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Research on Improved image Edge Detection Based on Hough Transform

Research on Improved Image Edge Detection Based on Hough Tra...

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International conference on image processing and Intelligent Control (IPIC)

作者： Cheng, Long Fang, Jian Wu, Yue Kang, Kai Changchun Univ Technol Elect & Elect Engn Changchun 130012 Peoples R China Changchun Univ Technol Coll Electromech Engn Changchun 130012 Peoples R China Jilin Engn Normal Univ Sch Elect Engn Changchun 130052 Peoples R China

ISBN: (数字)9781510647251

ISBN: (纸本)9781510647251;9781510647244

Line detection in images has always been one of the important research fields of artificial intelligence computer vision for image *** transform (HT) is one of the most extensive detection algorithms in image processing and machine vision applications, and has the advantages of anti-noise. However, it involves a huge amount of computation and too much *** view of its shortcomings, it is recommended to improve its performance. In this paper, a new detection method is cited, and the statistical probability Hough line transformation is *** first step is to use the Canny operator to obtain the edge information of the gray image and use the improved method to detect the straight line segment in the image. This algorithm enhances the accuracy of edge detection and optimizes the extraction of the straight line. At the same time, it has the simplicity and wide applicability of Hough transform.

关键词： image processing straight line detection edge detection Hough transform

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vision-Based Autonomous Car Brake and Steering Assistance Using Deep Learning

Vision-Based Autonomous Car Brake and Steering Assistance Us...

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AIML-applications for Engineering & Technology (ICAET), International conference on

作者： Anees H Pooja Agrawal School of Robotics Defence institute of advanced technology Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

This paper presents a novel approach for enhancing vehicle safety and navigation through an integrated system for lane detection, vehicle alignment, and automatic braking using visual feedback. Our proposed system employs advanced deep learning and computer vision techniques with real-time processing to detect the exact boundaries of lane and ensures precise vehicle movement within the lane. The system continuously analyses lane markings and modifies the vehicle's position to ensure optimal lane adherence by utilizing a combination of machine learning algorithms and camera-based image processing. Additionally, the system incorporates an adaptive braking mechanism that identifies vehicles ahead using visual inputs. Furthermore, the jerks experienced during steering alignment can be greatly reduced by the suggested steering control system. The system's efficiency in various driving conditions is evidenced by its experimental simulation results, which also show improvements in collision avoidance and lane-keeping accuracy. This approach contributes to improved driving convenience and road safety by marking a substantial advancement in autonomous driving technologies.

关键词： YOLO Instance segmentation Deep learning visualization Accuracy Simulation Real-time systems Road safety Automobiles Autonomous vehicles

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Performance Analysis of machine Learning Models for Object Recognition in Underwater video images 29

Performance Analysis of Machine Learning Models for Object R...

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29th IEEE conference on Signal processing and Communications applications (SIU)

作者： Ozdilli, Baris Guney Arslan, Mustafa Bora Alp, Tilbe Albayrak, Ozlem Unal, Perin Bozkurt, Ozlem Ozbayoglu, A. Murat TOBB Ekon & Teknol Univ Bilgisayar Muhendisligi Ankara Turkey Teknopar Ankara Turkey TOBB ETU Elekt Muhendisligi Ankara Turkey TOBB ETU Bilgisayar Muhendisligi Ankara Turkey

ISBN: (纸本)9781665436496

In this study, our primary aim is to detect different formations, objects on the images taken from various underwater videos. For this purpose, machine learning models such as SVM, multi-layer perceptron, logistic regression that use attributes, image histogram obtained from images were chosen. In addition, Autoencoder and CNN based deep learning models were used directly over images and their performances were compared. According to the results, it was observed that all models were satisfactory and achieved good classification performances. The highest performance was observed in the Autoencoder based deep learning model, which achieved an accuracy level of %95. In the future, we are planning to continue studies to focus on underwater cable tracking and detecting errors and anomalies in underwater cables.

关键词： Underwater image analysis computer vision object recognition deep learning machine learning

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Development of methods for parallel processing of series of images obtained by a machine vision system in various electromagnetic ranges 2

Development of methods for parallel processing of series of ...

