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检索条件"任意字段=International Conference on Robot Vision and Signal Processing"

共 18125 条记录，以下是561-570 订阅

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Enhancing Road Navigation for the Visually Impaired: A Laser-Based Night vision Approach 4

Enhancing Road Navigation for the Visually Impaired: A Laser...

引用

4th international conference on robotics, Electrical and signal processing Techniques, ICREST 2025

作者： Sakib, Fahim Faysal Akash, Nasin Ayenul Rayied, Shahzad Hussain Juel, Md. Sajjadul Islam Hasan, Tabin American International University-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331530938

This study proposes a Convolutional Neural Network (CNN) based system to aid visually impaired individuals by classifying road surfaces. Leveraging image analysis and processing techniques, the model uses laser beams to identify road types under different conditions, including low-light scenarios. The challenge faced by visually impaired people in navigating roads with traditional white canes limited in scope and effectiveness motivated this research. The system incorporates a custom dataset of over 3000 high-quality images, employing contour detection to recognize obstacles and road types from various distances. The model achieves an accuracy exceeding 99% indicating reliability. This advancement offers a significant improvement in navigation and safety providing a practical solution for enhancing mobility and independence for visually impaired users. Future work will focus on expanding the model to detect additional objects further enhancing user safety and environmental awareness. © 2025 IEEE.

关键词： Laser beams

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2024 IEEE 8th international conference on signal and Image processing Applications, ICSIPA 2024

2024 IEEE 8th International Conference on Signal and Image P...

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8th IEEE international conference on signal and Image processing Applications, ICSIPA 2024

ISBN: (纸本)9798350352368

The proceedings contain 39 papers. The topics discussed include: performance analysis of several CNN based models for brain MRI in tumor classification;MRI-based lumbar sagittal alignment classification system;3D mapping and landing zone identification in complex terrains using DSM and photogrammetry;vision language models for oil palm fresh fruit bunch ripeness classification;towards no shadow: region-based shadow compensation on low-altitude urban aerial images;comparative analysis of deep learning architectures for blood cancer classification;exploration of group and shuffle module for semantic segmentation of sea ice concentration;on handcrafted machine learning features for art authentication;and acoustic signature modelling of marine vessels in various environmental and operational conditions.

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Customized Obstacle Dataset Generation from RGB-D Datasets

Customized Obstacle Dataset Generation from RGB-D Datasets

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2024 IEEE international conference on Intelligent signal processing and Effective Communication Technologies, INSPECT 2024

作者： Vijetha, U. Geetha, V. National Institute of Technology Karnataka Surathkal India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379525

The field of Obstacle Detection in computer vision is evolving rapidly, with many researchers contributing innovative solutions to address different challenges. However, the current landscape presents several barriers that hinder effective comparison and collaboration among researchers. A significant challenge arises from the limited availability of publicly accessible obstacle datasets, with existing datasets predominantly focused on specific contexts such as obstacles on roads, sidewalks, or particular industrial settings. The varied application contexts, environments, and hardware equipment in use compel researchers to develop and use custom datasets tailored to their application-specific requirements. Constraints such as time, funding, and limited access to resources often discourage researchers from re-creating new datasets for the purpose of comparative evaluations, resulting in incomplete insights into the strengths and weaknesses of different approaches. To address this deficiency in comparative analysis, we propose a novel method for generating obstacle datasets from existing RGB-D datasets dynamically. The proposed method offers a quick and effortless way to generate customized variants of Obstacle dataset, without having to manually build them from the ground up. The cross-dataset compatibility of the proposed method further enhances the versatility and wide adoption potential of the proposed method. © 2024 IEEE.

关键词： Computer vision Annotations Roads Collaboration signal processing Hardware Communications technology Data mining Time factors Iterative methods

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EVENT DATA STREAM COMPRESSION BASED ON POINT CLOUD REPRESENTATION 30

EVENT DATA STREAM COMPRESSION BASED ON POINT CLOUD REPRESENT...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Huang, Bowen Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1012 Lausanne Switzerland

ISBN: (纸本)9781728198354

The Dynamic vision System (DVS) is a novel image acquisition system that works only when there is a brightness change in a pixel, resulting in a stream of events including timestamps, spatial coordinates and the sign of the brightness change (increase or decrease). Although DVS's output data size is much smaller than conventional image systems, it still requires further compression, as the main applications of DVS are embedded systems with limited transmission and storage resources. In this paper, we propose a new method for lossless compression of event data streams based on point cloud representations. The event data stream is organized into a 3D point cloud to which a compression algorithm is applied. In addition, different generation strategies are devised in order to compare the compression performance of the proposed approach. Experimental results show an improved compression ratio of about 22% under lossless conditions.

