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Retinal fundus image enhancement using an ensemble framework for accurate glaucoma detection

Neural Computing and applications 2024年 1-19页

作者： Lenka, Satyabrata Mayaluri, Zefree Lazarus Panda, Ganapati Department of Electrical Engineering C.V. Raman Global University Odisha Bhubaneswar India C.V. Raman Global University Odisha Bhubaneswar India

Retinal fundus imaging plays a crucial role in the diagnosis of ophthalmic diseases such as glaucoma, a significant cause of vision loss worldwide. Accurate detection of glaucoma using image processing, machine learning, and deep learning approaches depends on the effectiveness with which the retinal fundus images are captured. Poor-quality images with artifacts, including uneven illumination, blur, and color distortion, can lead to incorrect diagnoses. In this work, we propose an end-to-end glaucoma detection model based on the ensemble of image enhancement networks, segmentation networks, and image classification networks. The proposed approach consists of an improved version of generative adversarial network (GAN) called the cycle consistency GAN (cycle-GAN) for image quality enhancement, U-Net for optic cup and optic disc segmentation, and support vector machine for image classification. The cycle-GAN model uses autoencoders as generators and a deep convolutional neural network (CNN) as discriminators to generate high-quality fundus images. The cup-to-disc ratio, a popular feature, is utilized to categorize fundus images as either glaucomatous or non-glaucomatous. We use six imbalanced datasets for experimental analysis of the proposed ensemble model, including ORIGA, ACRIMA, DRISTI-GS, REFUGE, Messidor, and Mendeley. The experimental findings demonstrate that the proposed ensemble model works better than individual models such as GAN, Autoencoder, deep CNN, and also from existing methods. The proposed method not only reduces the artifacts from fundus images but also solves the problem of imbalanced datasets for accurate glaucoma detection. The experimental results show maximum accuracy, precision, recall, and F-measure values of 0.968, 0.821, 0.974, and 0.891, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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Hybrid features extraction for the online mineral grades determination in the flotation froth using Deep Learning

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ENGINEERING applications OF ARTIFICIAL INTELLIGENCE 2024年 129卷

作者： Bendaouia, Ahmed Abdelwahed, El Hassan Qassimi, Sara Boussetta, Abdelmalek Benzakour, Intissar Benhayoun, Abderrahmane Amar, Oumkeltoum Bourzeix, Francois Baina, Karim Cherkaoui, Mouhamed Hasidi, Oussama Cadi Ayyad Univ Fac Sci Semlalia Comp Sci Dept Comp Syst Engn Lab LISI Marrakech Morocco Moroccan Fdn Adv Sci Innovat & Res UM6P Rabat Morocco R&D & Engn Ctr Managem Grp Reminex Marrakech Morocco Cadi Ayyad Univ Fac Sci & Technol Comp & Syst Engn Lab L2IS Marrakech Morocco Mohammed V Univ Ecole Natl Super Informat Anal Syst ENSIAS Rabat 10000 Morocco ENSMR Engn Sch Rabat Morocco

The control of the froth flotation process in the mineral industry is a challenging task due to its multiple impacting parameters. Accurate and convenient examination of the concentrate grade is a crucial step in realizing effective and real-time control of the flotation process. The goal of this study is to employ image processing techniques and CNN-based features extraction combined with machine learning and deep learning to predict the elemental composition of minerals in the flotation froth. A real world dataset has been collected and preprocessed from a differential flotation circuit at the industrial flotation site based in Guemassa, Morocco. Using image-processing algorithms, the extracted features from the flotation froth include: the texture, the bubble size, the velocity and the color distribution. To predict the mineral concentrate grades, our study includes several supervised machine learning algorithms (ML), artificial neural networks (ANN) and convolutional neural networks (CNN). The industrial experimental evaluations revealed relevant performances with an accuracy up to 0.94. Furthermore, our proposed Hybrid method was evaluated in a real flotation process for the Zn, Pb, Fe and Cu concentrate grades, with an error of precision lesser than 4.53. These results demonstrate the significant potential of our proposed online analyzer as an artificial intelligence application in the field of complex polymetallic flotation circuits (Pb, Fe, Cu, Zn).

