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检索条件"任意字段=Iranian Conference on Machine Vision and Image Processing"

共 11506 条记录，以下是251-260 订阅

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Design of Inclinometer Calibration Data Acquisition System Based on LabVIEW 3

Design of Inclinometer Calibration Data Acquisition System B...

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3rd International conference on machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Yi, Zhaozang Chen, Juanjuan Liu, Rongming Chen, Hao Shanghai institute of measurement and testing technology Shanghai200000 China

ISBN: (纸本)9781510681880

This article introduces an automatic data acquisition system for the calibration device of the inclinometer using a circular grating encoder as the standard device. The system utilizes LabVIEW to develop a complete host computer system and employs vision modules to process, segment, and recognize the captured images of the inclinometer for indication. Through this system, data acquisition for inclinometers and angular encoders can be achieved, while automating the calibration process. © 2024 SPIE.

关键词： Calibration LabVIEW Optical character recognition Equipment Education and training Data acquisition Design image processing Human-machine interfaces Target recognition

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vision-Based Assistive Technologies for People with Cerebral Visual Impairment: A Review and Focus Study 24

Vision-Based Assistive Technologies for People with Cerebral...

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26th International ACM SIGACCESS conference on Computers and Accessibility (ASSETS)

作者： Gamage, Bhanuka Holloway, Leona McDowell, Nicola Do, Thanh-Toan Price, Nicholas Marriott, Kim Lowery, Arthur Monash Univ Melbourne Vic Australia Massey Univ Auckland New Zealand

ISBN: (纸本)9798400706776

Over the past decade, considerable research has investigated vision-Based Assistive Technologies (VBAT) to support people with vision impairments to understand and interact with their immediate environment using machine learning, computer vision, image enhancement, and/or augmented/virtual reality. However, this has almost totally overlooked a growing demographic: people with Cerebral Visual Impairment (CVI). Unlike ocular vision impairments, CVI arises from damage to the brain's visual processing centres. Through a scoping review, this paper reveals a signifcant research gap in addressing the needs of this demographic. Three focus studies involving 7 participants with CVI explored the challenges, current strategies, and opportunities for VBAT. We also discussed the assistive technology needs of people with CVI compared with ocular lowvision. Our fndings highlight the opportunity for the Human-Computer Interaction and Assistive Technologies research community to explore and address this underrepresented domain, thereby enhancing the quality of life for people with CVI.

关键词： cerebral visual impairment assistive devices computer vision machine learning augmented reality virtual reality focus group discussion

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Design and Research of Household Intelligent Classified Garbage Bin Based on machine vision 5

Design and Research of Household Intelligent Classified Garb...

引用

5th International conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

作者： Song, Tao Lin, Aiqin Liaoning Economic Vocational Technological Institute Shenyang China

ISBN: (纸本)9781839539848

Due to the lack of understanding of the concept of garbage classification and the lack of awareness of garbage classification in the process of garbage classification, a home intelligent sorting trash bin design based on machine vision is proposed based on these problems. The garbage sorting device, combined with the mechanical structure, proposes a garbage bin design using the Raspberry Pi system. The structure design of the intelligent trash bin composed of Raspberry Pi development board, Raspberry Pi camera, LED light, servo and independent four warehouses are given, and the design ideas of the trash bin in the realization process are expounded. The overall framework design of the system consists of image acquisition module, image processing module, four modules including garbage classification and control module, focusing on the application of image processing in garbage classification, improving the accuracy and processing efficiency of household garbage classification. © The Institution of Engineering & Technology 2023.

关键词： image recognition

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Reinforcement Learning Transformer for image Captioning Generation Model 15

Reinforcement Learning Transformer for Image Captioning Gene...

引用

15th International conference on machine vision (ICMV)

作者： Yan, Zhaojie Univ Calif Santa Barbara Santa Barbara CA 93106 USA

ISBN: (纸本)9781510666184;9781510666191

image captioning generation is a combination of the visual domain and natural language processing. The transformer framework has become the mainstream approach. This paper combines reinforcement learning and transformer methods to reward dynamics backpropagation and normalization in the testing phase. Its characteristic is that when the steps of reinforcement learning increase, the agent model has more knowledge of the fully information, which reduces the computing cost of the system. The experimental results show that the reinforcement transformer structure has achieved a certain improvement in speed.

