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

作者： Arunakumari, B.N. Shashidhar, R. Naziya Farheen, H.S. Roopa, M. Department of Computer Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru560064 India Department of Electronics and Communication Engineering JSS Science and Technology University Karnataka Mysuru570006 India Department of Electronics and Communication Engineering Navkis College of Engineering Hassan Karnataka Hassan573217 India Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru560078 India

ISBN: (纸本)9789819934805

In recent years, great progress has been made on 2D and 3D image understanding tasks, such as object detection and instance segmentation. The recent trends in technology driverless cars are making a difference in daily life. The basic principle in these driverless cars is object detection and localization using multiple video cameras and LIDAR and it is one of the current trends in research and development, so attempts to achieve the same on small scale using the available resources. In the proposed method, firstly the stereo images are captured in a dual-lens camera, and secondly, converting the RGB image into a grayscale image. The third step is to apply a global threshold to separate the background, to get the same size of the image using morphological operation. Blob detection is used to detect the points and regions in the image. The fourth step is to detect the object distance and size measurement using the pinhole camera formula. Further, in the proposed work, an effort is made to determine the linear space between the camera and the object from the pictures taken from the camera. Typically, stereo images are used for computation. Binocular stereopsis, or stereo vision, is the capability to derive information about how far left the objects are, grounded uniquely on the comparative places of the object in the two eyes. It depends on both sensory and motor capabilities, using the similar principle the human brain employs, taking two images of the same object taken from two different linearly separated distances. The frame rate of the system can go a maximum of up to 15 frames per second. 15 frames per second can be considered as acceptable for most autonomous systems, and it will work in realtime. Effective convolutional matching technique between embeddings are used for localization that leads LIDAR to increase centimeter level accuracy by about 97%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pinhole cameras

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A concise review on food quality assessment using digital image processing

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TRENDS IN FOOD SCIENCE & TECHNOLOGY 2021年 118卷 106-124页

作者： Meenu, Maninder Kurade, Chinmay Neelapu, Bala Chakravarthy Kalra, Sahil Ramaswamy, Hosahalli S. Yu, Yong Zhejiang Univ Coll Biosyst Engn & Food Sci 866 Yuhangtang Rd Hangzhou 310058 Peoples R China Indian Inst Technol Dept Mech Engn Jammu 181221 J&K India Natl Inst Technol Biotechnol & Med Engn Rourkela 769008 Odisha India McGill Univ Dept Food Sci 21111 Lakeshore Rd Ste Anne De Bellevue PQ H9X 3V9 Canada Minist Agr Key Lab Equipment & Informatizat Environm Control 866 Yuhangtang Rd Hangzhou 310058 Peoples R China

Background: Recent advances in signal processing technology and computational power have increased the attention towards computer vision-based techniques in diverse applications such as agriculture, food processing, biomedical, and military. Especially in agricultural and food processing, computer vision can replace most of the manual methods for screening of seed, grain and food quality. Scope and approach: The objective of present study is to review the recent advancements in computer vision techniques for predicting quality of various raw materials and food products. This review paper is focused on the quality determination of grains, vegetables, fruits, beverages, meat, sea food and edible oils using Digital image processing (DIP). Several studies have reported the successful applications of DIP techniques for feature extraction, classification and quality prediction of foods. DIP algorithms are used to extract the significant features from images which are further used as input for machine learning (ML) algorithms to classify them based on different criteria. These feature extraction methods have been improved by Deep Learning (DL) algorithms. Features can be automatically extracted by DL algorithms resulting in higher accuracy. DL algorithms require huge data management and computational resources which can be a major limitation. Key findings and conclusion: A significant literature is available for quality estimation of food products by using computer vision algorithms, but they lack commercial exploitation. Android based applications have not yet been developed for this specific purpose. User friendly, low cost and portable devices equipped for quality estimation would be helpful for rapid quality measurement of food products in real time.

关键词： Food quality Classification Prediction Deep learning Artificial intelligence machine learning Computer vision Linear regression DIP

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Evaluating the Fairness of Discriminative Foundation Models in Computer vision 23

Evaluating the Fairness of Discriminative Foundation Models ...

