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8th International Conference on Computer vision and image processing (CVIP)

作者： Sonti, Movya Kokil, Priyanka Indian Inst Informat Technol Design & Mfg Dept Elect & Commun Engn Adv Signal & Image Proc ASIP Lab Chennai 600127 Tamil Nadu India

ISBN: (纸本)9783031581731;9783031581748

Artificial intelligence has taken healthcare a step forward by providing quick diagnosis and treatment recommendations. The traditional machine-learning approach requires massive data to train the model for better diagnosis. But, due to policy regulations, medical data is always guarded by the barricades of the law, making data accessibility difficult for researchers. To address this issue, a data-decentralized collaborative framework known as federated learning is adopted that reaps the benefits of huge private data without aggregating it into a single common store. A pre-trained model is employed to diagnose age-related macular degeneration and performance of the proposed framework is compared with other two model architectures, namely, MobileNet and InceptionV3. To investigate the effectiveness of the proposed framework, a comparison is made with a data-centralized learning approach. Using the MobileNet model, the federated and centralized frameworks have achieved an accuracy of 95% and 92%, respectively. These findings encourage clinicians around the globe to utilize wealthy private data without violating privacy laws using federated learning to build a powerful model for classifying any disorders while maintaining data privacy.

关键词： Federated learning Privacy Data-security Data-decentralization Pre-trained models Age-related macular degeneration

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Increased Leverage of Transprecision Computing for machine vision applications at the Edge

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JOURNAL OF SIGNAL processing SYSTEMS FOR SIGNAL image AND VIDEO TECHNOLOGY 2022年第10期94卷 1101-1118页

作者： Minhas, Umar Ibrahim Lee, JunKyu Mukhanov, Lev Karakonstantis, Georgios Vandierendonck, Hans Woods, Roger Queens Univ Belfast Belfast Antrim North Ireland Queens Univ Belfast Inst Elect Commun & Informat Technol Belfast Antrim North Ireland Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland Queens Univ Belfast Sch Elect Elect Engn & Comp Sci High Performance & Data Intens Comp Belfast Antrim North Ireland Queens Univ Belfast Inst Elect Commun & Informat Technol Ctr Data Sci & Scalable Comp Belfast Antrim North Ireland

The practical deployment of machine vision presents particular challenges for resource constrained edge devices. With a clear need to execute multiple tasks with variable workloads, there is a need for a robust approach that can dynamically adapt at runtime and which can maintain the maximum quality of service (QoS) within the available resource constraints. A lightweight approach that monitors the runtime workload constraints and leverages accuracy-throughput trade-offs on a graphics processing unit (GPU), is presented. It includes optimisation techniques that identify the configurations for each task in terms of optimal accuracy, energy and memory and management of the transparent switching between configurations. Using a neural network architecture search that statically generates a range of implementations that target a resource-precision trade-off, we explore the detection of the optimal parameters for the required QoS under specific memory and energy constraints. For an accuracy loss of 1%, we demonstrate that a 1.6x higher frame processing rate can be achieved on GPU with further improvements possible at further relaxed accuracy. In order to further improve the switching between configurations, we enhance the proposed mechanism by employing central processing units (CPUs) for offloading some of the executed frames, which helps to improve the frame rate by further 0.9%.

关键词： Edge Computing Approximate Computing Transprecision Computing machine vision

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Application of deep learning approaches for classification of diabetic retinopathy stages from fundus retinal images: a survey

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MULTIMEDIA TOOLS AND applications 2023年第14期83卷 43115页

作者： Mukherjee, N. Sengupta, S. Infosys Ltd Mysore India Aliah Univ Kolkata India

Diabetic retinopathy (DR) is an impediment of diabetes mellitus, which if not treated early may result in complete loss of vision, even without any preemptive symptoms. DR is caused by high level of glucose in the blood, causing alterations in the microvasculature of retina. However, early screening of diabetic patients through retinal fundus imaging, along with proper diagnosis and treatment can control the prevalence of DR complications. Manual inspection of pathological changes in retinal fundus images is an extremely challenging and tedious task. Therefore, computer-aided diagnosis (CAD) system is an efficient and effective method for early detection of DR and can greatly assist the ophthalmologists. CAD system encompasses DR detection and severity grading that includes detection, classification, localization and segmentation of lesions from the fundus images. Significant contributions have been made in DR severity grading using conventional image processing approaches using hand-engineered features and traditional machine-learning (ML) techniques. In the recent years, significant development of deep learning (DL) methods alleviated by the advancement of hardware computation power and efficient learning algorithms, has triumphed over the traditional ML methods in DR detection and grading tasks. Many researchers have employed the established as well as customized DL models in different DR image repositories and reported their findings. In this paper, we conduct a detailed review of the recent state-of-the-art contributions in the field of DL based DR classification by explaining their methodologies and highlighting their advantages and limitations. A detailed comparative study based on certain statistical parameters has also been conducted to quantitatively evaluate the methods, models and preprocessing techniques. In addition, the challenges in designing an efficient, accurate and robust deep-learning model for DR classification are explored in details to help t

