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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1646 条记录，以下是1431-1440 订阅

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Research on Intelligent Recognition of Train image Based on Machine vision

Research on Intelligent Recognition of Train Image Based on ...

引用

international conference on Cloud Computing and Internet of Things (CCIOT)

作者： Yu Zeng Jianhai Yue School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9781538671412

ISBN: (纸本)9781538671429

Machine vision has become a new technique in the AI field of image processing as an important and useful technique. Thus, it is significant to study image fusion on train fault. image fusion mainly studies the image data fusion technology to obtain a synthetic image including the multiple images of the same scene from many different patterns by the image sensors or the same sensor at the different time. In this paper, the images are collected form the bogie and coupler draft devices in the operation of train, and these images are merged based on wavelet transform. Compared with the weighted average fusion method about the fusion results of subjective and objective evaluation the superiority of wavelet transform is fully proved.

关键词： image fusion image edge detection Time-frequency analysis Discrete wavelet transforms Standards

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Leveraging Swin Transformer for Local-to-Global Weakly Supervised Semantic Segmentation

Leveraging Swin Transformer for Local-to-Global Weakly Super...

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Iranian Machine vision and image processing (MVIP)

作者： Rozhan Ahmadi Shohreh Kasaei Department of Computer Engineering Sharif University of Technology Tehran Iran

In recent years, weakly supervised semantic segmentation using image-level labels as supervision has received significant attention in the field of computer vision. Most existing methods have addressed the challenges arising from the lack of spatial information in these labels by focusing on facilitating supervised learning through the generation of pseudolabels from class activation maps (CAMs). Due to the localized pattern detection of Convolutional Neural Networks (CNNs), CAMs often emphasize only the most discriminative parts of an object, making it challenging to accurately distinguish foreground objects from each other and the background. Recent studies have shown that vision Transformer (ViT) features, due to their global view, are more effective in capturing the scene layout than CNNs. However, the use of hierarchical ViTs has not been extensively explored in this field. This work explores the use of Swin Transformer by proposing "SWTformer" to enhance the accuracy of the initial seed CAMs by bringing local and global views together. SWTformer-V1 generates class probabilities and CAMs using only the patch tokens as features. SWTformer-V2 incorporates a multi-scale feature fusion mechanism to extract additional information and utilizes a background-aware mechanism to generate more accurate localization maps with improved cross-object discrimination. Based on experiments on the PascalVOC 2012 dataset, SWTformer-V1 achieves a 0.98% mAP higher localization accuracy, outperforming state-of-the-art models. It also yields comparable performance by 0.82% mIoU on average higher than other methods in generating initial localization maps, depending only on the classification network. SWTformer-V2 further improves the accuracy of the generated seed CAMs by 5.32% mIoU, further proving the effectiveness of the local-to-global view provided by the Swin transformer. Code available at: https://***/RozhanAhmadi/SWTformer

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A Fuzzy-Rough-Based Approach for Uncertainty Classification on Hybrid Information System

A Fuzzy-Rough-Based Approach for Uncertainty Classification ...

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international conference on image, vision and Computing (ICIVC)

作者： Renjun He Chao Xu Daiwei Li Wenfeng Hou Xi Yu Haiqing Zhang AVIC Chengdu Aircraft Design & Research Institute Chengdu China College of Software Engineering Chengdu University of Information Technology Chengdu China School of Information Science and Technology Southwest Jiaotong University Chengdu China School of Computer Science Chengdu University Chengdu China

ISBN: (纸本)9781538649923

Hybrid information system (HIS) contains variety types of data including boolean, categorical, missing, real-valued, and set-based data, which are becoming very important for meaningful information analysis in real applications. The defect of the previous classification algorithms mainly lies in that the feature types of dataset haven't been comprehensively considered. In this paper, the internal relations among objects based on features that can reflect multiple data types have been expressed by heterogeneous distance function. And then, a new fuzzy-rough relation calculation under HIS and a new fuzzy-rough lower and upper approximation have been analyzed. Finally, the fuzzy-rough nearest neighbor classification under HIS (FRNN-HIS) algorithm has been proposed. Eight datasets has been adopted to conduct experiments. The experiments have shown that the proposed FRNN-HIS algorithm can significantly outperform FRNN and VQNN algorithms in terms of predicting classification results.