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AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES)

作者： Ali, Junaid Kleindessner, Matthaus Wenzel, Florian Budhathoki, Kailash Cevher, Volkan Russell, Chris MPI Software Syst Saarbrucken Germany Amazon Web Serv Seattle WA USA

ISBN: (纸本)9798400702310

We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks. We then systematically evaluate existing methods for mitigating bias in these models with respect to our taxonomy. Specifically, we evaluate OpenAI's CLIP and OpenCLIP models for key applications, such as zero-shot classification, image retrieval and image captioning. We categorize desired behaviors based around three axes: (i) if the task concerns humans;(ii) how subjective the task is (i.e., how likely it is that people from a diverse range of backgrounds would agree on a labeling);and (iii) the intended purpose of the task and if fairness is better served by impartiality (i.e., making decisions independent of the protected attributes) or representation (i.e., making decisions to maximize diversity). Finally, we provide quantitative fairness evaluations for both binary-valued and multi-valued protected attributes over ten diverse datasets. We find that fair PCA, a post-processing method for fair representations, works very well for debiasing in most of the aforementioned tasks while incurring only minor loss of performance. However, different debiasing approaches vary in their effectiveness depending on the task. Hence, one should choose the debiasing approach depending on the specific use case.

关键词： AI fairness foundation models evaluation

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Design and Optimization of a Dynamic Train image Acquisition System Utilizing Line Scan Camera Technology 4

Design and Optimization of a Dynamic Train Image Acquisition...

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4th International Conference on Advances in Electrical Engineering and Computer applications, AEECA 2023

作者： Li, Changgeng Xu, Feng Li, Wei Sun, Xiaodong Yuan, Bo Crrc Qingdao Sifang Co. Ltd Qingdao266111 China

ISBN: (纸本)9798350308082

In response to the demand for efficient and high-quality daily maintenance and inspection of modern railway trains, the utilization of AI-based machine vision technology has been proven to significantly enhance maintenance efficiency and quality. image quality plays a critical role in visual inspection. Thus, this study presents the design of a dynamic train exterior image acquisition system based on line scan camera technology. Through a comprehensive analysis of maintenance requirements and business scenarios, the hardware structure and functional framework of the system are meticulously developed. The image acquisition module is strategically deployed in a 360° arrangement to enable panoramic image acquisition of the train's exterior. Leveraging the imaging principle of the line scan camera, two sets of magnetic cylinders are employed to measure the train's velocity multiple times, ensuring continuous correction of the image acquisition line frequency for stable image capture. Furthermore, a character recognition algorithm for train numbers is extensively researched, enabling accurate identification of the train number through image acquisition, preprocessing, and OCR recognition algorithms. The proposed system integrates professional image acquisition and processing techniques, enabling high-resolution and high-precision stable acquisition of dynamic train images. It provides crucial data support for precise detection of train exterior conditions and offers reliable technical means for train trajectory monitoring and safety management. © 2023 IEEE.

关键词： image acquisition

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A Comparative Evaluation of Deep Learning Techniques for Photovoltaic Panel Detection From Aerial images

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IEEE ACCESS 2023年 11卷 47579-47594页

作者： Arnaudo, Edoardo Blanco, Giacomo Monti, Antonino Bianco, Gabriele Monaco, Cristina Pasquali, Paolo Dominici, Fabrizio Politecn Torino Dipartimento Automati & Informat DAUIN I-10129 Turin Italy Fdn LINKS I-10138 Turin Italy ITHACA Srl I-10138 Turin Italy

Solar energy production has significantly increased in recent years in the European Union (EU), accounting for 12% of the total in 2022. The growth in solar energy production can be attributed to the increasing adoption of solar photovoltaic (PV) panels, which have become cost-effective and efficient means of energy production, supported by government policies and incentives. The maturity of solar technologies has also led to a decrease in the cost of solar energy, making it more competitive with other energy sources. As a result, there is a growing need for efficient methods for detecting and mapping the locations of PV panels. Automated detection can in fact save time and resources compared to manual inspection. Moreover, the resulting information can also be used by governments, environmental agencies and other companies to track the adoption of renewable sources or to optimize energy distribution across the grid. However, building effective models to support the automated detection and mapping of solar photovoltaic (PV) panels presents several challenges, including the availability of high-resolution aerial imagery and high-quality, manually-verified labels and annotations. In this study, we address these challenges by first constructing a dataset of PV panels using very-high-resolution (VHR) aerial imagery, specifically focusing on the region of Piedmont in Italy. The dataset comprises 105 large-scale images, providing more than 9,000 accurate and detailed manual annotations, including additional attributes such as the PV panel category. We first conduct a comprehensive evaluation benchmark on the newly constructed dataset, adopting various well-established deep-learning techniques. Specifically, we experiment with instance and semantic segmentation approaches, such as Rotated Faster RCNN and Unet, comparing strengths and weaknesses on the task at hand. Second, we apply ad-hoc modifications to address the specific issues of this task, such as the wide range of

