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检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

共 5156 条记录，以下是461-470 订阅

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Detection of images for Video Based Spectroscopy by Using the Combination of Saliency Methods 4

Detection of Images for Video Based Spectroscopy by Using th...

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Kumar, Kaushal Bhushan Agarwal, Bharat Computer Science & Engineering Department India

ISBN: (纸本)9798350364729

Depth perception affects 2D image brightness, color, texture, and motion. In addition to stereoscopic vision, depth range, displaying size, 3D visualization, naturalness, and visual comfort can reconstruct 3D depth information. these features do not affect 2D visual attention. Replicating human visual attention in 3D situations requires saliency prediction methods designed for 3D environments. Stereoscopic videos' visual contrasts and object movements make saliency detection difficult. Depth map data is essential for saliency detection. For practical use, stereoscopic data is better than 2D. As visual scenes get more complex, 2D data alone cannot identify significant items. Depth maps help distinguish related items in complex situations. We use spatiotemporal correlation and depth information refinement to overcome these problems in this saliency detection investigation. Comparative analytical analysis of a baseline spatiotemporal depth correction system for multimodal saliency detection frameworks supports the basic premise. We chose two more saliency detection models: a cutting-edge multimodal filter transformer-based approach and a two-stream hierarchical model. We then compared these models to the baseline. We found that the spatiotemporal saliency model is effective due to its higher mean absolute error (MAE) values. Saliency models show that this method reduces visual region estimation and object detection mistakes. the spatiotemporal depth correction strategy is proven necessary and successful by this method. © 2024 IEEE.

关键词： Stereo image processing

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IPMV 2022 - 2022 4th international conference on image processing and Machine vision

IPMV 2022 - 2022 4th International Conference on Image Proce...

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4th international conference on image processing and Machine vision, IPMV 2022

ISBN: (纸本)9781450395823

the proceedings contain 19 papers. the topics discussed include: infrared dim and small target detection based on total variation and multiple noise constraints modeling;infrared small target detection algorithm with complex background based on YOLO-NWD;masked face recognition with 3D facial geometric attributes;group sparse-based discriminative feature learning for face recognition;CLAMOT: 3D detection and tracking via multi-modal feature aggregation;research on the application of three-dimensional digital model in the protection and inheritance of traditional architecture: take the example of the ma tau wall of Huizhou architecture;hierarchical iris image super resolution based on wavelet transform;an improved dark channel prior defogging algorithm based on transmissivity image segmentation;image processing based scoring system for small arms firing in the military domain;and a quantitative comparison of automated cleaning techniques for web scraped image data of ‘smart cities’.

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Medicinal Plants Attribute Detection by Deep learning image processing Techniques 4

Medicinal Plants Attribute Detection by Deep learning Image ...

引用

4th international conference on Communication, Computing and Industry 6.0, C216 2023

作者： Chamanth, S. Begam, M. Farida Chandan, N.S. Girish, G.S. Department of Information Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (纸本)9798350327328

the identification of medicinal plant species leads to many benefits to wide range of stakeholders such as, forestry services, taxonomists, doctors, and pharmaceutical labs, organizations that protect endangered species, the government, and the general public. Important plant species are disappearing around the world on a daily basis. Steps include plant identification, restoration, and protection in order to control the phenomenon. Typically, leaves are used to identify plants. Convolutional Neural Network which is the widely used architecture for image base classification motivated us to creating automated methods for classifying various plant species and identify medicinal attributes of the plants. In this paper, using computer vision and machine learning approaches, a fully automated front end based software system has been designed and described for achieving this. Each leaf's length, width, perimeter, area, number of vertices, color, perimeter, and hull area were all just a few of the many features that were taken out. then, from these properties, a number of derived features were calculated. the native people are expected to get more knowledge about medicinal leaves with the use of this work for the automatic recognition of medicinal leaves. Additionally, it helps taxonomists in the development of more effective methods for identifying species, which will greatly help in the preservation of endangered species. the utilization of various plant traits ranges from the most basic, such as leaf color and form, to the most complicated, such as tissue and cell structure are applied. the results obtained with these analytics gives 98% accuracy in 100 epochs. © 2023 IEEE.

