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Design and Implementation of an image recognition System Based on the Embedded AI SOC Chip YuLong810A

Design and Implementation of an Image Recognition System Bas...

引用

Electronic Information Engineering and computer Science (EIECS), 2021 international conference on

作者： Zhiguang Lei Xin'An Qiu Zhiyong Lei Zhijun Lei Lanzhou Institute of Physics Lanzhou China Beijing Racobit Electronic Information Technology Co. Ltd. Beijing China China Electronics Technology Group Corporation 58th Research Institute Wuxi China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

To enhance real-time image recognition capabilities, an efficient embedded image recognition system was designed using the YuLong810A chip. By optimizing the hardware platform, image preprocessing, feature extraction algorithms, and classification models, the system achieves high-precision image recognition and classification. Experimental results indicate that the system significantly outperforms traditional methods in various performance metrics, demonstrating exceptional accuracy and stability. this suggests a broad range of applications in the field of intelligent vision.

关键词： Measurement image recognition Accuracy Stability analysis Hardware Robustness Real-time systems Classification algorithms System-on-chip image preprocessing

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Hybrid Edge Detection-Based Approach for Enhanced Text Extraction from image

Hybrid Edge Detection-Based Approach for Enhanced Text Extra...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Akash Burnwal Mamata P. Wagh Nishi Choudhary Saurav Kumar Computer Science And Engineering C.V. Raman Global University Bhubaneswar India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Text extraction from images plays a crucial role in applications such as document digitization, automated data extraction, and computer vision-based text processing. this paper introduces a multi-stage pipeline to enhance text recognition accuracy through preprocessing, Hybrid Edge Detection (HED), and Optical Character recognition (OCR). Preprocessing removes noise using Gaussian blurring, followed by grayscale conversion to improve contrast. the HED phase combines Canny and Sobel techniques to detect text contours, generating a hybrid edge map by applying an OR operation on the outputs of both methods, preserving all detected edges. this fused edge map is then processed through OCR to extract character patterns. In scenarios where external noise was deliberately introduced to test the robustness of various approaches, traditional methods failed to detect text effectively, whereas the proposed model demonstrated resilience in adverse conditions. the proposed HED scheme enhances the accuracy of text extraction, achieving the highest Word Match Accuracy of 83.3%.

关键词： Accuracy Text analysis image analysis image edge detection Computational modeling Optical character recognition Noise Pipelines Robustness Integrated circuit modeling

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Detection of Pathological Myopia from Fundus images 1

引用

4th international conference on Communication and Intelligent Systems, ICCIS 2022

作者： Ali, Sarvat Raut, Shital Visvesvaraya National Institute of Technology Nagpur India Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India

ISBN: (数字)9789819921003

ISBN: (纸本)9789819920990

Pathological myopia (PM), which results from degenerative changes in the sclera, choroid, and retinal pigment epithelium (RPE), is associated with irreversible vision loss. this study proposes automatically detecting PM or normal vision from input retina fundus image. We have experimented with various transfer learning models and the pre-processing steps using reinforcement learning (RL). the best results were achieved with our custom ResNet50 as a baseline model. It has achieved an AUC score of 0.9984 on the validation dataset provided by the PALM challenge, a Satellite Event of the IEEE international Symposium on Biomedical Imaging in Venice, Italy. this AUC score is among the top 3 performers in this challenge. As in medical domain, more accurate results are always in demand, and this score ensures that the model can be set up for a clinical application in future as a second opinion to ophthalmologists. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ophthalmology

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Deep learning-based vehicle tracking and traffic event detection

Deep learning-based vehicle tracking and traffic event detec...

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computer vision, image and Deep Learning (CVIDL), international conference on

作者： Guohao Sun Kehuan Yan Chengbin Fan School of Mathematics Tianjin University Tianjing China School of Computer and Data Science Fuzhou University Fujian China School of Electrical Engineering and Automation Xiamen University Tan Kah Kee College Fujian China

Time-series data mining plays an important role in big data decision making because it can reveal the development pattern of things. Similar concatenation of temporal data is a fundamental prerequisite for data twinning., whose core objective is to find all similar temporal data pairs according to a given similarity metric. Dynamic temporal regularization (DTW) has been widely used in many fields., such as target detection., trend prediction and fault identification., as the best data alignment method on temporal data. We use deep learning to extract vehicle trajectories and perform behavioral pattern learning., and finally use an improved DTW algorithm to pre-process the trajectory data and solve the distance function to achieve matching between the trajectories of the event sequence to be measured and the typical trajectory data patterns. By comparing the indicators with the unimproved DTW algorithm., the research results show that this traffic condition recognition method is stable and reliable., and can maintain high matching accuracy with significantly reduced computation., high success rate and good real-time performance.

