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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16444 条记录，以下是1461-1470 订阅

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Fusion recommendation algorithm based on DeepWalk and Item-Based collaborative filtering 3

Fusion recommendation algorithm based on DeepWalk and Item-B...

引用

3rd International conference on Computer Science and Management Technology, ICCSMT 2022

作者： Yu, Yicheng Li, Rongbin Yin, Jun Qinghai Normal University School of Computing Xi'Ning China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xi'Ning China

ISBN: (纸本)9798350320404

Collaborative filtering techniques have been applied to personalised recommendation systems in recommender systems. However, with the gradual increase in the number of platform users and goods, the user rating data on the entire project matrix is extremely sparse, resulting in the inaccuracy of the prediction results. DeepWalk, on the other hand, still performs well when the data is sparse. Therefore, DeepWalk is introduced into collaborative filtering, and a fusion recommendation algorithm based on DeepWalk and collaborative filtering is proposed to reduce the impact of cold start and sparsity on the recommendation results. Experiments show that the proposed fusion recommendation algorithm has higher recommendation accuracy when the data is sparse compared with other traditional algorithms, which improves the recommendation quality to a certain extent. © 2022 IEEE.

关键词： Collaborative filtering

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Lightweight Adaptive Feature Sampling-based High Dynamic Range Infrared image Detail Extraction Network 25

Lightweight Adaptive Feature Sampling-based High Dynamic Ran...

引用

Proceedings of the 4th International conference on Computer, Artificial Intelligence and Control Engineering

作者： Mingle Ma xinke Yang Huanan Li Rui Fan Chaoxiong Dai Peng Wang Xi' an Pinggao Smart Energy Co. LTD Xi' an Shaanxi China Faculty of Integrated Circuit Xidian University Xi' an Shaanxi China

ISBN: (纸本)9798400712647

Accurate detail extraction from infrared images is fundamental for subsequent analysis tasks such as target detection, fault diagnosis, and thermal anomaly identification, as these details often contain critical information about object boundaries, thermal variations, and potential defects. However, extracting fine details from such images remains challenging due to their inherent characteristics of low contrast, high noise, and complex parameter tuning requirements in traditional processing methods. This paper proposes a novel high dynamic range infrared image detail extraction method addressing the difficulties of traditional infrared image processing in detail extraction and parameter adjustment. Based on grouped deformable convolution and depth-wise separable convolution, we propose a lightweight adaptive feature sampling module (LAFSM) that can effectively achieve precise detail and basic component separation of high-bit infrared images. Compared with traditional algorithms, our method requires no manual parameter adjustment and significantly reduces parameter count. Experimental results demonstrate that this method performs excellently in high-bit infrared image detail extraction, with significant advantages in edge preservation and noise suppression, providing a new technical approach for high dynamic range infrared image preprocessing.

关键词： Convolution Neural Networks

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Research on image Analysis and processing Technology Based on Big Data Technology

Research on Image Analysis and Processing Technology Based o...

引用

Electrical Engineering, Big Data and algorithms (EEBDA), IEEE International conference on

作者： Yahui Hu Qiushi xi Lei xiao Qiqing Fang Yamin Hu Early Warning Academy Wuhan China

With the increase of information, the traditional image processing technology still has a big shortage in image quality and processing speed. Therefore, how to improve the image quality and processing speed is a research hotspot. To solve the above problems, we adopt the method of combining the generative countermeasure network with the super-resolution reconstruction, and use the open data set to train the model. Through the reasonable selection and optimization of the image processing algorithm, we can achieve the goal of improving the image super-resolution reconstruction effect. In order to improve the slow image processing speed, we set up Spark cluster to integrate computing resources. The experimental results show that the method based on the generation of countermeasure network has a good effect for high resolution image reconstruction, and this method can expand the resolution of low resolution image by four times; The cluster based on the Spark platform shows better performance advantages in processing a large number of image data. When the hardware indicators of the cluster environment and the stand-alone environment are consistent, the cluster based on the Spark platform can increase the running speed of the program by about 10%. When the data volume reaches the tera-byte level or even the petabyte level, the operation speed of the system can still be improved by increasing the overall running memory of the cluster.

