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检索条件"任意字段=2023 International Conference on Image Processing and Computer Vision, IPCV 2023"

共 16230 条记录，以下是4781-4790 订阅

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Improvising the CNN Feature Maps Through Integration of Channel Attention for Handwritten Text Recognition 7th

Improvising the CNN Feature Maps Through Integration of Chan...

引用

7th international conference on computer vision and image processing, CVIP 2022

作者： Shashank, B.N. Bhattu, S. Nagesh Sri Phani Krishna, K. Department of Computer Science and Engineering National Institute of Technology Andhra Pradesh Tadepalligudem India Department of Electrical Engineering National Institute of Technology Andhra Pradesh Tadepalligudem India

ISBN: (纸本)9783031314162

Convolutional Neural Network (CNN) based encoder and Recurrent Neural Network (RNN) based decoder architectures are widely used in the design of Handwritten Text Recognition (HTR) systems. Effective encoder representation plays a vital role in improving the performance of HTR systems. Squeeze and Excitation Networks, used in the context of image classification, object detection and scene classification, capture global inter-channel dependencies. ECA-Net learns channel attention via local Cross Channel Interaction (CCI). The current work proposes an encoder-decoder architecture for HTR which combines the benefits of local and global cross-channel attention for effective encoder representation. Experimental results on the IAM dataset show that there is an 8.98%, 3.24% reduction in Character Error Rate (CER) and an 8.98%, 3.45% reduction in Word Error Rate (WER) when the proposed module is applied to the state-of-the-art HTR Flor model and Puigcerver model respectively. The proposed work also presents a detailed error analysis at the character level on the IAM dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Decoding

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LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

LFNAT 2023 Challenge on Light Field Depth Estimation: Method...

引用

2023 IEEE/CVF conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Sheng, Hao Liu, Yebin Yu, Jingyi Wu, Gaochang Xiong, Wei Guo, Longzhao Xie, Yanlin Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Wang, Tun Yang, Da Cui, Zhenglong Wang, Sizhe Zhao, Mingyuan Wang, Qiong Chen, Qianyu Liang, Zhengyu Wang, Yingqian Yang, Jungang Yang, Xueting Deng, Junli Cong, Ruixuan Chen, Rongshan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China Tsinghua University China Shanghaitech University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Beijing Meet Yuan Co. Ltd China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada College of Computer Science and Technology Zhejiang University of Technology China National University of Defense Technology China School of Information and Communication Engineering Communication University of China China

ISBN: (纸本)9798350302493

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research. © 2023 IEEE.

关键词： Stereo image processing

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Long-distance 2D positioning with diffractive large diameter laser beam and camera

Long-distance 2D positioning with diffractive large diameter...

引用

IEEE international Instrumentation and Measurement Technology conference (I2MTC) - Rising Above Covid-19

作者： Su, Yinrui Matsuka, Daisuke Cheng, Wei Hitachi China Ltd Hitachi China Res Lab Guangzhou Peoples R China Hitachi Bldg Technol Guangzhou Co Ltd Guangzhou Peoples R China

ISBN: (纸本)9781665453837

In the construction of ultra-long structures or skyscraper, it is necessary to measure the shape and size of ultralong scale objects. The static station scanning measurement needs a lot of stitching in the ultra-long measurement. Meanwhile, the mobile measurement based on Simultaneous Localization and Mapping (SLAM) technology is difficult to achieve a good accuracy for long distance measurement with few features. We proposed a more accurate measuring method for ultra-long scale objects based on 2D laser positioning. The core of this technology is achieving accurate long-distance 2D positioning to obtain absolute coordinates in ultra-long structures. A positioning method based on machine vision is proposed to work with low-cost laser generator, with diffractive large diameter laser beam. A fast calibration method is generated to calibrate the positioner by capturing calibration board. image processing is taken to extract spot core from diffraction spot image and calculate its center to present relative position between laser beam and positioner. Calibration experiment is carried out. The calibrate error is about 0.055 mm. 100 m positioning experiment is designed. It is verified that positioning accuracy is about +/- 0.3 mm.

关键词： long distance 2D positioning machine vision calibration spot detection

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Text-Guided Generative Adversarial Network for image Emotion Transfer 19th

Text-Guided Generative Adversarial Network for Image Emotion...

