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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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Design of a Lightweight Convolutional Neural Network Accelerated by FPGA

Design of a Lightweight Convolutional Neural Network Acceler...

引用

Algorithm, image processing and machine vision (AIPMV), International conference on

作者： Yingjun Xu Kun Wang Shanxi University College of Physics and Electronic Engineering Taiyuan China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

image classification has widespread applications in various fields such as medical diagnosis, autonomous driving, security surveillance, and manufacturing. The existing architecture has a complex computation workload, leading to increased latency. This paper designs and implements a convolutional neural network architecture accelerated by FPGA, quantizing the parameters to int14 fixed-point integers, achieving an effective recognition rate of 98.2 % for handwritten digits. By reducing the number of parameters and arithmetic operations, the difficulty of deploying this CNN architecture on hardware devices is lowered, it also reduces the usage of storage resources, the combination of parallel computation of convolutions with pipelining operations improves the real-time performance of the overall architecture. Utilizing the resources on the ZYNQ-7020 FPGA development board to build the CNN architecture, achieving a prediction time of 86.02 μs for a single image under a clock frequency of 50 MHz, compared to existing architectures, it has significant advantages in terms of real-time performance.

关键词： Handwriting recognition Time-frequency analysis image coding Surveillance Computer architecture Real-time systems Convolutional neural networks Security Field programmable gate arrays Pipeline processing

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image Target Detection Algorithm Based on Computer vision Technology

Image Target Detection Algorithm Based on Computer Vision Te...

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2022 International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2022

作者： Yuan, Haidi Anhui Sanlian College Basic Experiment Teaching Center Anhui Hefei China

ISBN: (数字)9781665490092

ISBN: (纸本)9781665490092

With the rapid development of intelligent systems and the advent of the era of big data, the continuous development of computers is being promoted. Exporting and tracking moving targets in video images is one of the most important research contents of computer vision. It combines many advanced technologies in the field of computing, such as image processing, pattern recognition, automatic control and artificial intelligence, and is widely used in intelligent surveillance. In various fields such as traffic control, machine intelligence and medical diagnosis, visual effects are obtained through image or image processing. Record videos from the computer and perform specific mechanical tasks. In terms of intelligent tracking, as the demand for applications in various complex environments continues to grow, how to improve the robustness and accuracy of moving target tracking and tracking algorithms has become the focus of ongoing target tracking research. This paper studies the image target detection algorithm based on computer vision technology. Firstly, the literature research method is used to summarize the existing problems of image target detection based on computer vision technology and the existing algorithms. The experiment is used to analyze the image target based on computer vision technology. The detection algorithm is verified, and the error rate of image target detection of the algorithm proposed in this paper is compared. According to the experimental results, it can be seen from Figure 1 that in experiment 1, the target detection of the GMM-STMRF algorithm is more accurate than other methods based on the calculation of the false detection rate. The maximum false detection rate is only 2.3%, and the other algorithms have 5.4%-11.1% false detection rate The GMM-STMRF algorithm increases the multi-frame calculation in the time dimension, so the calculation time has increased. Algorithms such as GMM and MeanShift need to estimate the multi-frame parameters, an

关键词： Target tracking

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On-Line Monitoring System of Welding Quality Based on machine vision and machine Learning

On-Line Monitoring System of Welding Quality Based on Machin...

引用

image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Genchen Peng Liping Zhang Zhijun Lu Yong Shi Jiangsu XCMG Construction Machinery Research Institute Co. Ltd Xuzhou China Road Machinery Branch XCMG Construction Machinery Co. Ltd Xuzhou China

In this paper, an online monitoring system of welding quality based on machine vision and machine learning was proposed. A high-speed CCD camera was used to monitor the tail end of the molten pool, and the remove small objects algorithm and contour compensation based on convex hull algorithm were utilized to achieve high-precision collection of features such as the width and length of the tail of the molten pool. This effectively solved the technical challenges caused by welding splashes and plasma arc, which could interfere with visual acquisition. Combined with neural network algorithms, a welding quality model was established and validated to accurately identify defects such as welding undercut, welding deviations, and unstable welding processes, with a defect recognition rate of $\geq 94\%$ .

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Facial Expression Recognition Using Transfer Learning with ResNet50 7th

Facial Expression Recognition Using Transfer Learning with R...

