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Frame-Adaptive Multi-Object Tracking for Discontinuous image Sequences 2

Frame-Adaptive Multi-Object Tracking for Discontinuous Image...

引用

2nd International conference on Algorithm, image processing and machine vision, AIPMV 2024

作者： Li, Xiaolei Xiao, Xingjie Yang, Siyuan Sha, Moquan Sha, Zongyao Tu, Jianguang School of Remote Sensing and Information Engineering Wuhan University Wuhan China Wuhan Luojia Zhongheng Remote Sensing Data Technology Co Wuhan China China Unicom Smart City Research Institute Beijing China

ISBN: (纸本)9798350390254

In the field of multi-object tracking, this study introduces an innovative framework designed to address the challenges posed by frame loss in image sequences, particularly within the contexts of video surveillance and traffic management. Despite the significant achievements of deep learning-based tracking methods in processing high frame rate videos, frame loss remains a prevalent issue in real-world deployments due to network and hardware constraints, which can result in erroneous target associations and interrupted trajectory tracking. Our research begins by detecting frame loss through a timestamp- based analysis, this is followed by the deployment of an adaptive algorithm that fuses spatial and appearance metrics to compensate for the positional discrepancies and visual inconsistencies caused by missing frames, thus improving the precision of data association. We have curated a dataset that accurately reflects real-world environments, and conducted validation on subsampled versions of the MOT17 and MOT20 datasets, in addition to our original dataset. Empirical results indicate that our multi-object tracking approach demonstrates significant adaptability and robustness in handling discontinuous image sequences, reducing the number of ID switches by 54.1 % on our original dataset compared to state-of-the-art methods. The proposed framework has the potential to significantly enhance the performance of multi-object tracking systems in real-world scenarios where frame loss is a common occurrence, thereby improving the reliability and accuracy of video surveillance and traffic management applications. © 2024 IEEE.

关键词： Adaptive algorithms

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Unveiling the vision: A Comprehensive Review of Computer vision in AI and ML

Unveiling the Vision: A Comprehensive Review of Computer Vis...

引用

2024 International conference on Advances in Data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Laad, Meena Maurya, Ratan Saiyed, Najeeb Symbiosis Institute Of Technology Dept. Of Applied Science Maharashtra Pune India Symbiosis Institute Of Technology Dept. Of Artificial Intelligence And Machine Learning Maharashtra Pune India

ISBN: (纸本)9798350364828

In the era of rapid technological advancement, Computer vision has emerged as a transformative force, reshaping the landscape of Artificial Intelligence (AI) and machine Learning (ML). This comprehensive review paper aims to delve into the intricate evolution, methodologies, applications, challenges, and future trajectories of Computer vision. Moving beyond a mere exploration of technical intricacies, our objective is to present a holistic narrative that encapsulates the profound impact of computer vision on AI and ML and its repercussions across society. The journey begins by traversing the philosophical and historical roots of Computer vision, unraveling the threads that connect human visual perception to the development of artificial vision. By exploring the historical evolution from early image processing to the current era of deep learning, we seek to elucidate the intellectual milestones that have shaped the field. Methodologically, this paper navigates through both traditional approaches and contemporary deep learning paradigms. It dissects traditional methods, emphasizing their enduring relevance and influence on modern Computer vision applications. In parallel, exploring deep learning delves into established architectures all the nuanced impact of design choices on interpretability and explain ability. Applications form a cornerstone of our review, with an enriched focus on case studies that spotlight the transformative influence of Computer vision. Beyond the traditional domains of image recognition, we delve into the healthcare renaissance, where Computer vision contributes to diagnostics, drug discovery, and personalized medicine. Furthermore, we explore its role in smart cities, extending beyond surveillance to urban planning, traffic management, and environmental monitoring. © 2024 IEEE.

关键词： Computer vision

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Visual Recognition Method of Road Sign Line Missing Based on image High Resolution Reconstruction Algorithm 2

Visual Recognition Method of Road Sign Line Missing Based on...

引用

2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Li, Hong Hussin, Norriza Pingdingshan University College of Information Engineering Pingdingshan467002 China SEGi University Faculty of Engineering Built Environment and Information Technology Kota Damansara47810 Malaysia

ISBN: (纸本)9798350331417

The conventional visual identification method of road sign line loss mainly uses Sobel\Prewitt operator to dynamically divide the visual identification area, which is easily affected by the directional change of road signs, resulting in poor visual identification performance. Therefore, it is necessary to design a brand-new visual identification method of road sign line loss based on image high-resolution reconstruction algorithm. That is to say, the visual recognition features of road sign line missing are extracted, and the visual recognition algorithm of road sign line image super-resolution reconstruction is designed, thus realizing the visual recognition of road sign line missing. The experimental results show that the designed visual recognition method for high-resolution reconstruction of road marking line images has good recognition performance, accuracy and certain application value, and has made certain contributions to improving the safety of road traffic. © 2023 IEEE.

关键词： Identification Identification line image high resolution reconstruction algorithm Missing Road vision Way

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Visible-Infrared Person Re-identification Based on Deep-Shallow Spatial-Frequency Feature Fusion 24

Visible-Infrared Person Re-identification Based on Deep-Shal...

