practical resource on the importance of machine Learning and Deep Learning applications in various technologies and real-world situations machine Learning applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader’s active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective. Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, machine Learning applications includes information on:

Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing
Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules
AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change
Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records

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Recognition and monitoring of gas leakage using infrared imaging technique with machine learning

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MULTIMEDIA TOOLS AND applications 2023年第12期83卷 35413页

作者： Shirley, C. P. Raja, J. Immanuel John Sonia, S. v. Evangelin Titus, I. Karunya Inst Technol & Sci Coimbatore India

Gas leakage in the domestic sector leads to numerous dangerous hazards. The earlier prediction is one of the safety measures to prevent various consequences. The proposed system helps in the earlier detection of gas leakage using artificial intelligence techniques. This involves machine learning with infrared imaging techniques. machine learning is the process of teaching machines to do tasks automatically by analysing and testing data. The obtained data are processed using image processing techniques. The image processing technique is used to extract information from the images involving various stages such as image enhancement and image analysis. The initial data are obtained in the form of images using infrared imaging techniques. It is the technique that utilizes the infrared portion of the electromagnetic spectrum to obtain the desired images. The obtained images are processed to obtain clear images in the dataset. The data is then tested and taught using machine learning evolving optimization techniques on the data. This helps in the accurate detection of gas leakage. To compare, the individual models' test accuracy ranged from 99.8% (based on Gas Sensor data using Random Forest) with the training accuracy of 99.8%. Experimental results demonstrate its ability to automatically detect and display gas leaks in high quality by establishing a background model, segmenting the gas-leak zone with motion characteristics, and rendering the gas-leak region in colour using grayscale mapping.

关键词： Gas leakage Artificial intelligence Infrared imaging techniques machine learning image processing

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vision based leather defect detection: a survey

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MULTIMEDIA TOOLS AND applications 2023年第1期82卷 989-1015页

作者： Jawahar, Malathy Anbarasi, L. Jani Geetha, S. CSIR Cent Leather Res Inst Leather Proc Technol Div Chennai 600020 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

Increasing consumer quality awareness and increase in consumer wealth drives the market demand for high quality leather and leather products. Reliable and effective detection and classification of leather surface defects is of profound significance to tanneries and industries where leather is a major raw material for leather accessories and leather parts manufacturers. This paper presents a methodical and a detailed review of the leather surface defects detection methods starting from leather image acquisition, leather image processing, feature extraction and classification for defect detection. Firstly, we introduce the fundamentals of leather image acquisition and various related image processing methods, feature extraction and classification for the defect inspection. Next, the existing datasets and summary of the recent methodologies used in this field are discussed. Finally, the challenges and suggested improvements to further the development of the application of advanced machine Learning and Deep Learning in this field are discussed. Deep learning algorithms are shown to have a great potential for leather surface defect detection and can help prepare a robust system that would greatly guarantee quality leather and provide monetary wealth from such leather products. Finally, research guidelines are presented to fellow researchers regarding data augmentation, leather defect detection models which need to be investigated in the future to make progress in this crucial area of research.

关键词： Leather defect detection vision based approach image capture Stages in leather processing Leather quality classification

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An optimized deep learning approach to detect and classify defective tiles in production line for efficient industrial quality control

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NEURAL COMPUTING & applications 2023年第15期35卷 11089-11108页

作者： Kovilpillai, J. Judeson Antony Jayanthy, S. Sri Ramakrishna Engn Coll Dept Elect & Commun Coimbatore Tamil Nadu India

In this paper, a deep learning-based machine vision approach is proposed to automatically detect and classify defective tiles in the production assembly line of a tile manufacturing industry. The deep learning model used in this methodology is trained with 30,000 real-time images of cement/ceramic tiles, and the features of the image samples are extracted using the convolutional layers in the model. The defective tiles are identified and classified using an optimized activation function that acts as the decision-making layer or output layer of the deep learning model. The performance of this deep learning technique is evaluated using various metrics like accuracy, precision, recall and f1-score which is further compared with state-of-the-art activation functions like Relu, sigmoid, tanh and softmax. To further enhance the performance metrics, the feature extraction is done using various pre-trained models like vGG-16, Resnet50 and Inceptionv3 and was further evaluated using metrics like K (Kappa statistic), OA (overall accuracy) and AA (average accuracy). The obtained experimental results with an accuracy of 99.96% under a favorable learning rate prove the robustness and efficiency of the proposed methodology to enhance industrial quality control in any tile manufacturing industry.

关键词： Deep learning machine vision image processing Industry 4.0 Industry quality control vision computing

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