关键词： Convolution

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Graph Data Augmentation for Node Classification 26

Graph Data Augmentation for Node Classification

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26th international conference on pattern recognition / 8th international Workshop on image Mining - theory and Applications (IMTA)

作者： Wei, Ziyu Xiao, Xi Zhang, Bin Hu, Guangwu Li, Qing Xia, Shutao Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Chinese Acad Sci Inst Informat Engn Key Lab Network Assessment Technol Beijing Peoples R China Peng Cheng Lab Dept New Networks Shenzhen Peoples R China Shenzhen Inst Informat Technol Shenzhen Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In recent years, Graph Neural Networks (GNNs) have emerged as powerful techniques for graph-structure data, which are essential for a wide range of graph-based tasks like link prediction and node classification. However, over-smoothing and over-fitting are two main challenges that impact negatively on model performance. Data augmentation is a good solution to these two problems, and it is also proven very effective in computer vision and nature language processing. But there is a relatively small body of literature when it comes to graph data augmentation. In this paper, we propose a Graph Data Augmentation (GDA) strategy to optimize the graph topology for node classification tasks. Our GDA approach consists of two operations: edge manipulation based on similarities of node pairs (GDA-E) and new nodes addition to under-informed old nodes (GDA-N). GDA-E is designed to add missing edges and remove noisy edges, while GDA-N is established to help nodes with low degree. Both operations can improve the information-to-noise ratio of the whole graph and lead to better performance of GNNs. the comparative results of experiments on three different datasets show that our GDA approach achieves considerable improvement (11.0% average) over origin graphs, and the ablation study verifies the effectiveness of both GDA-E and GDA-N.

关键词： computer vision Computational modeling computer architecture Benchmark testing Graph neural networks Topology pattern recognition

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Intelligent Classification System for Recyclables based on image and Acoustic processing 4

Intelligent Classification System for Recyclables based on I...

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

作者： Lu, Hanning Quan, Letian Shi, Jia Hangzhou Dianzi University School of Communication Engineering Hangzhou310000 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Chongqing Medical University School of Medical Information Engineering Chongqing400038 China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

It is of great significance to classify garbage through deep learning and image processing technology to realize garbage recycling and resource reuse. We propose a garbage classification system based on improved Faster R-CNN with audio-visual combination to achieve higher classification accuracy. We have introduced a new deep learning based system to effectively combine image and sound features for garbage classification. Based on this model, we implement an automatic garbage classification program with a graphical interface. We tried to add transparency and audio features for garbage sorting. the result shows that our method has high recognition accuracy. © 2022 IEEE.

关键词： image recognition

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Comprehensive study of image processing techniques for low-altitude target recognition

Comprehensive study of image processing techniques for low-a...

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

作者： Jianming Shao Ya Yu Zheng Xu Chen Ye Minghao Yan Yuhao Tong State Grid Wuxi Power Supply Company Wuxi China Jiayuan Technology Co. Ltd Nanjing China

this article presents a comprehensive overview of image processing techniques for low-altitude target recognition. the analysis includes various algorithms that incorporate shape, texture, and motion features, highlighting their advantages, disadvantages, and applicability. Additionally, this article explores multiple methods for low-altitude target recognition based on multi-feature fusion, comparing their advantages and disadvantages, and discussing future technological development directions. the article serves as a valuable reference for researchers interested in low-altitude target recognition, providing insights on algorithm selection and the application of multi-feature fusion. the presented research is expected to contribute to the advancement of low-altitude target recognition technology.

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Performance and Cost Balancing in vision Transformer-Based image Captioning 4

Performance and Cost Balancing in Vision Transformer-Based I...

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4th Asia Service Sciences and Software Engineering conference, ASSE 2023

作者： Lyu, Yan Liu, Yong Zhao, Qiangfu School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Fukushima965-8580 Japan

ISBN: (纸本)9798400708534

image captioning connects computer vision and natural language processing. Many deep learning models have been proposed for solving this problem, such as Transformer-based, CLIP-based and Diffusion-based models. However, the primary focus has been on increasing the accuracy of generating human-like descriptions for given images, leading to expensive SOTA models that cannot be implemented on computation-limited devices. For better performance, we propose a ViT-LSTM model for solving image captioning tasks by addressing the challenge of long-range dependencies. Our model consists of a ViT model pre-trained using the imageNet 21k dataset, which captures global context, and an LSTM that generates captions reflecting both local and global visual cues. Additionally, we use convolutional layers to reduce feature map dimensionality while preserving spatial relationships and local patterns. this allows the model to understand local details and relationships between regions, achieving better performance with less computational cost. © 2023 ACM.