关键词： machine learning Deep learning Computer vision Features extraction Mining industry Industry 4.0 Flotation

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Oppositional Harris Hawks Optimization with Deep Learning-Based image Captioning

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Computer Systems Science & Engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of Computer vision(CV)and Natural Language processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： image captioning natural language processing artificial intelligence machine learning deep learning

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Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments

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Artificial Intelligence in Agriculture 2024年第3期13卷 84-99页

作者： Ranjan Sapkota Dawood Ahmed Manoj Karkee Center for Precision&Automated Agricultural Systems Washington State University24106 N Bunn RdProsser99350 WashingtonUSA

Instance segmentation,an important image processing operation for automation in agriculture,is used to precisely delineate individual objects of interestwithin images,which provides foundational information for various automated or robotic tasks such as selective harvesting and precision *** study compares the one-stage YOLOv8 and the two-stage Mask R-CNN machine learning models for instance segmentation under varying orchard conditions across two *** 1,collected in dormant season,includes images of dormant apple trees,which were used to train multi-object segmentation models delineating tree branches and *** 2,collected in the early growing season,includes images of apple tree canopies with green foliage and immature(green)apples(also called fruitlet),which were used to train single-object segmentation models delineating only immature green *** results showed that YOLOv8 performed better than Mask R-CNN,achieving good precision and near-perfect recall across both datasets at a confidence threshold of ***,for Dataset 1,YOLOv8 achieved a precision of 0.90 and a recall of 0.95 for all *** comparison,Mask R-CNN demonstrated a precision of 0.81 and a recall of 0.81 for the *** Dataset 2,YOLOv8 achieved a precision of 0.93 and a recall of *** R-CNN,in this single-class scenario,achieved a precision of 0.85 and a recall of ***,the inference times for YOLOv8 were 10.9 ms for multi-class segmentation(Dataset 1)and 7.8 ms for single-class segmentation(Dataset 2),compared to 15.6 ms and 12.8 ms achieved by Mask R-CNN's,*** findings showYOLOv8's superior accuracy and efficiency in machine learning applications compared to two-stage models,specifically Mask-R-CNN,which suggests its suitability in developing smart and automated orchard operations,particularly when real-time applications are necessary in such cases as robotic harvesting and robotic immature green fruit thin

关键词： YOLOv8 Mask R-CNN Deep learning machine learning Automation Robotics Artificial intelligence machine vision

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CLIPSONIC: TEXT-TO-AUDIO SYNTHESIS WITH UNLABELED VIDEOS AND PRETRAINED LANGUAGE-vision MODELS

CLIPSONIC: TEXT-TO-AUDIO SYNTHESIS WITH UNLABELED VIDEOS AND...

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IEEE Workshop on applications of Signal processing to Audio and Acoustics (WASPAA)

作者： Dong, Hao-Wen Liu, Xiaoyu Pons, Jordi Bhattacharya, Gautam Pascual, Santiago Serra, Joan Berg-Kirkpatrick, Taylor McAuley, Julian Dolby Labs San Francisco CA 94103 USA Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9798350323726

Recent work has studied text-to-audio synthesis using large amounts of paired text-audio data. However, audio recordings with high-quality text annotations can be difficult to acquire. In this work, we approach text-to-audio synthesis using unlabeled videos and pretrained language-vision models. We propose to learn the desired text-audio correspondence by leveraging the visual modality as a bridge. We train a conditional diffusion model to generate the audio track of a video, given a video frame encoded by a pretrained contrastive language-image pretraining (CLIP) model. At test time, we first explore performing a zero-shot modality transfer and condition the diffusion model with a CLIP-encoded text query. However, we observe a noticeable performance drop with respect to image queries. To close this gap, we further adopt a pretrained diffusion prior model to generate a CLIP image embedding given a CLIP text embedding. Our results show the effectiveness of the proposed method, and that the pretrained diffusion prior can reduce the modality transfer gap. While we focus on text-to-audio synthesis, the proposed model can also generate audio from image queries, and it shows competitive performance against a state-of-the-art image-to-audio synthesis model in a subjective listening test. This study offers a new direction of approaching text-to-audio synthesis that leverages the naturally-occurring audio-visual correspondence in videos and the power of pretrained language-vision models.