关键词： image quality Superresolution Clustering algorithms Big Data Hardware Sparks image reconstruction

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Quilt-1M: One Million image-Text Pairs for Histopathology 37

Quilt-1M: One Million Image-Text Pairs for Histopathology

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37th conference on Neural Information processing systems, NeurIPS 2023

作者： Ikezogwo, Wisdom O. Seyfioglu, Mehmet S. Ghezloo, Fatemeh Geva, Dylan Mohammed, Fatwir S. Anand, Pavan K. Krishna, Ranjay Shapiro, Linda G. University of Washington United States

ISBN: (纸本)9781713899921

Recent accelerations in multi-modal applications have been made possible with the plethora of image and text data available online. However, the scarcity of analogous data in the medical field, specifically in histopathology, has slowed comparable progress. To enable similar representation learning for histopathology, we turn to YouTube, an untapped resource of videos, offering 1, 087 hours of valuable educational histopathology videos from expert clinicians. From YouTube, we curate QUILT: a large-scale vision-language dataset consisting of 802, 144 image and text pairs. QUILT was automatically curated using a mixture of models, including large language models, handcrafted algorithms, human knowledge databases, and automatic speech recognition. In comparison, the most comprehensive datasets curated for histopathology amass only around 200K samples. We combine QUILT with datasets from other sources, including Twitter, research papers, and the internet in general, to create an even larger dataset: QUILT-1M, with 1M paired image-text samples, marking it as the largest vision-language histopathology dataset to date. We demonstrate the value of QUILT-1M by fine-tuning a pre-trained CLIP model. Our model outperforms state-of-the-art models on both zero-shot and linear probing tasks for classifying new histopathology images across 13 diverse patch-level datasets of 8 different sub-pathologies and cross-modal retrieval tasks. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Large datasets

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Digital Gesture Recognition based on Deep Capsule Network 2

Digital Gesture Recognition based on Deep Capsule Network

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2nd IEEE International conference on Electronic Technology, Communication and Information, ICETCI 2022

作者： Wang, Yifan Jiang, Hong School of Computer Science and Engineering Xi'an Technological University Shaanxi Xi'an710021 China

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

Because gesture recognition is more and more useful in life, gesture recognition should not only consider the changes of gestures, but also be affected by the environment. Therefore, there are high requirements for the gesture recognition algorithms. Previously, image processing methods were used for gesture recognition, but the recognition accuracy of traditional methods was not high. The Capsule Network Model (CapsNet) proposed by Hinton has better recognition performance than traditional networks due to the use of dynamic routing algorithms. Based on CapsN et, this paper designs and realizes a deep capsule network model (DCNet), change the size of the convolution kernel to reduce the amount of parameters, and adds a convolution to the original capsule network structure to extract more feature information, and increase the dimension of the capsule from 8 to 16 to improve the decision-making ability of the capsule. Make the recognition accuracy of the network on a SIGN digital gesture data set higher. Using DCNet to test on the SIGN data set, the detection accuracy of training learning can reach 99.62%. The experimental show that the test accuracy of DCN et is higher than CapsN et for testing with digital gesture data set. © 2022 IEEE.

关键词： Decision making

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Convolution Neural Networks: Intersection of Deep Learning and image processing in Computational Art 10

Convolution Neural Networks: Intersection of Deep Learning a...

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10th International conference on Digital and Interactive Arts (ARTECH)

作者： Shabbar, Aya Tishreen Univ Mechatron Engn Latakia Syria

ISBN: (纸本)9781450384209

Deep Learning and image processing is a key concept in today's world of computational art, where artists employed AI algorithms to generate visuals. This paper explores AI-generated images, using Convolutional Neural Networks software as a paradigm of symbolic AI creative systems, and contextualizes the use of modern image processing technologies to create visual artworks. It discusses the methodologies and strategies used to make art using AI algorithms, manipulating them with processing software tool. The discussion focuses on CNN (Convolutional Neural Network) and processing software (Java) as the main technologies used in distinct fields to generate images. My conception of technical images provides a conceptual framework for examining the qualities and attributes of AI-generated images.

关键词： Deep learning image processing computational art convolutional neural networks

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Sound and Complete Verification of Polynomial Networks 36

Sound and Complete Verification of Polynomial Networks

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36th conference on Neural Information processing systems (NeurIPS)

作者： Rocamora, Elias Abad Sahin, Mehmet Fatih Liu, Fanghui Chrysos, Grigorios G. Cevher, Volkan Ecole Polytech Fed Lausanne LIONS Lausanne Switzerland

ISBN: (纸本)9781713871088

Polynomial Networks (PNs) have demonstrated promising performance on face and image recognition recently. However, robustness of PNs is unclear and thus obtaining certificates becomes imperative for enabling their adoption in real-world applications. Existing verification algorithms on ReLU neural networks (NNs) based on classical branch and bound (BaB) techniques cannot be trivially applied to PN verification. In this work, we devise a new bounding method, equipped with BaB for global convergence guarantees, called Verification of Polynomial Networks or VPN for short. One key insight is that we obtain much tighter bounds than the interval bound propagation (IBP) and DeepT-Fast [Bonaert et al., 2021] baselines. This enables sound and complete PN verification with empirical validation on MNIST, CIFAR10 and STL10 datasets. We believe our method has its own interest to NN verification. The source code is publicly available at https://***/megaelius/PNVerification.

