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共 8563 条记录，以下是61-70 订阅

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An enhanced feature matching multi-temporal port remote sensing image registration network E-SuperGlue

An enhanced feature matching multi-temporal port remote sens...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： He, Xinyu Feng, Shuyi Shao, Wenbo He, Hengxiang Wu, Liming Shanghai Aerospace Electronic Technology Institute Department of Aerospace Electronic Research Shanghai China

ISBN: (纸本)9781510680425

Multi-temporal collaborative analysis of port scenes can enhance the representation ability of image scenes, and image registration is required before multi-temporal analysis. In this paper, an image registration network E-SuperGlue with enhanced feature matching is proposed to solve the problems such as the difficulty of extracting feature points and matching feature descriptors for port multi-temporal image registration. Our network takes SuperGlue network as the basic framework. Firstly, Focus is introduced into the feature extraction network to increase the number to increase the number and detection rate of feature points. Secondly, LFPE module is added to feature matching network coding module to improve the information efficiency of feature descriptor coding. Finally, an improved multi-layer sensing structure E-MLP is added to the feature matching network to improve the utilization rate of channel information. © 2024 SPIE.

关键词： Graph neural networks

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2nd IEEE international conference.on advances in Information Technology, ICAIT 2024 - proceedings

2nd IEEE International Conference on Advances in Information...

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2nd IEEE international conference.on advances in Information Technology, ICAIT 2024

ISBN: (纸本)9798350383867

The proceedings contain 352 papers. The topics discussed include: PAVED: a fusion of deep reinforcement learning with biometric authentication for self-driving vehicles;exploring cutting-edge machine learning and data mining techniques for enhancing big data management with advanced algorithmic strategies for optimal data processing and analysis;interactive delta bot with voice recognition and image processing – a review;smart surveillance camera using AI;minimized computational time in cloud using advanced encryption standard algorithm over file changed with security;Kannada legal document summarizer: a survey;synergistic revolution with AI-enabled organ matching and blockchain security in transplantation management;utilizing YoLov8 architecture for sharp-edge segmentation;and learning-based super-resolution for image upscaling using sparse representation.

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Interactive Delta Bot with Voice recognition and image processing - A Review 2

Interactive Delta Bot with Voice Recognition and Image Proce...

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2nd IEEE international conference.on advances in Information Technology, ICAIT 2024

作者： Mallam, Madhavi Hemantha, G. Reddy Manu, H. Mounika, K. Bhoomika, P. Keerthana, H. Department of Electronics and Communication PES Institute of Technology and Management Karnataka India Department of Electronics and Communication Madanapalle Institution of Technology and Science Andra Pradesh India Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350383867

The paper is designed to build a voice-controlled pick-and-place bot using a Raspberry Pi. Python and other open-source software are programmed to the architecture of bots with Raspberry Pi commands. The robots perform the action with a voice assistant via voice input. The system recognizes the object with the aid of image processing and can lift a maximum weight of 750 grams with a sled arm movement of 360 degrees. This robot uses the TensorFlow USB Coral accelerator to increase Raspberry Pi object detection. Interfacing of the bot with sensors and cameras to identify items in its surroundings, analysing spatial characteristics through image processing. Speech recognition technology enables natural communication with the robot, allowing users to give specific commands. This human-robot interaction ensures intuitive control and an improved user experience. The system's architecture includes a processing unit, motor controllers, cameras, microphones, and other hardware and software components. Machine learning methods is utilized for voice recognition and image manipulation which is adaptive and enhances the performance. © 2024 IEEE.

关键词： Human robot interaction

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A deep learning image classification method and its application introducing hybrid attention mechanism

A deep learning image classification method and its applicat...