关键词： Solar energy Prediction algorithms Semantics Photovoltaic systems Benchmark testing Computer vision deep learning image processing machine learning semantic segmentation instance segmentation remote sensing

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A machine vision Measurement Method for Large Plates Based on Reference Point Assistance 5

A Machine Vision Measurement Method for Large Plates Based o...

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5th International Conference on Electronics and Electrical Engineering Technology (EEET)

作者： Ji, Tianyi Zhao, Zhiwei Zhao, Ning Southeast Univ Coll Elect Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9798350320428

In this work, a coordinate positioning method based on machine vision with reference point assistance is proposed to apply to the size measurement for large rectangular plate parts. This method first obtains the coordinate information of the reference point through circular detection, and uses the line detection and mean-shift clustering algorithm to fit the plate vertex information. Then the pixel coordinate relationship between the reference point and the plate vertex is used to solve the world coordinate of the plate vertex. Finally, the world coordinate of the plate vertex is used to calculate the length and width dimensions of the plate. Instead of other methods which unify coordinate system by moving cameras, this method uses reference point assistance system on measuring plate to unify the coordinate system, simplifying the difficulty of the unification of coordinate system and ensuring accuracy. Effectiveness of such method is verified by measuring rectangular plates with scales from centimeters to meters. The results showed that the measurement error could be controlled within 5 millimeters. In practical applications, the measurement accuracy has been much higher than the processing accuracy during the plate manufacturing, meeting the needs of size measurement.

关键词： reference points size measurement machine vision image processing

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A systematic literature review of defect detection in railways using machine vision-based inspection methods

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International Journal of Transportation Science and Technology 2024年

作者： Kumar, Ankit Harsha, S.P. Advanced Mechanical Vibration Laboratory Mechanical and Industrial Engineering Department IIT Roorkee India IIT Roorkee India

Train rolling stock and track inspections are necessary for the safe operation of the train. For this reason, a regular inspection of defects is required for the train rolling stock. The conventional defect detection methods yield low efficiency, consume more time, are unreliable, and are less cost-effective. These obstacles may be mitigated by integrating a machine vision-based inspection system (MVIS). This systematic literature review explores the landscape of railway defect detection methodologies, primarily focusing on leveraging image processing techniques. This comprehensive analysis encompasses many studies examining the evolution of image processing applications in the context of railway rolling stock and rail track defect detection. From traditional methods to the latest advancements, a nuanced understanding of the challenges and innovations in this domain is required. Key themes include utilizing computer vision algorithms, machine learning models, and deep learning techniques for enhanced accuracy in identifying defects. We delve into the intricacies of image acquisition, preprocessing, and feature extraction, shedding light on the pivotal role of these processes in refining defect detection systems. Also, the current gaps and opportunities for future research, emphasizing the need for standardized datasets, benchmarking methodologies, and the integration of emerging technologies, are highlighted. This review not only consolidates the existing knowledge but also serves as a roadmap for researchers invested in advancing the field of railway defect detection. By synthesizing insights from many studies, this review contributes to a deeper understanding of the state-of-the-art in railway defect detection using image processing, fostering dialogue and collaboration for improving railway safety and reliability. © 2024 Tongji University and Tongji University Press

关键词： Computer vision

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International Conference on VLSI, Signal processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

International Conference on VLSI, Signal Processing, Power E...

