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Robust visual-based method and new datasets for ego-lane index estimation in urban environment

machine vision AND applications 2024年第5期35卷 112页

作者： Wang, Dianzheng Liang, Dongyi Li, Shaomiao Chinese Acad Sci Inst Engn Thermophys Beijing 100190 Peoples R China HAOMO AI Technol Co Ltd Beijing 100192 Peoples R China

Correct and robust ego-lane index estimation is crucial for autonomous driving in the absence of high-definition maps, especially in urban environments. Previous ego-lane index estimation approaches rely on feature extraction, which limits the robustness. To overcome these shortages, this study proposes a robust ego-lane index estimation framework upon only the original visual image. After optimization of the processing route, the raw image was randomly cropped in the height direction and then input into a double supervised LaneLoc network to obtain the index estimations and confidences. A post-process was also proposed to achieve the global ego-lane index from the estimated left and right indexes with the total lane number. To evaluate our proposed method, we manually annotated the ego-lane index of public datasets which can work as an ego-lane index estimation baseline for the first time. The proposed algorithm achieved 96.48/95.40% (precision/recall) on the CULane dataset and 99.45/99.49% (precision/recall) on the TuSimple dataset, demonstrating the effectiveness and efficiency of lane localization in diverse driving environments. The code and dataset annotation results will be exposed publicly on https://***/haomo-ai/LaneLoc.

关键词： Ego-lane index estimation Visual image Dataset Double supervision Urban environments Autonomous driving

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Edge deployed satellite image classification with TinEViT a X-Cube-AI compatible efficient vision transformer

Edge deployed satellite image classification with TinEViT a ...

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Conference on Real-Time image processing and Deep Learning

作者： Halford, Gavin Depoian, Arthur C., ii Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673878;9781510673861

Lower resolutions and a lack of distinguishing features in large satellite imagery datasets make identification tasks challenging for traditional image classification models. vision Transformers (ViT) address these issues by creating deeper spatial relationships between image features. Self attention mechanisms are applied to better understand not only what features correspond to which classification profile, but how the features correspond to each other within each separate category. These models, integral to computer vision machine learning systems, depend on extensive datasets and rigorous training to develop highly accurate yet computationally demanding systems. Deploying such models in the field can present significant challenges on resource constrained devices. This paper introduces a novel approach to address these constraints by optimizing an efficient vision Transformer (TinEVit) for real-time satellite image classification that is compatible with ST Microelectronics AI integration tool, X-Cube-AI.

关键词： ViT STM-32 X-Cube-AI Satellite image Classification Efficient AI EuroSAT

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

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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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GMDIC: a digital image correlation measurement method based on global matching for large deformation displacement fields

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND vision 2024年第11期41卷 2263-2276页

作者： Wang, Linlin Shao, Jing Wang, ZhuJun Gao, Qian Wang, ChuanYun Yan, Zhuo Li, ZhongYi Zhang, Tong ShenYang Aerosp Univ Coll Artificial Intelligence Shenyang 110136 Liaoning Peoples R China ShenYang Aerosp Univ Sch Automat Shenyang 110136 Liaoning Peoples R China

The digital image correlation method is a non-contact optical measurement method, which has the advantages of full-field measurement, simple operation, and high measurement accuracy. The traditional DIG method can accurately measure displacement and strain fields, but there are still many limitations. (i) In the measurement of large displacement deformations, the calculation accuracy of the displacement field and strain field needs to be improved due to the unreasonable setting of parameters such as subset size and step size. (ii) It is difficult to avoid under-matching or over-matching when reconstructing smooth displacement or strain fields. (iii) When processing large-scale image data, the computational complexity will be very high, resulting in slow processing speeds. In recent years, deep-learning-based DIG has shown promising capabilities in addressing the aforementioned issues. We propose a new, to the best of our knowledge, DIG method based on deep learning, which is designed for measuring displacement fields of speckle images in complex large deformations. The network combines the multi-head attention Swin-Transformer and the high-efficient channel attention module EGA and adds positional information to the features to enhance feature representation capabilities. To train the model, we constructed a displacement field dataset that conformed to the real situation and contained various types of speckle images and complex deformations. The measurement results indicate that our model achieves consistent displacement prediction accuracy with traditional DIG methods in practical experiments. Moreover, our model outperforms traditional DIG methods in cases of large displacement scenarios. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Computational imaging Deep learning Imaging techniques machine vision Neural networks Speckle imaging

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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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image Generation from Arabic Text: Comparative Study of Proposed Architectures 8th

Image Generation from Arabic Text: Comparative Study of Prop...

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8th International Conference on Arabic Language processing

作者： Bahani, Mourad El Ouaazizi, Aziza Maalmi, Khalil Essahlaoui, Abdelouahed Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Artificial Intelligence Data Sci & Emerging Syst Fes Morocco Sidi Mohamed Ben Abdellah Univ Lab Engn Sci FPT Taza Morocco

ISBN: (纸本)9783031804373;9783031804380

Text-to-image generation is a cutting-edge technology that enables computers to generate images from textual descriptions. While this technology has been extensively researched and applied to English language text, applying it to Arabic language text is still in its early stages. Additionally, the Arabic language is challenging due to its right-to-left writing system and extensive vocabulary of 1.3 million words. In this paper, we explore text-to-image generation for generating images from Arabic language text descriptions. Firstly, we fine-tune a transformer-based model pre-trained on the Arabic text to transform the text information into affine transformation within the DF-GAN generator. Secondly, we present a text transformer that combines LSTM layers to address the limitation of unrecognized words. Thirdly, a mask predictor is trained into the generator using a weakly supervised method and incorporated into the affine transformation for a more effective integration of image and text features. In addition, we add the DAMSM loss function as a regularization to the loss function to achieve convergences and stability in the training phase. The experiment on two challenging datasets CUB and Oxford-flower shows that our architectures can accurately generate high-quality images faithfully representing the Arabic textual descriptions. We believe the scaling of this task could have critical applications in fields such as Arabic visual learning, e-commerce, advertising, and entertainment.

关键词： machine Learning Deep Learning Computer vision Generative Adversarial Networks Text-to-image Generation Arabic Text processing

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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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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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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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