引用

19th international conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Zhu, Siqi Qing, Chunmei Xu, Xiangmin South China Univ Technol Sch Elect & Informat Engn Guangzhou Guangdong Peoples R China South China Univ Technol Sch Future Technol Guangzhou 510641 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China

ISBN: (纸本)9789819947416;9789819947423

In recent years, image emotion computing has attracted widespread attention from researchers in the computer vision field due to its demonstrated potential in various domains. The main research direction in this field has been extended from image emotion classification and recognition to tasks such as image emotion transformation and generation. However, the semantic gap between high-level semantic image emotion and low-level image features has led to biases in existing image style transfer models' representation of image emotion, resulting in inconsistencies in the generated images' emotional features and semantic content. Inspired by text-guided image generation models, we first introduced the guiding role of text information into the research of emotional image transformation. The proposed model has achieved significant advantages in the performance of text-based editing of image emotions compared to existing text-guided image generation models. By leveraging the inherent connection between text and image emotion and content, we improved the accuracy of image emotion transformation and generated more natural and realistic images compared with existing image style transfer models and image emotion transfer models.

关键词： image emotion transfer generative adversarial network affective computing semantically multi-modal image synthesis

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Gesture Recognition Based Virtual Mouse and Keyboard 1

引用

international conference on Advances in Communication Technology and computer Engineering, ICACTCE 2023

作者： Sandhya, B.R. Amrutha, C. Ashika, S. Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India Department of ISE Sri Venkateshwara College of Engineering Bengaluru India Department of CSE Sri Venkateshwara College of Engineering Bengaluru India

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

Today, computer vision has advanced to an extent where a machine can recognize its owner by running a straightforward picture processing application. In the modern day, individuals use this vision for various activities in their daily life, including automatic cars, face recognition, and color detection. Hand gesture recognition is important and common since it also plays a role in the understanding of sign language. In the proposed framework, Human computer Interaction (HCI) approach has been used. An optical mouse and keyboard that employ hand motions are created using computer vision. The computer’s camera will recognize various hand gestures made by a user, and the computer’s mouse or pointer moves according to the movements of the motions. Users may even conduct right and left clicks using various gestures. Similar to this, several gestures might be used to operate the keyboard, such as the one-fingered gesture for selecting an alphabet and the two-fingered motion for swiping left and right. It won’t require a cable or other external accessories to function as a virtual mouse and keyboard. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer vision

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A system for automated tool wear monitoring and classification using computer vision 16

A system for automated tool wear monitoring and classificati...

引用

16th CIRP conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022

作者： Friedrich, Markus Gerber, Theresa Dumler, Jonas Döpper, Frank University of Bayreuth Chair Manufacturing and Remanufacturing Technology Bayreuth Germany Fraunhofer-Institute for Manufacturing Engineering and Automation IPA Project Group Process Innovation Bayreuth Germany

This paper presents an approach for automated monitoring and classification of face milling tool wear using computer vision. A test setup with low-cost equipment for in-machine application is developed and used to generate an image dataset from worn and new tools. Different types of filters and segmentation techniques are applied and compared for image preprocessing. For wear detection, both classification and regression models using convolutional neural networks are evaluated. Best results were obtained with a combined model. This demonstrates that optical wear monitoring is feasible with low-cost equipment. However, potentials for improvement were identified in manual labeling and image quality. © 2023 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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Research on Landscape image processing Based on Neural Network Algorithm 2

Research on Landscape Image Processing Based on Neural Netwo...

引用

2nd international conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Wang, Jingxu Hubei Three Gorges Polytechnic Yichang Hubei443000 China

ISBN: (纸本)9798350323818

In order to create a good landscape environment, it is necessary not only to make the layout beautiful and exquisite, in line with the public aesthetic point of view, but also to flexibly use new construction technologies to ensure the good quality of landscape construction, so as to truly create a landscape area with beautiful environment, excellent quality and rich functions for people and provide assistance for the construction and development of the city. The reasonable distribution of high-quality garden landscape can not only represent the overall image of the city, but also represent the cultural heritage of the city. In this paper, an algorithm of landscape image processing based on artificial neural network (ANN) is proposed, and ANN is applied to solve the optimization problem of landscape spatial layout with performance constraints, which realizes the optimization design of landscape spatial layout and lays a solid foundation for the automatic computer optimization design of landscape space. The simulation results show that this algorithm is more accurate in optimizing the spatial pattern of community culture, which is 26.88% higher than the traditional layout optimization algorithm, and can accurately locate the edge contour of the optimized space. This method not only improves the rationality and coordination of landscape planning, but also greatly reduces the cost of community greening planning. © 2023 IEEE.