关键词： image recognition

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Solar Powered Auto Irrigation Monitoring System with Plant Health Indication Using image processing

Solar Powered Auto Irrigation Monitoring System with Plant H...

引用

International conference on Open Source systems and Technologies (ICOSST)

作者： Maida Saleem Khadija Nawaz Shahroz Ali Hira Ali Jamal Dept. of Electrical Engineering University of Engineering and Technology Lahore RCET Campus Gujranwala Pakistan

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

This project aims to develop a solar powered auto irrigation system that incorporates image processing techniques to monitor and maintain the health of the plants. The system utilizes solar energy to power the irrigation process, making it environmentally friendly and cost-effective. image processing algorithms are employed to analyze the visual characteristics of the plants and detect any signs of distress or disease. This information is then used to automatically adjust the irrigation schedule and provide necessary nutrients to ensure optimal plant health. Furthermore, the system integrates image processing techniques to analyze the health of the plants. images of the plants are captured using a camera module and processed to detect any signs of diseases or abnormalities. The system then provides real-time feedback on the plant health status. Through the implementation of this project, the efficiency and effectiveness of irrigation systems can be improved, leading to better crop yields and reduced water usage.

关键词： Temperature sensors Training Irrigation Visualization image processing Valves Sensor systems Real-time systems Sensors Monitoring

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image Compressive Sensing With Adaptive Sampling by Median Filtering

Image Compressive Sensing With Adaptive Sampling by Median F...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yanfeng Wu Chen Hui Ronghua Liao Shaohui Liu Debin Zhao Department of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep unfolding compressive sensing (CS) has experienced remarkable advancements. However, there still exist two challenges: (1) Many algorithms either use uniform block-based sampling, which ignore the fact that the content of different blocks is different, or allocate the sampling rate referring to complete signal before CS sampling, which is not always feasible in real-world scenarios. (2) Traditional CNN is difficult to capture broader contextual priors during iterative recovery. In this paper, we propose a novel network ASMFNet to solve the above two issues. Specifically, to address the first issue, we introduce a dual-branch network featuring a basic sampling branch to acquire reference image and an adaptive sampling branch by median filtering for allocating remaining sampling rate adaptively. For the second problem, we use Swin Transformer and feature fusion block to increase the feature interactions. Experimental results demonstrate that our proposed method outperforms existing methods.

关键词： Adaptive systems Filtering Signal processing algorithms Signal processing Transformers Iterative algorithms Sensors Speech processing Compressed sensing image reconstruction

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Could Giant Pretrained image Models Extract Universal Representations? 36

Could Giant Pretrained Image Models Extract Universal Repres...

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36th conference on Neural Information processing systems (NeurIPS)

作者： Lin, Yutong Liu, Ze Zhang, Zheng Hu, Han Zheng, Nanning Lin, Stephen Cao, Yue Xi An Jiao Tong Univ Xian Peoples R China Univ Sci & Technol China Hefei Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781713871088

Frozen pretrained models have become a viable alternative to the pretraining-then-finetuning paradigm for transfer learning. However, with frozen models there are relatively few parameters available for adapting to downstream tasks, which is problematic in computer vision where tasks vary significantly in input/output format and the type of information that is of value. In this paper, we present a study of frozen pretrained models when applied to diverse and representative computer vision tasks, including object detection, semantic segmentation and video action recognition. From this empirical analysis, our work answers the questions of what pretraining task fits best with this frozen setting, how to make the frozen setting more flexible to various downstream tasks, and the effect of larger model sizes. We additionally examine the upper bound of performance using a giant frozen pretrained model with 3 billion parameters (SwinV2-G) and find that it reaches competitive performance on a varied set of major benchmarks with only one shared frozen base network: 60.0 box mAP and 52.2 mask mAP on COCO object detection test-dev, 57.6 val mIoU on ADE20K semantic segmentation, and 81.7 top-1 accuracy on Kinetics-400 action recognition. With this work, we hope to bring greater attention to this promising path of freezing pretrained image models.

关键词： Object detection

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