关键词： Classification algorithms Approximation algorithms Prediction algorithms Information systems Rough sets Task analysis Uncertainty

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image segmentation based on bat algorithm and pulse coupled neural network

Image segmentation based on bat algorithm and pulse coupled ...

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Electronic Information Technology and computer Engineering (EITCE), international conference on

作者： Qiang Lu Zhaochen Zhang Chao Yue College of Medical Information Engineering Shandong First Medical University & Shandong Academy of Medical Sciences Taian China

ISBN: (数字)9781728135847

ISBN: (纸本)9781728135854

image segmentation plays an important role in image processing and computer vision. The pulse coupled neural network has been applied in image segmentation. The pulse coupled neural network model has many parameters which have the influence on the performance. And the parameters optimization becomes a problem. Bat algorithm is inspired by the echolocation behavior of natural bats in determining their foods and also applied in many optimization problem. In this paper, the bat algorithm is used to optimize the parameters of pulse coupled neural network and the pulse coupled neural network is applied in image segmentation. The results show that the new algorithm has better performance than the threshold algorithm and otsu image segmentation algorithm.

关键词： Neural networks

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The Precise Recognition of Moving Object in Complex Background

The Precise Recognition of Moving Object in Complex Backgrou...

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3rd international conference on Natural Computation (ICNC 2007), vol.2

作者： Weiyao Huang Zhijing Liu Wenjuan Pan School of Computer Science and Technology Xidian University Xi'an China

Moving object detection is currently one of the most active research topics in the domain of computer vision and video processing. In this paper, a simply and fast quadric binaryzation method is proposed to remove environmental noise points effectively. Moreover, based on the stability of drawing the contour from fixed scene, the background subtraction method is combined with the time-stepping method, an effective method which could be used to detect the movement areas. Through the average weighted of two means can realize to enhance the accuracy in distinguishing target. Experiment results have shown that this method gives stable performances and good robustness.

关键词： Morphology image segmentation Object detection Working environment noise image analysis image recognition Surveillance image coding Shape measurement computer science

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Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA images 25th

Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic R...

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25th international conference on Medical image Computing and computer-Assisted Intervention , MICCAI 2022

作者： Hou, Junlin Xiao, Fan Xu, Jilan Zhang, Yuejie Zou, Haidong Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Ophthalmology Shanghai General Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031336577

The ultra-wide optical coherence tomography angiography (OCTA) has become an important imaging modality in diabetic retinopathy (DR) diagnosis. However, there are few researches focusing on automatic DR analysis using ultra-wide OCTA. In this paper, we present novel and practical deep-learning solutions based on ultra-wide OCTA for the Diabetic Retinopathy Analysis Challenge (DRAC). In the first task of segmentation of DR lesions, we utilize UNet and UNet++ to segment three lesions with strong data augmentation and model ensemble. In the second task of image quality assessment, we create an ensemble of Inception-V3, SE-ResNeXt, and vision Transformer models. Pre-training on the large dataset as well as the hybrid MixUp and CutMix strategy are both adopted to boost the generalization ability of our models. In the third task of DR grading, we build a vision Transformer and find that the model pre-trained on color fundus images serves as a useful substrate for OCTA images. Extensive ablation studies demonstrate the effectiveness of each designed component in our solutions. The proposed methods rank 4th, 3rd, and 5th on the three leaderboards of DRAC, respectively. Our code is publicly available at https://***/FDU-VTS/DRAC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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A study on Generative Adversarial Text to image Synthesis