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Research on Machine vision Intelligent Detection System Based on Faster R-CNN

Research on Machine Vision Intelligent Detection System Base...

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Data Science and computer Application (ICDSCA), IEEE international conference on

作者： Xueting Wang Weizhao Song Huitong He Fanshuo Zeng Zhipeng Zhang Zheyuan Zhou Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

With the rapid development of artificial intelligence technology, visual inspection and image processing algorithms have been continuously improved in accuracy and efficiency, and intelligent inspection systems based on machine vision have been widely used in product quality control, defect detection and process monitoring. In this paper, a machine vision intelligent inspection system based on Faster Region-based Convolutional Network (Faster R-CNN) is proposed, which aims to improve the detection accuracy and efficiency. In this paper, the overall architecture of the system is introduced, including the image acquisition module, the data preprocessing module and the detection module based on Faster R-CNN. In the data preprocessing module, the Deep Convolution Generative Adversarial Networks (DCGAN) algorithm was proposed for image expansion generation, and high-resolution image samples that were basically consistent with the collected image datasets were obtained. then, the Faster R-CNN model was optimized and adjusted for specific application scenarios, so that the average accuracy (mAP) of the model reached 78.23% and the FPS value reached 11.0.

关键词： Process monitoring Visualization Accuracy image recognition Machine vision Computational modeling Data preprocessing Inspection Product design Quality assessment

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3D Dual-stream Convolutional Neural Networks with Simple Recurrent Unit Network: A New Framework for Action recognition 4

3D Dual-stream Convolutional Neural Networks with Simple Rec...

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4th international conference on Communications, Information System and computer Engineering, CISCE 2022

作者： Zhai, Jiecheng Yao, Xunxiang Dong, Guangyuan Jiang, Qun Zhang, Yunfeng Shandong University of Finance and Economics Key Laboratory of Digital Media Technology Shandong University of Finance and Economics College of Computer Science and Technology Jinan China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

Human action recognition is a supervised process of labeling an entire video image sequence with action labels in computer vision fields. Different from the recognition of static images, this process also needs to learn the contact information between video frames, such as the timing characteristics that reflect the changes of actions in the video. In existing deep learning methods, due to the size of the convolution kernel, the models use a small number of consecutive frames as input and trains to assign feature vectors to short sequences instead of the entire sequence. therefore, even if the learned features contain time information, their evolution over time will be completely ignored. In this work, we propose a dual-stream deep fusion framework that can fully utilize the long-term information of a video. We preprocess the video into static frames and optical flow graphs and input them into a three-dimensional convolutional neural network to obtain the spatiotemporal feature stream with time series. then, the spatiotemporal feature stream is input into a simple recurrent unit network to learn the long-term sequence features of the time dimension. Finally, SoftMax classifier is used for feature classification. We tested our model on the classic action recognition UCF-101 and HMDB-S1 data sets, and our model achieved better performance than existing methods. © 2022 IEEE.

关键词： Convolutional neural networks

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Element-Wise Layer Normalization for Continuous Signal Representation 23

Element-Wise Layer Normalization for Continuous Signal Repre...

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15th international conference on Digital image processing, ICDIP 2023

作者： Chen, Weifeng Ding, Hui He, Zhifen Li, Bo Liu, Bin Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Jiangxi Science and Technology Infrastructure Center Jiangxi Computing Center Jiangxi Computing Center Nanchang China

ISBN: (纸本)9798400708237

Implicit neural representation (INR), sometimes also referred to coordinate-based representation or fitting, has gained the state-of-the-art performance in numerous research fields including computer vision and computer graphics due to the powerful continuous representation ability. Recent researches indicate that Fourier embedding is critical for INR to fit realistic images with high-frequency details. However, is Fourier embedding all we need for high-frequency coordinate (image) fitting? In this paper, we revisit the problem of coordinate fitting from a novel perspective of distribution mapping. Fourier embedding, as a preprocessing step of coordinate fitting, essentially performs the operation of mapping the uniform coordinate distribution to a normal distribution, and makes the learning of mapping function between two similar smooth distribution become easier. However, the number of discrete Fourier basis function affects the fitting performance dramatically and cannot be determined automatically. Based on the above analysis, a simple yet efficient INR coordinate fitting method is proposed in this paper, which demonstrates that the Fourier embedding is not the only way to improve INR. the proposed method only adds an element-wise layer normalization (ELN) module to the vanilla multi-layer perception (MLP) with ReLU activation. Experimental results on public database demonstrate that the proposed method outperforms the state-of-the-art methods using Fourier embedding. © 2023 ACM.