关键词： Neural networks

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PTS-LIC: Pruning Threshold Searching for Lightweight Learned image Compression

PTS-LIC: Pruning Threshold Searching for Lightweight Learned...

引用

2023 IEEE international conference on Visual Communications and image processing, VCIP 2023

作者： Luo, Ao Sun, Heming Liu, Jinming Lin, Fangzheng Katto, Jiro Yokohama National University Faculty of Engineering Kanagawa Japan Shanghai Jiao Tong University China Waseda University Department of Computer Science and Communication Engineering Tokyo Japan Waseda Research Institute for Science and Engineering Tokyo Japan

ISBN: (纸本)9798350359855

Learned image Compression (LIC), which uses neural networks to compress images, has experienced significant growth in recent years. The hyperprior-module-based LIC model has achieved higher performance than classical codecs. However, the LIC models are too heavy (in calculation and parameter amounts) to apply to edge devices. To solve this problem, some former papers focus on structural pruning for LIC models. However, they either cause noticeable performance decrement or neglect the appropriate pruning threshold for each LIC model. These problems keep their pruning results sub-optimal. This paper proposes a Pruning Threshold Searching on the hyperprior module for different-quality LIC models. Our method removes most parameters and calculations while keeping the performance the same as the models before pruning. We removed at least 49.8% of parameters and 28.5% of calculations for the Channel-Wise-Context-Model-based models and 29.1% of parameters for the Cheng-2020 models. © 2023 IEEE.

关键词： image compression

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Two-branch weakly supervised semantic segmentation network based on web data enhancement 23

Two-branch weakly supervised semantic segmentation network b...

引用

2023 international conference on computer, vision and Intelligent Technology, ICCVIT 2023

作者： Zhang, Fengshun Yang, Yanjiao Zhang, Tao Wen, Long Yue, Tianjiao College of Computer Science and Engineering Northwest Normal University Lanzhou730070 China

ISBN: (纸本)9798400708701

As we all know, CAM [1] can only provide initial seeds of objects in weakly supervised segmentation. This paper proposes a two-branch semantic segmentation network that can transfer semantic knowledge, which can help CAM achieve more complete and accurate regions of objects. Thus, the network can be specifically divided into 3 steps: (1) Firstly, training the attention map to obtain the initial mask of the target dataset;(2)Secondly, Crawling images from the web to build a web dataset, use the trained attention map model to clean the web dataset, then transfer knowledge to target dataset by training group segmentation network;(3) Finally, Fusion of the initial target dataset masks and enhanced masks are used as pseudo-supervised training full convolutional networks. The backbone network are VGG16 and ResNet-101, image-level labels were used to obtain segmentation performances of Val 65.1% and Test 65.9% on the PASCAL VOC 2012 dataset. © 2023 ACM.

关键词： Semantic Segmentation

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Path planning and image classification algorithms for glass cleaning robots based on hybrid path planning and convolutional neural networks 4

Path planning and image classification algorithms for glass ...

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4th international conference on computers, Information processing and Advanced Education, CIPAE 2023

作者： Li, Zequn Han, Xiao Wang, Xiao Li, Yu Zhu, Xingyu Hohai University Nanjing China

ISBN: (纸本)9798350342710

With the development of the times and the progress of society, the number of high-rise buildings is increasing, and the cleaning of the outer glass at height is extremely difficult, relying on manual cleaning with high safety risks and low efficiency. The trend is to use automated control products such as cleaning robots to take on some of the difficult cleaning work. Path planning and image recognition for glass cleaning robots is a hot issue for cleaning robots to achieve efficient cleaning. In this paper, a path planning and image recognition method for glass cleaning robots based on hybrid path planning and convolutional neural networks is proposed to address this hot problem. The cleaning robot adopts a hybrid path of contour-parallel path and method-parallel path to achieve full-coverage path planning, and uses a portable camera to take photos of the glass, and uses a convolutional neural network model to determine the degree of dirty glass, and uses this as the basis for different cleaning processes, effectively improving the efficiency of cleaning and the utilisation of resources. In this paper, we design an intelligent control algorithm for a glass cleaning robot that combines artificial intelligence and 3D printing path planning generation by combining two of these methods, which helps to improve the cleaning effect of the cleaning robot. © 2023 IEEE.

关键词： Glass

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