A study on Generative Adversarial Text to Image Synthesis

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Marjana Tahmid Samiul Alam Kalim Akram Kazi Muhammad Asif Ashrafi Dept. of CSE East Delta University Chittagong Bangladesh Dept. of EEE East Delta University Chittagong Bangladesh Dept. of CSE Ulm University Ulm Germany

image captioning is one of the most prevalent and difficult challenges in Natural Language processing and computer vision: given an image, a written description of the image must be developed. The counterpart of the text to image problem is text to image synthesis, which involves creating a picture from a written description. These challenges approximate language translation challenges from a high-level perspective. images and text are two separate "languages" to convey related information, just like equivalent semantics might be represented in two different languages. This is undoubtedly beneficial, however existing AI systems are a long way from achieving this. Strong neural network topologies, such GANs (Generative Adversarial Networks), have been found to provide effective outcomes in recent years. There are, however, some text to image synthesis algorithms that use GAN (Generative Adversarial Network) that attempt to directly map words and characters to image pixels utilizing image synthesis and natural language synthesis approaches. To reconcile these advancements in text as well as image modeling and successfully translate visual representations from characters to pixels, we design an innovative deep architecture and GAN formulation in this study. Here, the demonstration of our model has been done by generating plausible images of birds and flowers from detailed text descriptions.

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A Method of Camera Link image Data processing Based on FPGA

A Method of Camera Link Image Data Processing Based on FPGA

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2018 3rd international conference on Computational Modeling, Simulation and Applied Mathematics (CMSAM 2018)

作者： Shi-feng WANG

Camera Link is based on the development of Channel Link Technology. The data is transmitted using low voltage differential signal(LVDS). It has some advantages, such as fast speed, strong anti-interference ability and so on. Camera Link is widely used in the field of robot vision applications. FPGA integrates rich gate circuit resources and has the advantages of high speed, which makes it suitable for processing parallel data. In this paper, a Camera Link image data processing method based on FPGA is proposed, which is verified by simulation and has been successfully applied to practical projects.

关键词： Camera Link image Data processing FPGA

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A component based architecture to improve testability, targeted FPGA-based vision systems

A component based architecture to improve testability, targe...

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international conference on Communication Software and Networks, ICCSN

作者： Mostafa Pordel Nima Moghaddami Khalilzad Farahnaz Yekeh Lars Asplund Department of Computer Science Umeå University Umea Sweden School of Innovation Design and Engineering Mälardalen University Vasteras Sweden

FPGA has been used in many robotics projects for real-time image processing. It provides reliable systems with low execution time and simplified timing analysis. Many of these systems take a lot of time in development and testing phases. In some cases, it is not possible to test the system in real environments very often, due to accessibility, availability or cost problems. This paper is the result of a case study on vision systems for two robotics projects in which the vision team consisted of seven students working for six months fulltime on developing and implementing different image algorithms. While FPGA has been used for real-time image processing, some steps have been taken in order to reduce the development and testing phases. The main focus of the project is to integrate different testing methods with FPGA development. It includes a component based solution that uses a two-way communication with a PC controller for system evaluation and testing. Once the data is acquired from the vision board, the system stores it and simulates the same environment that has been captured earlier by feeding back the obtained data to FPGA. This approach addresses and implements a debugging methodology for FPGA based solutions which accelerate the development phase. In order to transfer massive information of images, RMII which is an interface for Ethernet communication, has been investigated and implemented. The provided solution makes changes easier, saves time and solves the problems mentioned earlier.

关键词： Field programmable gate arrays Robots Process control Monitoring Switches Clocks Reliability

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Classification of Diabetic Retinopathy Using image Pre-processing Techniques

Classification of Diabetic Retinopathy Using Image Pre-proce...

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Intelligent Technologies (CONIT), international conference on

作者： Kavya Duvvuri Harshitha Kanisettypalli Masabattula Teja Nikhil Suja Palaniswamy Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Most diseases that affect humans cause an effect on the eyes which is observed first by a physician while treating a patient. Eye diseases are conditions that affect the functioning of the eye and lead to loss of vision. Globally, at least 2.2 billion people have near or distant vision impairment. Most of these eye diseases have long-term effects so, early detection of the eye disease is very important to avoid the consequences. Some eye diseases include cataracts, glaucoma, diabetic retinopathy, and retinal detachment, etc. Almost 10% of people are facing an eye disease called diabetic retinopathy (DR). In this paper, multiclassification of DR images has been done after performing a few preprocessing techniques like erosion, histogram equalization, and comparing the accuracy of the CNN model before and after the pre-processing. The classification model yielded an accuracy of 86.4% on retinal fundus images filtered from the Kaggle DR detection database. The obtained results show that the proposed preprocessing methods are best suitable for diagnosing diabetic retinopathy from retinal fundus images.

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