关键词： Mapping

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Pipe thread Parameter Detection System Based on Machine vision 4

Pipe Thread Parameter Detection System Based on Machine Visi...

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2022 4th international conference on Artificial Intelligence and computer Science, AICS 2022

作者： Li, Yingying Wu, Yuxiu Zhang, Donghong School of Electrical and Information Engineering Anhui University of Technology Anhui Maanshan China

In order to achieve non-contact pipe thread detection with high-precision, high-efficiency and make the use of pipe threads safer, using machine vision and image processing technology to measure and analyze the geometric parameters of pipe threads. the projection matrix and distortion coefficient of the camera are obtained by Zhang Zhengyou's calibration method, and the distortion coefficient is used to correct the distortion of the collected image. After that, grayscale processing is performed on the RGB (Red, Green, Blue) image obtained by the collection and correction, fusion filtering and denoising, threshold segmentation to obtain the binary image, and then the Canny operator is used to detect the thread edge information on the binary image. then the pipe thread parameter measurement algorithm is proposed based on the extracted pipe thread image characteristics. Finally, the feasibility of the measurement algorithm is verified by using the measured data of 60 sets of pipe threads. the test results show that the measured values of tooth height and crest height obtained by this algorithm are concentrated near the standard value, with high accuracy and precision, and the overall data fluctuation is small. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： computer vision

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A Benchmark and Chain-of-thought Prompting Strategy for Large Multimodal Models with Multiple image Inputs 27th

A Benchmark and Chain-of-Thought Prompting Strategy for Larg...

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27th international conference on pattern recognition, ICPR 2024

作者： Zhang, Daoan Yang, Junming Lyu, Hanjia Jin, Zijian Yao, Yuan Chen, Mingkai Luo, Jiebo University of Rochester Rochester United States Nanjing University of Posts and Telecommunications Nanjing China New York University New York United States Stony Brook University Stony Brook United States

ISBN: (纸本)9783031784552

When exploring the development of Artificial General Intelligence (AGI), a critical task for these models involves interpreting and processing information from multiple image inputs. However, Large Multimodal Models (LMMs) encounter two issues in such scenarios: (1) a lack of fine-grained perception, and (2) a tendency to blend information across multiple images. To better investigate the capability of LMMs to perceive fine-grained visual details when dealing with multiple input images, we built a benchmark for evaluating LMM with multiple image inputs - MIMU(Muti-image Inputs Multimodal Understanding Benchmark). the benchmark focuses on two scenarios: first, image-to-image matching (to evaluate whether LMMs can effectively reason and pair relevant images), and second, multi-image-to-text matching (to assess whether LMMs can accurately capture and summarize detailed image information). We conduct evaluations on a range of both open-source and closed-source large models, including GPT-4V, Gemini, OpenFlamingo, and MMICL. Although GPT-4V achieves the best results in all metrics, it still has a significant gap from Human Evaluation. To enhance model performance, we further develop a Contrastive Chain-of-thought (CoCoT) prompting approach based on multi-input multimodal models. this method requires LMMs to compare the similarities and differences among multiple image inputs, and then guide the models to answer detailed questions about multi-image inputs based on the identified similarities and differences. Our experimental results showcase CoCoT’s proficiency in enhancing the multi-image comprehension capabilities of large multimodal models. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligence

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Golf Swing Sequencing Using computer vision 10th

Golf Swing Sequencing Using Computer Vision

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10th Iberian conference on pattern recognition and image Analysis (IbPRIA)

作者： Marais, Marc Brown, Dane Rhodes Univ Dept Comp Sci ZA-6140 Grahamstown South Africa

ISBN: (纸本)9783031048814;9783031048807

Analysis of golf swing events is a valuable tool to aid all golfers in improving their swing. image processing and machine learning enable an automated system to perform golf swing sequencing using images. the majority of swing sequencing systems implemented involve using expensive camera equipment or a motion capture suit. An image-based swing classification system is proposed and evaluated on the GolfDB dataset. the system implements an automated golfer detector combined with traditional machine learning algorithms and a CNN to classify swing events. the best performing classifier, the LinearSVM, achieved a recall score of 88.3% on the entire GolfDB dataset when combined with the golfer detector. However, without golfer detection, the pruned VGGNet achieved a recall score of 87.9%, significantly better (>10.7%) than the traditional machine learning models. the results are promising as the proposed system outperformed a Bi-LSTM deep learning approach to achieve swing sequencing, which achieved a recall score of 76.1% on the same GolfDB dataset. Overall, the results were promising and worked towards a system that can assist all golfers in swing sequencing without expensive equipment.

关键词： Golf swing sequencing Histogram of oriented gradients Linear Discriminate Analysis Support Vector Machines Gradient Boosting Machines Deep learning

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