关键词： image captioning transformer reinforcement learning reward dynamics backpropagation

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Expanding Hyperspherical Space for Few-Shot Class-Incremental Learning

Expanding Hyperspherical Space for Few-Shot Class-Incrementa...

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IEEE/CVF Winter conference on Applications of Computer vision (WACV)

作者： Deng, Yao Xiang, Xiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

ISBN: (纸本)9798350318920;9798350318937

In today's ever-changing world, the ability of machine learning models to continually learn new data without forgetting previous knowledge is of utmost importance. However, in the scenario of few-shot class-incremental learning (FSCIL), where models have limited access to new instances, this task becomes even more challenging. Current methods use prototypes as a replacement for classifiers, where the cosine similarity of instances to these prototypes is used for prediction. However, we have identified that the embedding space created by using the relu activation function is incomplete and crowded for future classes. To address this issue, we propose the Expanding Hyperspherical Space (EHS) method for FSCIL. In EHS, we utilize an odd-symmetric activation function to ensure the completeness and symmetry of embedding space. Additionally, we specify a region for base classes and reserve space for unseen future classes, which increases the distance between class distributions. Pseudo instances are also used to enable the model to anticipate possible upcoming samples. During inference, we provide rectification to the confidence to prevent bias towards base classes. We conducted experiments on benchmark datasets such as CIFAR100 and miniimageNet, which demonstrate that our proposed method achieves state-of-the-art performance.

关键词： Algorithms Algorithms and algorithms formulations image recognition and understanding machine learning architectures

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image rain removal algorithm based on conditional diffusion model 2

Image rain removal algorithm based on conditional diffusion ...

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2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Li, Yuxin Liu, Lijun Ma, Baoying Dalian Minzu University School of Science Dalian China

ISBN: (纸本)9798350331417

Rainfall image restoration is one of the research hotspots in the field of computer vision. For raindrops attached to the camera lens or glass, it will hinder the visualization of the complete image and also lead to image degradation. Most of the existing methods for converting raindrop-degraded images into clean images use generative adversarial neural networks (GANs) to solve this problem. At present, the denoising probability diffusion model has been widely used in various fields. Therefore, this paper proposes a raindrop image restoration algorithm based on conditional diffusion model, which improves the U-Net network used for noise prediction in diffusion model. The model is verified and evaluated on the raindrop removal dataset, and has strong generalization ability for raindrop image restoration in the real world. © 2023 IEEE.

关键词： component Conditional diffusion model Raindrop Removal U-Net network

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An Improved U-Net image Segmentation Network for Crankshaft Surface Defect Detection

An Improved U-Net Image Segmentation Network for Crankshaft ...

引用

iranian machine vision and image processing (MVIP)

作者： Ashkan Moosavian Elmira Bagheri Alireza Yazdanijoo Amir Hossein Barshooi Department of Agricultural Engineering Technical and Vocational University (TVU) Tehran Iran School of Automotive Engineering Iran University of Science and Technology (IUST) Tehran Iran Irankhodro Powertrain Company (IPCo) Tehran Iran

Crankshaft is one of the mechanical components of the vehicle engine, and quality control of it holds significant importance in the production line. In this paper, a vision-based system was developed to detect apparent structural defects on the crankshaft surface. By examining the different approaches in computer vision tasks, the semantic segmentation technique was chosen to solve this problem. In the first stage, a dataset consisting of 400 crankshaft experimental images with structural defects such as scratch, pitting, and grinding were collected. Then, the Convolutional Neural Network (CNN) with MobileNet architecture was trained to detect apparent defects, and an Intersection over Union (IoU) evaluation criteria of 64.7% was obtained. In the third stage, some image processing techniques were used to increase the performance. By applying the DexiNed edge detection filter on the train-set images, the IoU was increased by 8.4%. Considering the importance of this issue in the automotive industry, it has been tried again to boost the performance by augmenting the dataset images. On the other hand, this can also prevent overfitting of the model. By training the model under the same conditions as the previous stages, the IoU in this stage increased by 13.2% and reached 86.3%.

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Proceedings of the 4th International conference on image processing and vision Engineering, IMPROVE 2024

Proceedings of the 4th International Conference on Image Pro...