关键词： Diabetic Retinopathy DR Stage Classification DR-related Lesions Medical image Analysis Computer-assisted Diagnosis machine Learning Deep Learning Survey

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Research on taper thread’s compensation algorithm based on machine vision considering the inclined state effect and tooth profile distortion

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Multimedia Tools and applications 2023年第29期82卷 45983-46010页

作者： Lu, Qianhai Kong, Lingfei Tian, Dongzhuang Sun, Jin Li, Longlong Gong, Chunyuan School of Mechanical and Precision Instrument Engineering Xi’an University of Technology Shannxi Xi’an710048 China Xi’an Research Institute of China Coal Technology and Engineering Group Corp Shaanxi Xi’an710077 China School of Mechanical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China

Drill pipe joint’s thread quality directly affects the machining performance and the drill pipe’s service life. machine vision can quickly detect thread parameters to determine the thread processing quality, but this method has low thread measurement accuracy due to factors such as drill pipe joint inclination and tooth shape distortion. This paper proposes a thread detection compensation algorithm based on thread geometry space and thread section projection theory to promote machine vision inspection accuracy. The distortion mechanism of thread section image caused by the drill pipe joint in an inclined state is revealed and a taper thread mathematical model is proposed. The difference equation is obtained by subtracting the projected contour from the theoretical contour, and the extreme value is obtained to correct the thread contour in the inclined state. Experiments show that the thread profile angle compensation efficiency can be increased by 60% under inclined conditions, and the requirements for the placement of drill pipe joints are reduced. A good agreement of the standard measurement with the experimental data proves the effectiveness of the proposed method. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Drill pipe

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Artificial Intelligence Algorithms for Robotic Harvesting of Agricultural Produce 1

Artificial Intelligence Algorithms for Robotic Harvesting of...

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1st International Conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Kolhalkar, Nilesh R. Pandit, Anupama A. Kedar, Shridhar Ashok Yedukondalu, G. MKSSS's Cummins College of Engg. For Women Department of Mechanical Engineering MH Pune India Department of Computer Science & Engg. Pune MH Pune India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Mechanical Engineering A.P. Vijayawada India

ISBN: (纸本)9798350355611

Robotic harvesting of fruits and vegetables is an advanced technology that leverages Robotics, Artificial Intelligence, and machine vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations. © 2025 IEEE.

关键词： machine vision

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Deep computer vision system for cocoa classification

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MULTIMEDIA TOOLS AND applications 2022年第28期81卷 41059-41077页

作者： Lopes, Jessica Fernandes Turrisi da Costa, Victor G. Barbin, Douglas F. Pier Cruz-Tirado, Luis Jam Baeten, Vincent Barbon Junior, Sylvio Londrina State Univ UEL Dept Elect Engn Londrina Parana Brazil Londrina State Univ UEL Dept Comp Sci Londrina Parana Brazil Univ Campinas UNICAMP Dept Food Engn Campinas Brazil Walloon Agr Res Ctr CRA W Gembloux Belgium Univ Trieste Dipartimento Ingn & Architettura DIA Trieste Italy

Cocoa hybridisation generates new varieties which are resistant to several plant diseases, but has individual chemical characteristics that affect chocolate production. image analysis is a useful method for visual discrimination of cocoa beans, while deep learning (DL) has emerged as the de facto technique for image processing . However, these algorithms require a large amount of data and careful tuning of hyperparameters. Since it is necessary to acquire a large number of images to encompass the wide range of agricultural products, in this paper, we compare a Deep Computer vision System (DCVS) and a traditional Computer vision System (CVS) to classify cocoa beans into different varieties. For DCVS, we used a Resnet18 and Resnet50 as backbone, while for CVS, we experimented traditional machine learning algorithms, Support Vector machine (SVM), and Random Forest (RF). All the algorithms were selected since they provide good classification performance and their potential application for food classification A dataset with 1,239 samples was used to evaluate both systems. The best accuracy was 96.82% for DCVS (ResNet 18), compared to 85.71% obtained by the CVS using SVM. The essential handcrafted features were reported and discussed regarding their influence on cocoa bean classification. Class Activation Maps was applied to DCVS's predictions, providing a meaningful visualisation of the most important regions of the images in the model.

关键词： machine learning Deep learning Computer vision Food quality

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2nd International Conference on Big Data, machine Learning, and applications, BigDML 2021

2nd International Conference on Big Data, Machine Learning, ...