引用

2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Pan, Shaowei Han, Jinyun Guo, Zhi Zhang, Jiaqing Ju, Zebin School of Computer Science Xi'an Shiyou University Shaanxi Xi'an710065 China School of Information Technology Deakin University VIC3216 Australia Research Institute of Petroleum Exploration & Development PetroChina Beijing100083 China China National Logging Corporation Shaanxi Xi'an710077 China

ISBN: (纸本)9781510680425

We introduce a deep learning methodology that combines the Inception-ResNet-V2 architecture with a hybrid attention mechanism. This fusion enables the extraction of crucial information from both channel and spatial dimensions within feature maps, giving rise to the ECA-SA-Inception-ResNet model. To assess the performance of our model, we conducted experiments on a petrographic thin section image dataset utilizing Grad-CAM visualization techniques. The results demonstrate the model's proficiency in extracting essential features from petrographic thin section images, thereby resulting in substantial enhancements in image classification accuracy. © 2024 SPIE.

关键词： image classification

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Semantic feature integration network for fine-grained visual classification

Semantic feature integration network for fine-grained visual...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Wang, Hui Li, Yueyang Luo, Haichi Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China College of Internet of Things Engineering Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi China

ISBN: (纸本)9781510680425

Fine-Grained Visual Classification (FGVC) is a challenging task due to subtle differences among subordinate categories. Many current FGVC approaches focus on identifying and locating discriminative regions, but neglect the presence of unnecessary features that impair the understanding of object structure. These unnecessary features, including 1) ambiguous parts resulting from the visual similarity in object appearances and 2) noninformative parts (e.g., background noise), can have a significant adverse impact on classification results. To address this limitation, we propose the Semantic Feature Integration network (SFI-Net) to eliminate unnecessary information and reconstructing the semantic relations among discriminative features. Our SFI-Net are carefully designed and can be trained end-to-end in a weakly-supervised way. Extensive experiments on four challenging fine-grained benchmarks demonstrate that our work achieves the state-of-the-arts performance. Especially, the classification accuracy of our model on CUB-200-2011 and Stanford Dogs reaches 92.64% and 93.03%, respectively. © 2024 SPIE.

关键词： image classification

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Grottoes color analysis based on image processing and its application in environmental design 24

Grottoes color analysis based on image processing and its ap...

引用

3rd international conference.on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Zhang, Jing Han, Xinghui Zhao, Bo College of Art and Design Xian University of Technology China

ISBN: (纸本)9798400718120

This study aims to explore the grotto color analysis based on image processing and its application in environmental design. By using multispectral and hyperspectral imaging techniques, we collected and analyzed the color information of grotto murals in detail, applied to cultural heritage protection, modern architectural design and interior beautification. Color histogram, convolutional neural network (CNN), K-means clustering techniques were used to preprocess image data, feature extraction and pattern recognition. The results show that these techniques can effectively improve the accuracy of color restoration and provide scientific color application scheme for environmental design. © 2024 Copyright held by the owner/author(s).

关键词： Ecodesign

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Exploration of cervical cancer image processing technology based on deep learning

Exploration of cervical cancer image processing technology b...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Cheng, Cheng Yang, Yi Qu, Youshan Changchun University of Science and Technology 7089 Weixing Road Jilin Province Changchun City China The Second Norman Bethune Hospital of Jilin University No.218 Ziqiang Street Nanguan District Jilin Province Changchun City China Xi'an Institute of Optics and Precision Mechanics of CAS No.17 Information Avenue New Industrial Park Gaoxin District Xi'an China

ISBN: (纸本)9781510680425

The aim of this paper is to investigate cervical cancer image processing technology utilizing deep *** cancer stands as a prevalent malignancy in females, and precise identification and localization of cancer cells hold paramount significance for treatment and prognosis *** paper presents the fundamental workflow of cervical cancer image processing and the associated principles of deep learning, including convolutional neural networks, autoencoders, and generative adversarial *** recent times, the swift advancement of deep learning technology has brought forth novel concepts and approaches for cervical cancer image *** paper is oriented toward the exploration of cervical cancer image processing technology grounded in deep ***, the basic workflow of cervical cancer image processing, including steps such as image acquisition, preprocessing, feature extraction, and target detection, is *** application of deep learning in cervical cancer image processing is discussed in *** one of the core deep learning technologies, convolutional neural networks (CNNs) have achieved significant results in the fields of image classification, segmentation, and *** paper shall present the fundamental principles and prevalent architectures of CNNs, alongside their instances of utilization in cervical cancer image ***, the utilization of alternative deep learning approaches in cervical cancer image processing is also ***, the paper contrasts the strengths and weaknesses of diverse deep learning techniques in cervical cancer image processing and deliberates the challenges and future trajectories of development within this domain. © 2024 SPIE.