关键词： Sound synthesis audio generation multimodal learning diffusion models neural networks machine learning

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Development of an Independent Adversarial Sample Detection Model, Based on image Features 5th

Development of an Independent Adversarial Sample Detection M...

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5th International Conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Xu, Long Beijing University of Aeronautics and Astronautics Beijing China

ISBN: (纸本)9789819751808

Independent adversarial sample detection is an important problem in the field of computer vision and machine learning, especially in the context of the widespread use of deep learning models. This can lead to misclassification and performance degradation of the model, so adversarial sample detection is crucial to ensure the reliability of the model. This research focuses on the development of an independent adversarial sample detection model based on image features. A new approach is proposed which does not rely on the original model but focuses on detecting adversarial features in the samples. The effectiveness and robustness of the proposed method is verified in extensive experiments. The model is able to detect independent adversarial samples with high accuracy, regardless of whether the adversarial samples are targeted at a specific deep learning model or not. In addition, the method demonstrates excellent performance on a variety of image datasets and applications in different domains. It is expected to enhance the robustness and reliability of deep learning models, and thus better cope with adversarial sample attacks in practical applications. This approach also has a wide range of applications for a variety of computer vision tasks and domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Swin on Axes: Extending Swin Transformers to Quadtree image Representations

Swin on Axes: Extending Swin Transformers to Quadtree Image ...

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IEEE/CVF Winter Conference on applications of Computer vision (WACV)

作者： Oliu, Marc Nasrollahi, Kamal Escalera, Sergio Moeslund, Thomas B. Aalborg Univ Fredrik Bajers Vej 7KOst Aalborg Denmark Milestone Syst Banemarksvej 50 Brondby Denmark Univ Barcelona Gran Via Corts Catalanes 585 Barcelona Spain Comp Vis Ctr Campus UABEdifici O Cerdanyola Del Valles Spain

ISBN: (纸本)9798350370287;9798350370713

In recent years, Transformer models have revolutionized machine learning. While this has resulted in impressive results in the field of Natural Language processing, Computer vision quickly stumbled upon computation and memory problems due to the high resolution and dimensionality of the input data. This is particularly true for video, where the number of tokens increases cubically relative to the frame and temporal resolutions. A first approach to solve this was vision Transformers, which introduce a partitioning of the input into embedded grid cells, lowering the effective resolution. More recently, Swin Transformers introduced a hierarchical scheme that brought the concepts of pooling and locality to transformers in exchange for much lower computational and memory costs. This work proposes a reformulation of the latter that views Swin Transformers as regular Transformers applied over a quadtree representation of the input, intrinsically providing a wider range of design choices for the attentional mechanism. Compared to similar approaches such as Swin and MaxViT, our method works on the full range of scales while using a single attentional mechanism, allowing us to simultaneously take into account both dense short range and sparse long range dependencies with low computational overhead and without introducing additional sequential operations, thus making full use of GPU parallelism.