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7th International conference on Inventive Systems and Control, ICISC 2023

作者： Hiremath, Shantala S. Hiremath, Jayaprada Kulkarni, Vaishnavi V. Harshit, B.C. Kumar, Sujith Hiremath, Mrutyunjaya S. Image Processing Sony India Software Centre Pvt. Ltd. Karnataka Bangalore560103 India Department of Computer Science and Engineering Visvesvaraya Technological University Belgaum590018 India Department of CSE Alvas Institute of Engineering and Technology Mijar Karnataka Moodbidri574225 India Department of Image Processing eMath Technology Pvt. Ltd. Karnataka Bangalore560072 India Artificial Intelligence and Machine Learning VVDN Technologies Pvt. Ltd. Karnataka Bangalore560066 India

ISBN: (纸本)9789819916238

Facial expression recognition mimics human coding abilities and delivers non-verbal human–robot communication cues. machine learning and deep learning techniques enable real-world computer vision applications. Deep learning-based facial emotion recognition models have under-fitted or over-fitted due to inadequate training data. They are using FER2013’s 7 picture categories. Face detection using AdaBoost, scaling with OpenCV, and contrast improvement with histogram equalization preprocess these pictures. These pre-processed pictures are given to the ResNet50 pre-trained network, which obtained 77.3% accuracy. Transfer learning improves this outcome. By running pre-processed pictures through ResNet50’s FC1000 layer, features are retrieved and trained using a multiclass nonlinear support vector machine (SVM) classifier with seven classes. Training with 89.923% accuracy creates a knowledge base. Emotion recognition techniques let robots understand people, which can improve HCI. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Golub Kahan Based Methods for Partial Multidimensional Singular Value Decomposition 13

Golub Kahan Based Methods for Partial Multidimensional Singu...

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13th International conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Hachimi, Anas El Hached, Mustapha Jbilou, Khalide Ratnani, Ahmed Mohammed VI Polytechnic University UM6P Vanguard Center Green City Morocco Université du Littoral ULCO Calais France Laboratoire P. Painlevé Université de Lille Villeneuve d'Ascq France Université du Littoral ULCO Laboratoire LMPA Calais France Mohammed VI Polytechnic University Green City Morocco

ISBN: (纸本)9798350396119

Many applications of machine learning, data, image and signal processing require the design of algorithms capable of dealing with very large datasets. Extracting the most meaningful features out of these large datasets is often a computationally challenging task. In the matrix case, the Singular Value Decomposition (SVD) has shown to be a very efficient tool to do so. In recent years there has been a very significant increase in the amount of available data and the datasets are often formatted as tensors, for which some generalisations of the SVD have already been proposed for some specific choices of tensor-tensor products, well adapted to tensors of order 3. In this work, we propose a new generalisation, associated with the well-known Einstein product, regardless of the order of the tensors. As in the matrix case, only a few singular elements need to be computed and we address this problem by proposing an iterative approach based on the tensor Lanczos bidiagonalization applied to the Einstein product. We also propose a refinement to this method in order to increase its accuracy. We give numerical results for both synthetic and real data to illustrate the efficiency of these methods. © 2023 IEEE.

关键词： Tensors

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Self-Supervised Deep Fisheye image Rectification Approach using Coordinate Relations 17

Self-Supervised Deep Fisheye Image Rectification Approach us...

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17th International conference on machine vision applications (MVA)

作者： Hosono, Masaki Simo-Serra, Edgar Sonoda, Tomonari Waseda Univ Tokyo Japan Utagoe Inc Tokyo Japan

ISBN: (纸本)9784901122207

With the ascent of wearable camera, dashcam, and autonomous vehicle technology, fisheye lens cameras are becoming more widespread. Unlike regular cameras, the videos and images taken with fisheye lens suffer from significant lens distortion, thus having detrimental effects on image processing algorithms. When the camera parameters are known, it is straight-forward to correct the distortion, however, without known camera parameters, distortion correction becomes a non-trivial task. While learning-based approaches exist, they rely on complex datasets and have limited generalization. In this work, we propose a CNN-based approach that can be trained with readily available data. We exploit the fact that relationships between pixel coordinates remain stable after homogeneous distortions to design an efficient rectification model. Experiments performed on the cityscapes dataset show the effectiveness of our approach. Our code is available at GitHub(1).

关键词： machine vision Distortion Cameras Data models Task analysis image reconstruction Autonomous vehicles

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Design of Multi view Natural Language Query System on machine vision

Design of Multi View Natural Language Query System on Machin...