关键词： Signal encoding

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Rice Leaf Disease Detection Using image processing Techniques 4th

Rice Leaf Disease Detection Using Image Processing Technique...

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4th international conference on Data, Engineering, and Applications, IDEA 2022

作者： Gayathiri, P. Nirmala College for Women Coimbatore641018 India

ISBN: (纸本)9789819724505

One of the factors that have contributed significantly to human civilization's development is agriculture. Automation has started to control every industry in this modern era. However, compared to other industries, farm automation is not as sophisticated. As a result, the need for an automation system in agriculture is pressing. Finding illnesses in crops at an early stage of growth is one of the main problems that farmers confront. this research aims to develop an autonomous drone to identify diseases at the earliest and prevent the crop from diseases. Plant or crop diseases play a significant role in both value and amount in agriculture, it is significantly effective to identify and diagnose these disorders. A significant scientific difficulty is a proper classification with small datasets in machine learning. Disease categorization, image processing, pattern recognition, and disease detection are all accomplished using the neural network whale optimization method. the neural network whale optimization is suggested in this research study as a way to identify rice leaf diseases. this study presented 5932 on-field photos of four different rice leaf diseases, including bacterial blight, blast, brown spot, and tungro. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural networks

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Non-uniform Piecewise Linear Activation Functions in Deep Neural Networks 26

Non-uniform Piecewise Linear Activation Functions in Deep Ne...

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26th international conference on pattern recognition / 8th international Workshop on image Mining - theory and Applications (IMTA)

作者： Zhu, Zezhou Dong, Yuan Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Recently, PWLU has been proposed to learn specialized activation functions with straightforward piecewise linear definition and SOTA performance in different vision tasks and neural networks. However, the uniformly distributed intervals strongly limit the flexibility of PWLU, and the definition of PWLU requires the statistic-based realignment method to handle the misalignment between PWLU and input data. this paper proposes a new piecewise linear activation function called Nonuniform Piecewise Linear Unit (N-PWLU). N-PWLU has two advantages to overcome the drawbacks in PWLU. First, nonuniformly distributed intervals are used to increase flexibility. Second, the cumulative definition establishes close connections to the parameters in different intervals, which helps alleviate the misalignment issue. With these advantages, N-PWLU significantly outperforms PWLU, especially with fewer intervals. For example, on imageNet classification dataset, 4-interval N-PWLU outperforms 4-interval PWLU with 1.15% top-1 accuracy in MobileNetV3. Besides, the expressivity of 4-interval N-PWLU is compatible with 16-interval PWLU in different datasets and architectures. Fewer intervals simplify the computation of N-PWLU, which is friendly to be deployed on edge devices. We believe that our NPWLU gets a step further in learning better parametric activation functions.

关键词： Performance evaluation Deep learning image edge detection Computational modeling Neural networks Distributed databases computer architecture

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Indoor Fire Detection System Based on Multi-sensor Information Fusion Algorithm 4

Indoor Fire Detection System Based on Multi-sensor Informati...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Wang, Qingjuan Lin, Jinyue Zhou, Yan Luo, Huibin Zhao, Ren College of Computer Beijing Institute of Technology Zhuhai Zhuhai China Zhuhai Microfence Technology Co. Ltd Rail Fire Robot Project Department Zhuhai China

ISBN: (纸本)9798350324303

In order to detect fires in the first place and use modern science and technology to effectively reduce and prevent disasters, four environmental parameters are collected and the fire status is determined by information fusion algorithms in this paper. In the information fusion framework of the system, the Naive Bayesian algorithm and the D-S evidence theory algorithm are used as the first level decision layer. the fuzzy comprehensive evaluation method is used as the second level decision layer of the framework, and a percentage rating value is used to make a determination of the current environmental state. the hardware part of the system uses ESP8266 as the microcontroller for processing the collected sensing signals. the Raspberry Pi is used as the master controller to handle the input signals and the effective linkage of the information fusion framework. After testing, this system can realize the determination of fire status in real environment and can make more effective, scientific, sensitive and low-cost detection results for fire detection. © 2023 IEEE.

关键词： Fires

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