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2nd International Conference on Big Data, machine Learning, and applications, BigDML 2021

ISBN: (纸本)9789819934805

The proceedings contain 58 papers. The special focus in this conference is on Big Data, machine Learning, and applications. The topics include: A Comparative Study of Loss Functions for Deep Neural Networks in Time Series Analysis;learning Algorithm for Threshold Softmax Layer to Handle Unknown Class Problem;traffic Monitoring and Violation Detection Using Deep Learning;conjugate Gradient Method for finding Optimal Parameters in Linear Regression;rugby Ball Detection, Tracking and Future Trajectory Prediction Algorithm;early Detection of Heart Disease Using Feature Selection and Classification Techniques;Gun Detection System for Surveillance Cameras Using HOG-Assisted KNN Classifier;Optimized Detection, Classification, and Tracking with YOLOV5, HSV Color Thresholding, and KCF Tracking;realtime Object Distance Measurement Using Stereo vision image processing;COVID-19 Detection Using Chest X-ray images;Comparative Analysis of LDA Algorithm for Low Resource Indian Languages with Its Translated English Documents;text Style Transfer: A Comprehensive Study on Methodologies and Evaluation;classification of Hindustani Musical Ragas Using One-Dimensional Convolutional Neural Networks;w-Tree: A Concept Correlation Tree for Data Analysis and Annotations;crawl Smart: A Domain-Specific Crawler;evaluating the Effect of Leading Indicators in Customer Churn Prediction;classification of Skin Lesion Using image processing and ResNet50;data Collection and Pre-processing for machine Learning-Based Student Dropout Prediction;Nested Named-Entity Recognition in Multilingual Code-Switched NLP;an Insight on Drone applications in Surveillance Domain;deep Learning-Based Semantic Segmentation of Blood Cells from Microscopic images;a Partitioned Task Offloading Approach for Privacy Preservation at Edge;Artificial Intelligence in Radiological COVID-19 Detection: A State-of-the-Art Review.

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A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

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PEERJ COMPUTER SCIENCE 2024年 10卷 e1947页

作者： Bhulakshmi, Dasari Rajput, Dharmendra Singh Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore Tamil Nadu India

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning -based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

关键词： DR DL Convolutional neural networks Recurrent neural networks Generative adversarial networks Fundus image

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A comprehensive survey on techniques to handle face identity threats: challenges and opportunities

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MULTIMEDIA TOOLS AND applications 2023年第2期82卷 1669-1748页

作者： Rusia, Mayank Kumar Singh, Dushyant Kumar MNNIT Allahabad CSED Prayagraj Uttar Pradesh India

The human face is considered the prime entity in recognizing a person's identity in our society. Henceforth, the importance of face recognition systems is growing higher for many applications. Facial recognition systems are in huge demand, next to fingerprint-based systems. Face-biometric has a highly dominant role in various applications such as border surveillance, forensic investigations, crime detection, access management systems, information security, and many more. Facial recognition systems deliver highly meticulous results in every of these application domains. However, the face identity threats are evenly growing at the same rate and posing severe concerns on the use of face-biometrics. This paper significantly explores all types of face recognition techniques, their accountable challenges, and threats to face-biometric-based identity recognition. This survey paper proposes a novel taxonomy to represent potential face identity threats. These threats are described, considering their impact on the facial recognition system. State-of-the-art approaches available in the literature are discussed here to mitigate the impact of the identified threats. This paper provides a comparative analysis of countermeasure techniques focusing on their performance on different face datasets for each identified threat. This paper also highlights the characteristics of the benchmark face datasets representing unconstrained scenarios. In addition, we also discuss research gaps and future opportunities to tackle the facial identity threats for the information of researchers and readers.

关键词： Biometrics Face recognition Authentication Computer vision machine learning Deep learning image processing

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MisConv: Convolutional Neural Networks for Missing Data 22

MisConv: Convolutional Neural Networks for Missing Data

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22nd IEEE/CVF Winter Conference on applications of Computer vision (WACV)

作者： Likowski, Marcin Przewiez Smieja, Marek Struski, Lukasz Tabor, Jacek Jagiellonian Univ Fac Math & Comp Sci 6 Lojasiewicza St PL-30348 Krakow Poland

ISBN: (纸本)9781665409155

processing of missing data by modern neural networks, such as CNNs, remains a fundamental, yet unsolved challenge, which naturally arises in many practical applications, like image inpainting or autonomous vehicles and robots. While imputation-based techniques are still one of the most popular solutions, they frequently introduce unreliable information to the data and do not take into account the uncertainty of estimation, which may be destructive for a machine learning model. In this paper, we present MisConv, a general mechanism, for adapting various CNN architectures to process incomplete images. By modeling the distribution of missing values by the Mixture of Factor Analyzers, we cover the spectrum of possible replacements and find an analytical formula for the expected value of convolution operator applied to the incomplete image. The whole framework is realized by matrix operations, which makes MisConv extremely efficient in practice. Experiments performed on various image processing tasks demonstrate that MisConv achieves superior or comparable performance to the state-of-the-art methods.

关键词： Adaptation models Uncertainty Convolution image processing Neural networks Estimation machine learning

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