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4th International conference on image processing and vision Engineering, IMPROVE 2024

ISBN: (纸本)9789897586934

The proceedings contain 11 papers. The topics discussed include: toward objective variety testing score based on computer vision and unsupervised machine learning: application to apple shape;using deep learning for the dynamic evaluation of road marking features from laser imaging;Belfort birth records transcription: preprocessing, and structured data generation;fitting tree model with CNN and geodesics to track blood vessels in 2D medical images and application to ultrasound localization microscopy data;production-ready end-to-end visual quality inspection for defect detection on surfaces based on a multi-stage ai system;multimodal deepfake detection for short videos;HERO-GPT: zero-shot conversational assistance in industrial domains exploiting large language models;leveraging temporal context in human pose estimation: a survey;and chaotic convolutional long short-term memory network for respiratory motion prediction.

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Application of image processing in Piston-connecting Rod Group Surface Defect Detection 7

Application of Image Processing in Piston-connecting Rod Gro...

引用

7th IEEE Information Technology and Mechatronics Engineering conference, ITOEC 2023

作者： Zheng, Bin Jian, Hongji Zhang, Keyuan Panzhihua University School of Intelligent Manufacturing Sichuan Panzhihua China

ISBN: (纸本)9798350334197

In traditional industry, the detection of the automobile components quality is mainly completed by human eyes. Low detection accuracy, high labor consumption, and slow detection speed are important reasons for the slow development of manufacturing industry. With the continuous upgrading of optical hardware equipment such as industrial lenses and cameras, and the development of image processing technology, machine vision technology has developed rapidly in the field of image detection. machine vision technology collects images through the imaging system, converts them into digital images by the image acquisition card, and finally detects defects through image processing software. A machine vision approach is proposed to replace traditional manual inspection methods for the detection of piston-connecting rod groups in automobile engines. The surface defects of the piston-connecting rod groups are the research object, with image processing technology as the core. Through image gray-scale transformation, de-noising, median filtering, threshold segmentation, and region of interesting extraction methods, the surface defect detection of piston-connecting rod group was ultimately achieved. It provides a research approach for similar research. © 2023 IEEE.

关键词： Computer vision

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DCT-HistoTransformer: Efficient Lightweight vision Transformer with DCT Integration for histopathological image analysis 31

DCT-HistoTransformer: Efficient Lightweight Vision Transform...

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31st National and 9th International iranian conference on Biomedical Engineering, ICBME 2024

作者： Cherakhloo, Mahdi Ranjbar, Mahtab Mohebbi, Mehdi Vahdat, Bijan Vosoughi Sharif University of Technology Department of Medical Engineering Electrical Engineering Department Iran Kharazmi University Department of Mathematical and Computer Sciences Iran

ISBN: (纸本)9798331529710

In recent years, the integration of advanced imaging techniques and deep learning methods has significantly advanced computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Transformers, which have shown great promise in computer vision, are now being applied to medical image analysis. However, their application to histopathological images presents challenges due to the need for extensive manual annotations of whole-slide images (WSIs), as these models require large amounts of data to work effectively, which is costly and time-consuming. Furthermore, the quadratic computational cost of vision Transformers (ViTs) is particularly prohibitive for large, high-resolution histopathological images, especially on edge devices with limited computational resources. In this study, we introduce a novel lightweight breast cancer classification approach using transformers that operates effectively without large datasets. By incorporating parallel processing pathways for Discrete Cosine Transform (DCT) Attention and MobileConv, we convert image data from the spatial domain to the frequency domain to utilize the benefits such as filtering out high frequencies in the image, which reduces computational cost. This demonstrates the potential of our approach to improve breast cancer classification in histopathological images, offering a more efficient solution with reduced reliance on extensive annotated datasets. Our proposed model achieves an accuracy of 9 6 0 0% ± 0 4 8% for binary classification and 8 7 8 5% ± 0.93% for multiclass classification, which is comparable to state-of-the-art models while significantly reducing computational costs. This demonstrates the potential of our approach to improve breast cancer classification in histopathological images, offering a more efficient solution with reduced reliance on extensive annotated datasets. © 2024 IEEE.

关键词： Frequency domain analysis

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