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1st International conference on image processing machine Learning and Pattern Recognition

作者： Chen, Yumin Che, Jin Yang, Yingying Ningxia Univ Ningxia Key Lab Intelligent Sensing Desert Inform Sch Elect & Elect Engn Yinchuan Ningxia Peoples R China

ISBN: (纸本)9798400707032

Visible-Infrared Person Re-identification (VI-ReID) is a challenging task of cross-modality person retrieval. Traditional approaches, hindered by significant inter-modality variations, have predominantly targeted the extraction of shared features in the network's final output layer, often overlooking the valuable shallow-level information. To counter this, the paper introduces a novel framework for VI-ReID, the Deep-Shallow Spatial-Frequency Feature Fusion (DSSF3), which prioritizes the integration of rich, multi-level features. Primarily, the Four-Stream Feature Extraction network (FSFE) is expertly crafted to bridge the gap between visible and infrared images, bolstering the network's fine-grained semantic feature extraction via strategic data augmentation. Concurrently, this paper proposes the Spatial-Frequency Fusion Module (SFFM), which adeptly captures critical spatial and frequency domain details that are commonly neglected during training. The results on two public datasets demonstrate a significant improvement of the proposed method.

关键词： Computer vision Visible-infrared person re-identification Four-Stream Feature Extraction Spatial-Frequency Fusion Module

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Segmentation and Quantitative Evaluation of Tertiary Lymphoid Structures in Hepatocellular Carcinoma Based on Deep Learning 2

Segmentation and Quantitative Evaluation of Tertiary Lymphoi...

引用

2nd International conference on Algorithm, image processing and machine vision, AIPMV 2024

作者： Li, Rong Chen, Rui Yue, Jingwen Liu, Le Jia, Zijian School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798350390254

Tertiary lymphoid structures (TLS) present in hepatocellular carcinoma (HCC) significantly impact tumor initiation and progression, holding significant clinical implications for the treatment and prognosis of patients, but there is limited research focusing on its identification and pathological quantitative analysis in present. In this study, we devised the CBAM-Segformer model, an enhanced iteration of the Segformer deep learning algorithm, and conducted experiments using 1460 patches containing tertiary lymphoid structures of hepatocellular carcinoma to segment the tertiary lymphoid structures regions. Experimental findings affirmed the efficacy of the proposed method in accurately segmenting and detecting tertiary lymphatic structures within hepatocellular carcinoma pathological images, outperforming comparative algorithms. The attained metrics, including Accuracy, MPrecision, MIoU and MPA recorded values of 0.9921, 0.9244, 0.8845 and 0.9467, respectively, underscoring the superior performance of our approach in segmentation task. Ultimately, we conducted calculations on the area of segmented intratumoral and peritumoral tertiary lymphoid structures, then produced histogram and boxplot to illustrate the area distribution of tertiary lymphoid structures. Our methodology provides conducive framework for medical personnel to analyze tertiary lymphoid structures of hepatocellular carcinoma with enhanced expediency and efficacy, thereby facilitating further therapies and prognostic assessments of hepatocellular carcinoma. © 2024 IEEE.

关键词： image segmentation

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Design Of An Improved Dish Recognition Algorithm Based On image Retrieval 2

Design Of An Improved Dish Recognition Algorithm Based On Im...

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2nd International conference on machine vision, image processing and Imaging Technology, MVIPIT 2024

作者： Yu, Yi Liu, Jie Sebial, Archival Canillo, Angie Ceniza Jiangxi Vocational College of Industry and Engineering China Department of Computer Information Sciences and Mathematics School of Arts and Sciences University of San Carlos Cebu Philippines

ISBN: (纸本)9798331543037

With the booming of the new retail industry, unmanned billing systems have become the key to improve the operational efficiency of restaurants and reduce labour costs. In the field of payment and billing, traditional recognition systems based on deep neural networks often rely on a single network to achieve the detection and classification of Chinese dishes, but in the face of the increase or decrease in the number of dishes, it is necessary to frequently adjust the type of the network output and re-collect a large amount of image data for training, which leads to high update costs. In this study, a deep learning EfficientNet convolutional neural network combined with image retrieval for dish recognition is used to solve this problem. Firstly, EfficientNet, an advanced dish detection network, is used to accurately locate the positions of multiple dishes in an image, determine their orientations, and effectively separate the dishes from the background. Subsequently, the EfficientNet based feature extraction network calculates the image features of the separated dishes and uses cosine similarity as a metric to efficiently retrieve the dishes in the reference dish library, thus obtaining the classification results of the dishes. Finally, the system outputs the precise location and category information of the dishes. Experimental results show that the EfficientNet and cosine similarity approach achieves an average accuracy of 98.3% on the dish image dataset of China Food Network, which not only significantly shortens the updating time compared with traditional algorithms, but also requires only 1/24 of the amount of image data of the traditional deep learning classification *** breakthrough effectively solves the problem of the difficulty and high cost of updating the current dish recognition algorithms. This breakthrough effectively solves the current problem of difficult and costly updating of dish recognition algorithms, and is expected to significantly redu

关键词： Convolutional neural networks

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An Improved Reversible Data Hiding Scheme Based on Referred Frequencies for a VQ Compressed Index 2

An Improved Reversible Data Hiding Scheme Based on Referred ...