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conference on Optical Instrument Science, Technology, and applications II Held as Part of SPIE Optical System Design conference

作者： Semenishchev, Evgenii Voronin, viacheslav Alepko, Andrey Zelensky, Aleksandr Agaian, Sos Moscow State Tech Univ STANKIN Moscow Russia CUNY Dept Comp Sci New York NY 10314 USA

ISBN: (数字)9781510645974

ISBN: (纸本)9781510645974;9781510645967

machine vision systems used in modern industrial complexes, based on the analysis of multi and hyperspectral imaging. The transition to implementing the "Industry 4.0" program is not possible when using one type of data. The first control system used only the visible range image. They made it possible to analyze the trajectories of movement of objects, control product quality, carry out security functions (control of perimeter crossing), etc. The development of new industrial robotic cells and processing complexes using cognitive functions implying the receipt, analysis, and processing of heterogeneous data. The construction of a unified information field, which allows performing multidimensional operations with data, allows increasing the speed of decision-making and the implementation of automated robot-human systems at the level of an assistant working in a unified workspace. The use of machine vision systems analyzing information received in: visible (shape, the trajectory of movement, position of objects, etc.);near-infrared range (data is similar to visible, allows operation in dusty, foggy, low light conditions);far-infrared range - thermal (plotting temperature gradients, identifying areas of overheating);ultraviolet range (analysis of ionization sources, corona discharges, static charges, tags);X-ray and microwave ranges (analysis of the surface and internal structure of objects, allow the identification of defects);range and 3D sensors (construction of volumetric figures, analysis of the relative position of objects and their interaction), etc. Data analysis is often performed not by a single camera but by a group of sensors located not in a single housing. Primary data integration reduces the number of information channels while maintaining the functionality and accuracy of the analysis. The article discusses creating fusion images obtained by industrial sensors into a combined image containing joint data. Combining multi and hyperspectral imaging makes i

关键词： preprocessing IR image local features denoising combining image fusion image

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Enhancing Industrial Automation Flexibility Through Neural Network-Empowered machine vision applications 1st

Enhancing Industrial Automation Flexibility Through Neural N...

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1st International conference on Artificial Intelligence and its applications, ICAIA 2023

作者： George, Navin M. Rosero, Evelyn Mayakannan, S. Shameem, A. Divya, S. Arul, N. Electronics and Communication Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Riobamba060155 Ecuador Department of Mechanical Engineering Vidya Vikas College of Engineering and Technology Namakkal Tamilnadu Tiruchengode India AMET Business School AMET University Tamilnadu Chennai India Erode Tamil Nadu Perundurai638057 India Department of Mechatronics Engineering Sona College of Technology Tamilnadu Salem India

ISBN: (纸本)9783031843969

vision-based systems provide non-contact sensory input for processing and feedback, expanding the capabilities of industrial automation applications. Prediction using ANNs aids such conformities in navigating nonlinear computational landscapes. They map the plethora of potential outcomes or zones of uncertainty introduced by the system’s constituents onto a set of feasible options for action. A measure of intelligence is transferred to robotic systems through the use of trained networks. Two uses of machine vision are presented here. The 3 DOF robotic assembly enables precision cutting of fragile materials with the help of visual guiding and pixel elimination. The 6-degrees-of-freedom robot uses a combination of visual feedback from an onboard camera and supervision from an external camera to achieve its goals. Pick-and-place operations are executed with a switching control method. Both systems improved in terms of computing time and convergence once ANN was used to make the strategies intelligent. The retrieved scene image features are put to use by the networks in a variety of ways. The simulation and experimental results that back up the suggested approaches demonstrates the value of artificial neural network in machine vision applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robotic assembly

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Role of Thermal images in Various applications of Computer vision

Role of Thermal Images in Various Applications of Computer V...

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Intelligent Engineering and Management (ICIEM), International conference on

作者： Ravina Gupta Sarika Jain Manoj Kumar AIIT Amity University Noida India EIS University of Wollongong Dubai

Object detection is an advanced area of image processing and computer vision. Its major applications are in surveillance, autonomous driving, face recognition, anomaly detection, traffic management, agriculture etc. This paper focuses on various object detection techniques in thermal images. A thermal imaging sensor is a device that creates an image by analyzing temperature differences between different objects in a scene and detecting radiation from those objects. In recent years, many machine learning and deep learning algorithms have been used to recognize objects in thermal images. This study makes a comparison of YOLO, YOLO DarkNet, Retinex algorithm, CNN-based machine learning model Support Vector machine (SVM), and Gaussian Mixture Model (GMM), Mixer of Gaussian (MoG), Mean Shift Approach, Faster R-CNN and Deep Neural Network along with different datasets.