关键词： Convolutional neural networks

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Fast landmark image retrieval based on Gaussian mixture clustering

Fast landmark image retrieval based on Gaussian mixture clus...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Xie, Ziheng Yang, Shuang Zhengzhou University 97 Culture Road Zhengzhou450000 China

ISBN: (纸本)9781510680425

Due to the increasing scale of the reference landmark image database, the efficiency of image retrieval tasks is significantly reduced. In this work, we propose an image retrieval method based on feature clustering to improve the efficiency of large-scale image retrieval. Firstly, The Deep Orthogonal Local and Global(DOLG)information fusion framework is used to extract features from images. Secondly, the optimal number of clusters is determined by Bayesian. Then, the image features are divided into different clusters by the Gaussian mixture clustering algorithm. After that, the feature of the queried image will be compared with all centers of clusters and retrieved in the luster with the nearest cluster center. Finally, the retrieved images are reordered by the contextual similarity aggregated with the self-attention mechanism;the most similar referenced images are regarded as the retrieval result. Experimental results on the Roxford5k and Rparis6k datasets demonstrate that the proposed feature clustering-based image retrieval method can significantly reduce the time cost by more than 26% without loss of accuracy. © 2024 SPIE.

关键词： Information fusion

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Design of image parallel compression system based on FPGA

Design of image parallel compression system based on FPGA

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2023 international conference.on image, Signal processing, and pattern recognition, ISPP 2023

作者： Ma, Chaoqun Xu, Zhiyong Zhang, Yao School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing101408 China Institute of Optics and Electronics Chinese Academy of Sciences Chengdu610207 China

ISBN: (纸本)9781510666351

In order to solve the problem of high-speed image compression, this paper designs a parallel bit-plane encoding algorithm based on CCSDS image compression algorithm, and uses FPGA to implement it. Experimental results show that the system can effectively reduce the image coding time and meet the requirements of high-speed image compression. © 2023 SPIE.

关键词： image compression

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recognition pattern Analysis of Astronomical Object Forms Using the Analytical Model 14

Recognition Pattern Analysis of Astronomical Object Forms Us...

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14th international conference.on Advanced Computer Information Technologies, ACIT 2024

作者： Khlamov, Sergii Savanevych, Vadym Troianskyi, Volodymyr Grebennik, Igor Bondar, Yehor Netrebin, Yuriy Kharkiv National University of Radio Electronics Department of Systems Engineering Kharkiv Ukraine Astronomical Institute of the Slovak Academy of Sciences Tatranska Lomnica Slovakia

ISBN: (纸本)9798350350036

image processing and pattern recognition in astronomy represents a dynamic field of study where the analysis of astronomical object shapes plays a crucial role. This is associated with the necessity of identifying and classifying various patterns present in astronomical images. An important aspect of processing astronomical images is the recognition and analysis of patterns contained within these images. patterns can vary from circular shapes of stars and galaxies to elongated structures of gas clouds. Understanding these patterns not only helps us identify objects in space but also provides unique information about their structure, evolution, and physical characteristics. Another key aspect is the utilization of an analytical image model. This is a mathematical approach that allows modeling images of objects in space and analyzing their characteristics. The analytical model enables the detection of brightness peaks in images, determining typical object shapes, and conducting various analytical investigations. © 2024 IEEE.

关键词： Object detection

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