引用

2nd International conference on machine vision, image processing and Imaging Technology, MVIPIT 2024

作者： Hwang, Min-Shiang Fatima, Kanza Wang, Yu-Lun Chan, Chi-Shiang Wu, Chia-Chun Department of Computer Science & Information Engineering Fintech and Blockchain Research Center Asia University Taichung Taiwan Department of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (纸本)9798331543037

Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. There are many compression methods, such as Huffman, VQ, ZIP, AMBTC, RAR, JPEG, etc. In this article, we propose an improved data hiding in VQ compression method to achieve better capacity and high quality. Experimental results show that our data-hiding approach is practical. ©2024 IEEE.

关键词： compressed image data hiding LSB matching reversible data hiding steganography vector quantization

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Safety risk assessment method of key personnel in infrastructure projects based on image data coupling identification

Safety risk assessment method of key personnel in infrastruc...

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2023 International conference on Algorithm, Imaging processing, and machine vision, AIPMV 2023

作者： Wang, Xiaodong Zhang, Linyu Xu, Zhiqiang Xiao, Hui Guo, Lixuan State Grid Information and Communication Industry Group Beijing100052 China State Grid Hunan Electric Power Company Hunan Changsha410004 China Anhui Jiyuan Software Co. LTD Anhui Hefei230088 China

ISBN: (纸本)9781510672444

In the course of work, people are easily affected by the complexity of the environment, so it is difficult to ensure the assessment of personnel safety risks. In view of this situation, a safety risk assessment method of key personnel in infrastructure projects based on image data coupling identification is proposed. Under the coupling recognition of image data, a comprehensive evaluation model is established by combining dynamic graph reasoning technology, and the relevant signals of the working environment of key personnel in infrastructure projects are extracted from the model, and images are obtained on the projection surfaces of the left terminal and the right terminal, so as to locate the positions of key personnel in infrastructure projects and realize the safety risk assessment of key personnel in infrastructure projects. The experimental results show that the average channel collision rate of the proposed method is low, which can greatly improve the accuracy of dangerous action identification and has good performance and effect. © 2024 SPIE.

关键词： Personnel

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Design of PLC visual control system based on web 10

Design of PLC visual control system based on web

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10th International conference on Mechanical Engineering, Materials, and Automation Technology, MMEAT 2024

作者： Li, Xian Zhu, Qingsong Lv, Shixia Gou, Weijie Wang, Xuelei Chen, Minghui Automotive Engineering College Beijing Polytechnic Beijing China Beidou Applied Development Research Institute Beijing China

ISBN: (纸本)9781510682634

A design scheme for a Web-based PLC visual control system is proposed, which integrates Web technology, PLC control, visual inspection and MATLAB image processing, to realize remote multi-camera control and monitoring of the visual system, and provides a scheme for improving the automation level of small and medium-sized enterprises. And conducting a discussion focused on the key technologies in the communication process between PLC and vision, MATLAB and Web. And based on this system, a set of Web pages using HTML markup language is developed. The experimental results show that the communication between PLC and vision, PLC and Web, PLC and MATLAB, MATLAB and vision is solved. With PLC as the control core, visual detection and image processing are achieved through Web control, and the results are fed back to PLC, while monitoring is presented through the Web. Compared with the traditional simple use of PLC and vision, the work efficiency of this system can be increased by 20%, and the correct identification rate of workpiece size can be increased by 30%. And greatly improves the intelligence of the system and provides solution support for industrial automation. © 2024 SPIE.

关键词： image enhancement

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The Ship Classification and Detection Method of Optical Remote Sensingimage Based on improved YOLOv7-tiny

The Ship Classification and Detection Method of Optical Remo...

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2023 International conference on Algorithm, Imaging processing, and machine vision, AIPMV 2023

作者： Cheng, Jinwei Yuan, Jie Hu, Xiaoning Xie, Baorong Wang, Junrui ShangHai Institute of Aerospace Electronics Technology Shanghai201109 China Beijing Institute of Remote Sensing Information Beijing100192 China

ISBN: (纸本)9781510672444

In view of the fault and leak detection problems caused by complex scenes of offshore area in remote sensing image ship detection, a lightweight ship classification detection method is proposed based on improved YOLOv7-tiny. On the one hand, this method stacks a lightweight feature extraction module and applies it to the backbone feature extraction network, which significantly reduces the parameter and computational complexity and does not weaken the network's ability of feature extraction. On the other hand, this method introduces spatial information into the feature pyramid, raising the discrimination of features at different scales, to improve the classification and detection ability of the network. This method has been tested on the remote sensing image ship data set. The experimental results show that the average accuracy of ship classification detection based on the improved network is increased by 2.9%. Meanwhile, the parameter quantity and computational complexity are better than YOLOv7-tiny, with a 15% reduction in parameter quantity and a 24% reduction in computational complexity. © 2024 SPIE.

关键词： Feature extraction

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