关键词： Natural language processing systems

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The applications of machine vision in Raw Material and Production of Wood Products

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BIORESOURCES 2022年第3期17卷 5532-5556页

作者： Wang, Qi Zhan, Xianxu Wu, Zhihui Liu, Xinyou Feng, Xinhao Nanjing Forestry Univ Coll Furnishings & Ind Design Nanjing 210037 Peoples R China Jiangsu Coinnovat Ctr Efficient Proc & Utilizat Nanjing 210037 Jiangsu Peoples R China Dehua Tubao New Decorat Mat Co Ltd Deqing 313200 Peoples R China

machine vision has been developed nearly for 70 years and been widely applied in electronics, automotive manufacturing, food processing, etc. With deepening study of its theory and technology in forestry industry, the industry of wood products is moving steadily toward the goal of automated identification and production to improve the manufacturing intelligence of enterprises. In this study, theoretical and algorithmic research on image acquisition, feature extraction, recognition, and classification involved in machine vision- based wood recognition technology were analyzed on the basis of its global development. The applications of machine vision in the wood materials, such as the identification of tree species, wood inspection and classification, defects detection of wood product, surface analysis of wood color, and quality control of furnishing products were thoroughly analyzed. The development trend of machine vision in the production and management of wood materials was considered in the current development of wood and furnishing enterprises. These results lay a solid foundation for wood science research, and intelligent manufacture of wooden furniture, and efficient development of greener and cleaner production of the furniture industry, which could improve the environmental effect of the wood products and furniture and make a great contribution for the carbon goal of "30-60" in China.

关键词： machine vision Forestry industry Wood products Defects detection Quality control

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Deep Learning-Based Framework for Power Converter Circuit Identification and Analysis

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IEEE ACCESS 2024年 12卷 115356-115369页

作者： Bohara, Bharat Sarma Krishnamoorthy, Harish Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA

This paper introduces a deep learning-based framework for identifying hand-drawn schematics of power converter circuits and performing automated simulations. The framework employs cutting-edge computer vision-based object detection models, such as YOLOv8, to achieve a high mean average precision (mAP) of 96.7% to accurately identify components. Wire tracing and connectivity are achieved through a combined architecture built upon classical image processing techniques and deep learning approaches. Detailed information extracted from a hand-drawn circuit schematic is used to automatically create its netlist for automated simulation through the spice engine. The proposed framework is successfully tested on various nonisolated (buck, boost) and isolated (flyback, full-bridge) converters under both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) operations. In the comprehensive assessment of the entire framework, its efficacy is tested on 140 newly drawn circuit diagrams. The overall accuracy in the generation of netlists reaches a high value of 95.71%, utilizing the robust component detection capabilities of YOLOv8. Moreover, the framework enables the generation of both graphical representations and adjacency matrices for circuit diagrams. This output serves as a valuable dataset generator, contributing to the rapidly advancing domains of machine learning, including graph neural networks and geometric learning, particularly in the application space of power and energy systems. This framework can be further employed as an educational tool, and the ideas introduced can be developed to generate fully automated and efficient power converter designs for real-world applications.

关键词： Integrated circuit modeling Computational modeling Feature extraction Computer architecture Accuracy Optical character recognition Numerical models Computer vision Deep learning Automated circuit simulation computer vision deep learning hand-drawn circuit diagram NetList spice power converter automated graph generation

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Rolling Shutter Camera:Modeling,Optimization and Learning

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machine Intelligence Research 2023年第6期20卷 783-798页

作者： Bin Fan Yuchao Dai Mingyi He School of Electronics and Information Northwestern Polytechnical UniversityXi’an710129China

Most modern consumer-grade cameras are often equipped with a rolling shutter mechanism,which is becoming increasingly important in computer vision,robotics and autonomous driving ***,its temporal-dynamic imaging nature leads to the rolling shutter effect that manifests as geometric *** the years,researchers have made significant progress in developing tractable rolling shutter models,optimization methods,and learning approaches,aiming to remove geometry distortion and improve visual *** this survey,we review the recent advances in rolling shutter cameras from two aspects of motion modeling and deep *** the best of our knowledge,this is the first comprehensive survey of rolling shutter *** the part of rolling shutter motion modeling and optimization,the principles of various rolling shutter motion models are elaborated and their typical applications are ***,the applications of deep learning in rolling shutter based image processing are ***,we conclude this survey with discussions on future research directions.

关键词： Rolling shutter motion modeling image correction temporal super-resolution deep learning

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