引用

Power Electronics, Computer applications (ICPECA), IEEE International conference on

作者： Quanrong Fang Amazon (China) Investment Co. Ltd Beijing China

In various fields such as medical imaging, object detection, and video surveillance, multi view natural language query systems utilize image data to provide a more comprehensive perspective, allowing users to intuitively query and obtain information. Due to the lack of a deep understanding of natural language in the hard coded matching rule method, the query results do not match the user's intentions and are difficult to meet practical application needs. Therefore, this article introduces machine vision algorithms for optimization and improvement. This article first discusses the system architecture of four modules: data input and preprocessing, visual feature extraction, natural language understanding and matching, and result generation and feedback. Then, the application of machine vision technology in the system was analyzed using two calculation formulas: grayscale conversion and binarization, and natural language processing technology was briefly discussed. Subsequently, a context understanding module was added to construct a multi view natural language query system based on machine vision. Finally, two sets of simulation experiments were conducted to draw the following conclusion: compared with traditional methods, the overall average improvement in image recognition accuracy indicators is about 14.3%, while the overall average improvement in response speed indicators is about 26.5%. This research system can effectively process images from different perspectives and match them with natural language queries.

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4th International conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

4th International conference on Innovative Manufacturing, Me...

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4th International conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

ISBN: (纸本)9789819988181

The proceedings contain 50 papers. The special focus in this conference is on Innovative Manufacturing, Mechatronics and Materials Forum. The topics include: Effect of Crack Length, Depth, and Location on Natural Frequencies of Railway Track;imputation Analysis of Time-Series Data Using a Random Forest Algorithm;harnessing machine Learning, Blockchain, and Digital Twin Technology for Advanced Robotics in Manufacturing: Challenges and Future Directions;humanizing Humanoids: An Extensive Review on the Potential of Prosthetic Robotic Arm with Integrated Monitoring System for Disabled People;intelligent Machining Systems for Robotic End-Effectors: State-of-the-Art and Toward Future Directions;artificial Neural Network Analysis in Road Crash Data: A Review on Its Potential Application in Autonomous Vehicles;the Role and Impact of Robotics Integration in Precision Machining and Manufacturing: A Comprehensive Review;nanomaterial in Robotics: Bridging the Gap Between Current applications and Future Possibilities;Optimized-ELM Based on Geometric Mean Optimizer for Bearing Fault Diagnosis;detection of Fault Features in Remanufacturing of Automotive Components Using image processing and Computer vision Techniques;the Significance of the Thoracic Spinal Multiple Segments During Different Pick-Object Approaches;vibration Suppression of the Flexible Beam Structure Using PID Controller Tuned by Advanced Firefly Algorithm;Bearing Fault Diagnosis Based on Prominence Peak-Picking IMFs Selection and PSO-SSAE;investigation of Wheel Robot Grouser Width Parameter Effect on Robot Mobility in Soft Sand Terrain Using Sand Test Bed;automated Harmonic Signal Removal-Based image Feature Extraction Technique: A Comparative Study Using Online Databases;Human Mental Stage Interpretation Based on the Analysis of Electroencephalogram (EEG) Signals.

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Benefits of Layered Software Architecture in machine Learning applications

Benefits of Layered Software Architecture in Machine Learnin...

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International conference on image processing and vision Engineering (IMPROVE)

作者： Romhanyi, Armin Vamossy, Zoltan Obuda Univ John von Neumann Fac Informat Becsi St Budapest Hungary

ISBN: (纸本)9789897585111

The benefits of layering in software applications are well-known not only to authors and industry experts, but to software enthusiasts as well because the layering provides a testable and more error-proof framing for applications. Despite the benefits, however, the increasingly popular area of machine learning is yet to embrace the advantages of such a design. In the present paper, we aim to investigate if characteristic benefits of layered architecture can be applied to machine learning by designing and building a system that uses a layered machine learning approach. Then, the implemented system is compared to other already existing implementations in the literature targeting the field of facial recognition. Although we chose this field as our example for its literature being rich in both theoretical foundations and practical implementations, the principles and practices outlined by the present work are also applicable in a more general sense.

关键词： machine Learning Layered Architecture Software Engineering Facial Authentication Facial Landmark Points

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A Survey of applications of vision Transformer and its Variants

A Survey of Applications of Vision Transformer and its Varia...

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Intelligent Data and Security (IDS), IEEE International conference on

作者： Chuang Wu Tingqin He Hunan Key Laboratory for Service computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

The Transformer architecture, renowned for its efficacy in natural language processing, encounters unique hur-dles when applied to computer vision. In response, the vision Transformer (vi $T$ ) emerges as a successful adaptation for image classification tasks. While viT exhibits tremendous potential in revolutionizing computer vision, addressing its inherent chal-lenges and limitations stands as a critical endeavor. This compre-hensive survey meticulously scrutinizes the drawbacks associated with viT, proposing bespoke adaptations tailored to specific applications while showcasing their remarkable performance across diverse visual tasks. Moreover, it delves into the evolution of viT adaptations across various visual domains, elucidating four promising directions for future research and development in this dynamic field.

关键词： Surveys Computer vision visualization Computational modeling Transformers Real-time systems Data models

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