关键词： Event camera data compression point cloud

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MobileNet-DeepLabV3+ based robot Passable Path Segmentation and Navigation Line Extraction 23

MobileNet-DeepLabV3+ based Robot Passable Path Segmentation ...

引用

7th international conference on Computer Science and Application Engineering, CSAE 2023

作者： Zhang, Caixia Wang, Chenyu Xu, Qingyang Weihai Vocational College China New Sci-Tech Park Weihai Shandong China Shandong University China No.180Wenhua Xilu Weihai Shandong China

ISBN: (纸本)9798400700590

Traditional vision-based navigation methods for mobile robot can only be applied to some specific simple scenes due to the limitations of complex road conditions and light variations. However, the research achievements of deep learning and the substantial improvement in computer data processing performance have made it practical and applicable to guide robots in navigation using visual semantic information. For the robot visual semantic navigation problem, a lightweight DeepLabV3+ image semantic segmentation model based on MobileNetV2 is constructed as the backbone network. The images acquired by the robot vision sensor can be sensed and segmented into targets and feasible paths within the scene. Navigation lines for navigable paths can be established using the obtained feasible areas. In this way, the robot's motion decisions can be guided. Therefore, the mobile robot is capable of pure vision-based navigation. The segmentation algorithm is validated using the CamVid dataset and the constructed experimental environment dataset with mPA 82.73 and mIoU 71.8, and the navigation line can be generated according to the drivable path, which guides the robot's navigation. © 2023 ACM.

关键词： Mobile robots

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3D reconstruction measurement method based on binocular vision

3D reconstruction measurement method based on binocular visi...

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2022 international conference on signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Zhou, Jian Wu, Jian School of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9781510664616

At present, the measurement of irregular workpiece is mainly carried out by coordinate measuring machine, but this kind of equipment is expensive and inconvenient to operate, and can’t be applied to soft objects. This paper proposed a 3D surface reconstruction method based on binocular vision to imitate the structure of human eyes, and then measured various sizes on the point cloud model. The reconstruction method based on binocular vision is fast, efficient and low-cost. Then, the 3D coordinates of the feature points in the world coordinate system are solved by the reprejectImgeTo3d function to generate the point cloud. The final point cloud model has rich surface texture information and can completely reflect the 3D size information of the object. © 2023 SPIE.

关键词： Binocular vision

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Estimation of the Moisture Content of In Vitro Chewed Food Boluses by Image processing 30

Estimation of the Moisture Content of In Vitro Chewed Food B...

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30th international conference on Mechatronics and Machine vision in Practice, M2VIP 2024

作者： Akarawita, Isurie Chen, Bangxiang Dhupia, Jaspreet Xu, Weiliang Stommel, Martin The University of Auckland Dept. of Mechanical and Mechatronics Engineering Auckland New Zealand Auckland University of Technology Dept. of Electrical and Electronic Engineering Auckland New Zealand

ISBN: (纸本)9798350391916

This paper presents a study on the evaluation of moisture content in in vitro chewed food boluses using image processing techniques. This integration offers a novel, non-destructive method that enhances the accuracy and efficiency of moisture assessments compared to traditional gravimetric methods and external sensors, which are typically more time-consuming and potentially alter the sample's properties. Invitro food samples were taken for moisture analysis from a biomimetic robot equipped with automatic saliva injections. Image skewness, variance, lightness, and Gray-Level Co-occurrence Matrix (GLCM) analysis were found to quantitatively indicate moisture content for two types of in vitro chewed food samples, at different chewing cycles and trajectories. This pilot study was conducted with two food samples, but the approach can be applied to a broader range. The study's findings highlight the effectiveness of image processing in analyzing moisture dynamics within chewed food boluses, providing valuable insights that could enhance the design and functionality of biomimetic chewing robots to resemble human oral processing more closely. © 2024 IEEE.