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DocEnTr: An End-to-End Document image Enhancement Transformer 26

DocEnTr: An End-to-End Document Image Enhancement Transforme...

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26th International conference on Pattern Recognition / 8th International Workshop on image Mining - Theory and applications (IMTA)

作者： Souibgui, Mohamed Ali Biswas, Sanket Jemni, Sana Khamekhem Kessentini, Yousri Fornes, Alicia Llados, Josep Pal, Umapada Univ Autonoma Barcelona Barcelona Spain Univ Sfax MIRACL Lab Sfax Tunisia Digital Res Ctr Sfax SMRTS Lab Sfax Tunisia Univ Autonoma Barcelona Dept Comp Sci Comp Vis Ctr Barcelona Spain Indian Stat Inst CVPR Unit Kolkata India

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of-the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://***/dali92002/DocEnTR.

关键词： Degradation Head Optical character recognition Self-supervised learning Benchmark testing Transformers Magnetic heads

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Enhanced Efficient image Dehazing for Onboard Satellite imagery processing

Enhanced Efficient Image Dehazing for Onboard Satellite Imag...

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machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International conference on

作者： D. Sai Sreekar vishesh Vatsal Adithya Kothandhapani Ramakrishnan Rajagopalan Computer Vision Team Hyspace Technologies Pvt. Ltd. Bangalore India Chief of Technology Hyspace Technologies Pvt. Ltd. Bangalore India Chief of Products Hyspace Technologies Pvt. Ltd. Bangalore India Former Group Director at Space Applications Center ISRO

Satellite imagery is often composed of diverse terrains like forest, desert, snow and exhibits haze, fog, thin clouds which require dehazing in order to make them analysisready. Onboard processing of satellite imagery requires the algorithm's parameters to be fine-tuned depending on the type of terrain encountered. From the atmospheric light scattering model, the estimation of atmospheric light and transmission map is performed in single image dehazing method. This paper focuses on tuning an existing method the “Efficient image Dehazing with Boundary Constraints and Contextual Regularization method for satellite imagery”. A new image quality assessment method is introduced to enable fine-tuning the exponent of the algorithm. With the onset of onboard processing requirements, parallel implementation and faster image processing methods are explored for small run-times.

关键词： image quality Satellites Atmospheric modeling Snow image processing Light scattering Forestry

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Evolving processing Pipelines for Industrial Imaging with Cartesian Genetic Programming

Evolving Processing Pipelines for Industrial Imaging with Ca...

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Autonomic Computing and Self-Organizing Systems (ACSOS),International conference on

作者： Andreas Margraf Henning Cui Anthony Stein Jörg Hähner Digital Production and AI Fraunhofer IGCV Augsburg Germany Organic Computing Group University of Augsburg Augsburg Germany AI in Agricultural Engineering University of Hohenheim Stuttgart Germany

The reconfiguration of machine vision systems heavily depends on the collection and availability of large datasets, rendering them inflexible and vulnerable to even minor changes in the data. This paper proposes a refinement of Miller’s Cartesian Genetic Programming methodology, aimed at generating filter pipelines for image processing tasks. The approach is based on CGP-IP, but specifically adapted for image processing in industrial monitoring applications. The suggested method allows for retraining of filter pipelines using small datasets; this concept of self-adaptivity renders high-precision machine vision more resilient to faulty machine settings or changes in the environment and provides compact programs. A dependency graph is introduced to rule out invalid pipeline solutions. Furthermore, we suggest to not only generate pipelines from scratch, but store and reapply previous solutions and re-adjust filter parameters. Our modifications are designed to increase the likelihood of early convergence and improvement in the fitness indicators. This form of self-adaptivity allows for a more resource-efficient configuration of image filter pipelines with small datasets.

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Hybrid machine vision Systems Achieve High-speed video Rates with Object and Scene Tracking

Hybrid Machine Vision Systems Achieve High-speed Video Rates...

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conference on Lasers and Electro-Optics (CLEO)

作者： Rodriguez, Frank Muminov, Baurzhan Vuong, Luat T. Univ Calif Riverside Dept Mech Engn Riverside CA 92521 USA

Hybrid vision systems may enable real-time image processing in remote, power/energy-limited applications. We demonstrate 40k/17k frame-per-second self-motion inference rates with optical processing, which is 3 orders ... 详细信息

ISBN: (纸本)9781943580910

关键词： High speed cameras

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