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International Conference on VLSI, Signal processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

ISBN: (纸本)9789819746569

The proceedings contain 35 papers. The special focus in this conference is on VLSI, Signal processing, Power Electronics, IoT, Communication and Embedded Systems. The topics include: Sensor Perception Reliability in Automated Vehicles;design and Implementation of 16-Bit Posit Arithmetic;emergence, Evolution, and applications of Medical Cyber-Physical Systems;smart Insole Design for Foot Pressure Monitoring;an Efficient Cryptographic Technique to Improve Data Privacy and Security in E-Commerce;an Iot-Based Transmission Line Fault Monitoring System;performance Analysis of the System of IoT Architecture;real-Time Implementation of IoT-Based Home Automation System on Arduino Platform;Data Security Paradigms: Paillier Homomorphic Encryption and AES in the Context of Privacy-Preserving Computation;a Review on Watermarking Techniques of Digital image;An Improved Pre-charging Strategy for Asymmetrical Mixed-Cell Submodule (SM) Based Modular Multilevel Converter;classification of Remotely Sensed Data Using Fisher’s Linear Discriminant;design of Smart Wheelchair for Disabilities with Quadriplegia Through Brain–Computer Interface;Automatic Load Distribution of Power During Low Power Generation Using PLC;AI-driven Lung Cancer Detection for Rapid Analysis of Medical Imaging Data;comparative Study of Transfer Learning Models for Mushroom Classification;automated Software Defect Prediction Model: AdaBoost-Based Support Vector machine Approach;Preprocessing of MRI Data for the Early Detection of Alzheimer’s Disease Using DWT;classification of Soybean Seed Using Support Vector machine with image Enhancement Techniques;From Deployment to Drift: A Comprehensive Approach to ML Model Monitoring with Evidently AI;a Real-Time Computer vision-Based Green Intensity Estimation Method for the Arecanut Tree Pesticide Spraying System;key Enabling Technologies for Next-Generation Wireless Systems.

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Contribution Unveiling Cutting-Edge machine Learning Techniques for image Segmentation

Contribution Unveiling Cutting-Edge Machine Learning Techniq...

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International Conference on Electrical and Electronics Engineering, ICEEE 2023

作者： Shaik, Nazeer Gupta, Ankur Bhati, Sunita Kumar, Jaideep Singh, Jagendra Budhiraja, Ishan Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology Andhra Pradesh Anantapur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Applications JECRC University Jaipur India Raj Kumar Goel Institute of Technology AKTU University Lucknow India

ISBN: (纸本)9789819986606

Segregation of images is a critical step in processing images, computer vision, and a variety of other disciplines. The technique involves decomposing an illustration into numerous components or components, every single one that consists of an ensemble of elements with identical features for example African descent, frequency, or consistency. The most important objective of appearance, the intention of fragmentation seems to reduce complexity or customize the mathematical representation of an illustration in a way that is more readily reasonable and more straightforward for assessment. It is widely employed to locate boundaries and features in photos, and this is favorable in an assortment of industries including clinical imaging, object detection, recognition, and autonomous vehicles. Several image segmentation techniques are available, and in incorporating thresholding, zone is an area-based differentiation and corner-based recognition. A threshold segment is a simple and commonly used technique that involves setting a threshold value and dividing the pixels into two classes based on their intensity values. Region-based segmentation involves grouping pixels based on their spatial proximity and similarity in characteristics, while edge-based segmentation involves detecting edges or boundaries in an image and using them to separate different regions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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A review of digital image processing techniques for optical tweezers: from algorithms to applications

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JOURNAL OF MODERN OPTICS 2025年

作者： Tang, Weijie Zhou, Ruohan Hu, Sheng Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Hebei Key Lab Micronano Precis Opt Sensing & Measu Qinhuangdao 066004 Peoples R China

As a ubiquitous manipulation tool, optical tweezers are widely used in biochemistry and applied physics, so that a wide range of microscopic and nanoscopic particles could be investigated. In recent years, digital image processing techniques for improving target particle observation have diversified, leading to the development of numerous automatic tasks. The technique was developed in response to the need for multi-particle manipulation and feature detection. Here we describe how digital image processing can be used to enhance the capabilities of optical manipulation. In particular, cutting-edge image processing techniques that rely on artificial intelligence development are making optical trapping more widely accessible and enabling automatic manipulation of microscopic and nanoscopic particles.

关键词： Optical tweezers image processing machine vision image algorithm artificial intelligence

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