关键词： Microrobots

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Plate Visual Recognition Technology of Restaurant Service robot 2

Plate Visual Recognition Technology of Restaurant Service Ro...

引用

2nd international conference on Mechatronics and Automation Technology, ICMAT 2023

作者： Li, Shengqian Zhang, Xiaofan School of Electrical Engineering Guangdong Mechanical & Electrical Polytechnic Guangdong Guangzhou510550 China School of Automobile and Transportation Engineering Guangdong Polytechnic Normal University Guangdong Guangzhou510665 China

ISBN: (纸本)9781643684826

According to visual restaurant service robot application environment under the condition of uneven light and changeful, light and shade and occlusion, make the RGB image noise influence is very big which was collected by the binocular vision, difficult to use conventional image processing method for accurate identification of plate, the plate image is analyzed the characteristics and the traditional image processing technology, a adaptive edge of ellipse fitting method was proposed in this paper. Firstly, the image of dinner plate was preprocessed, and then the image edge information was extracted. Finally, according to the edge information, the dinner plate was identified by fitting ellipse with Hough transform. The experimental results show that under the circumstance of different light and shade in most restaurants, the ellipse feature of the edge of the plate can be accurately extracted by using this method, and the plate can be recognized, the validity of the method was verified. © 2024 The Authors.

关键词： Binocular vision

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Application of Computer vision Technology in the Field of Automatic Target Reporting 14th

Application of Computer Vision Technology in the Field of Au...

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14th international conference on Frontier Computing, FC 2024

作者： Zhu, Rui Yu, Jie Yao, Fushan Shi, Wenhua Army Academy of Armored Force Jilin Changchun China The Army of 95795 Guangxi Guilin China

ISBN: (纸本)9789819627936

Computer vision is to measure and judge by machine instead of man, and convert the captured target scene into image signal through camera device. Transmitting it to the image processing system and converting it into a digital signal through the significant mark of the image;Through the calculation of the digital signal, the characteristics of the target are obtained, so as to analyze, sort out, or obtain the judgment results. It is applied to the field of automatic target reporting, so as to improve the efficiency and quality. In view of the problems existing in manual target detection, combined with the actual needs, this paper intends to apply computer vision technology to automatic target reporting, focusing on the basic principle and system composition of automatic target reporting. Combined with the actual application, this paper studies and demonstrates the highly feasible automatic target reporting technology and forms a summary report, Lay a foundation for the subsequent research and development of automatic target reporting system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine vision

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TRANSFORMER-BASED CLIPPED CONTRASTIVE QUANTIZATION LEARNING FOR UNSUPERVISED IMAGE RETRIEVAL 31

TRANSFORMER-BASED CLIPPED CONTRASTIVE QUANTIZATION LEARNING ...

引用

2024 international conference on Image processing

作者： Dubey, Ayush Dubey, Shiv Ram Singh, Satish Kumar Chu, Wei-Ta Indian Inst Informat Technol Comp Vision & Biometr Lab Allahabad Uttar Pradesh India Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9798350349405;9798350349399

Unsupervised image retrieval aims to learn the important visual characteristics without any given level to retrieve the similar images for a given query image. The Convolutional Neural Network (CNN)-based approaches have been extensively exploited with self-supervised contrastive learning for image hashing. However, the existing approaches suffer due to lack of effective utilization of global features by CNNs and biased-ness created by false negative pairs in the contrastive learning. In this paper, we propose a TransClippedCLR model by encoding the global context of an image using Transformer having local context through patch based processing, by generating the hash codes through product quantization and by avoiding the potential false negative pairs through clipped contrastive learning. The proposed model is tested with superior performance for unsupervised image retrieval on benchmark datasets, including CIFAR10, NUS-Wide and Flickr25K, as compared to the recent state-of-the-art deep models. The results using the proposed clipped contrastive learning are greatly improved on all datasets as compared to same backbone network with vanilla contrastive learning.

关键词： Unsupervised Learning Image Retrieval Contrastive Learning Transformer Clipping

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