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

共 11454 条记录，以下是41-50 订阅

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Frequency-Aware Hierarchical image Compression for Humans and machines

Frequency-Aware Hierarchical Image Compression for Humans an...

引用

2024 conference on Visual Communications and image processing

作者： Luo, Yue Zhang, Zixiang Kuang, Jinhao Yu, Li Huazhong Univ Sci & Technol Wuhan Peoples R China Huazhong Univ Sci & Technol Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

To achieve efficient compression for both human vision and machine perception, scalable coding methods have been proposed in recent years. However, existing methods do not fully eliminate the redundancy between features corresponding to different tasks, resulting in suboptimal coding performance. In this paper, we propose a frequency-aware hierarchical image compression framework designed for humans and machines. Specifically, we investigate task relationships from a frequency perspective, utilizing only HF information for machine vision tasks and leveraging both HF and LF features for image reconstruction. Besides, the residual block embedded octave convolution module is designed to enhance the information interaction between HF features and LF features. Additionally, a dual-frequency channel-wise entropy model is applied to reasonably exploit the correlation between different tasks, thereby improving multi-task performance. The experiments show that the proposed method offers -69.3%similar to-75.3% coding gains on machine vision tasks compared to the relevant benchmarks, and -19.1% gains over state-of-the-art scalable image codec in terms of image reconstruction quality.

关键词： scalable image coding image coding for machines learned image compression

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END-TO-END LEARNED SCALABLE MULTILAYER FEATURE COMPRESSION FOR machine vision TASKS 31

END-TO-END LEARNED SCALABLE MULTILAYER FEATURE COMPRESSION F...

引用

2024 International conference on image processing

作者： Chen, Qiaoxi Gao, Changsheng Liu, Dong Univ Sci & Technol China Hefei 230026 Anhui Peoples R China

ISBN: (纸本)9798350349405;9798350349399

In the field of Video Coding for machines (VCM), scalable feature compression has attracted attention for its potential to support a variety of machine vision tasks. However, the existing scalable feature compression methods exhibit limited performance. To address this problem, we propose an end-to-end learned scalable multilayer feature compression method in this paper. First, we propose to leverage an end-to-end feature compression method, which can efficiently exploit redundancy among features through a learning approach, to improve compression efficiency. Second, we introduce a novel strategy involving the use of the transformed latent of the base layer as the conditional information for the enhancement layer. Given the learnable nature of our compression method, we propose to optimize the base layer and the enhancement layer jointly. The joint optimization encourages the base layer to produce more suitable conditional information for the enhancement layer. Comparative experiments against existing feature compression and image compression methods verify our approach's remarkable performance improvements.

关键词： Feature compression machine vision scalable compression

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Recognition and Analysis of Glass Subsurface Defects by Integrating machine vision and image processing Technology 5

Recognition and Analysis of Glass Subsurface Defects by Inte...

引用

5th International conference on Computers, Information processing and Advanced Education, CIPAE 2024

作者： Lin, Chen Ling, Xiong Luo, Guangming Wuhan University of Science and Technology Wuhan China Changli Glass Honghu Co. Ltd Wuhan China

ISBN: (纸本)9798331527662

machine vision and computer image processing technologies are widely used in the metallurgical industry, especially in recognizing and analyzing defects in glass. High surfaces of planer surface and quality in the glass and metal manufacturing sector primarily require automated and performance visual detection, and inspection systems and algorithms are constantly improving. This article, therefore, seeks to comprehensively recognize and analyze glass subsurface defects through the integration of image processing technologies and machine vision. According to the algorithm's properties and the image features typified in the process, the defects of identified glass components are primarily classified based on determining the cause and the optical features of characteristics exhibited. Given the nature of varied and detected effects determined mainly by the classifiers, an improved accuracy level was notable, and a reduction of the computational complexity was evident following the use of machine vision and image processing technologies. © 2024 IEEE.

关键词： machine vision

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Advancing Industry 4.0 with Real-Time machine vision Integration in Cyber-Physical Systems 3

Advancing Industry 4.0 with Real-Time Machine Vision Integra...

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3rd International conference on Computing and machine Intelligence (ICMI)

作者： Hamza, Syed Amir Jesser, Alexander Heilbronn Univ HHN Inst Intelligent Cyber Phys Syst ICPS Heilbronn Germany

ISBN: (纸本)9798350372977;9798350372984

This study introduces a machine vision system integrated into cyber-physical systems (CPS) for enhanced industrial control. The system employs a specialized script for real-time image processing and edge detection, with a focus on precision and speed. Results showcase the system's rapid processing capabilities and high-accuracy feature detection, facilitated by machine learning algorithms that enable adaptability and iterative improvement. The system distinguishes itself by not only providing rapid and accurate feature recognition but also by outputting precise coordinates, crucial for micron-level manufacturing precision. An intuitive human-machine interface ensures seamless operation within industrial workflows. This integration significantly improves automated quality control and operational efficiency, demonstrating the system's potential to advance smart manufacturing in line with Industry 4.0 standards.

关键词： Cyber-Physical Systems (CPS) machine vision Systems Real-Time image processing Edge Detection Smart Manufacturing Industry 4.0 Automated Quality Control machine Learning Algorithms Precision Manufacturing

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Deep Curriculum Learning for PolSAR image Classification 12

Deep Curriculum Learning for PolSAR Image Classification

引用

12th iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Mousavi, Hamidreza Imani, Maryam Ghassemian, Hassan Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

Following the great success of curriculum learning in the area of machine learning, a novel deep curriculum learning method proposed in this paper, entitled DCL, particularly for the classification of fully polarimetric synthetic aperture radar (PolSAR) data. This method utilizes the entropy-alpha target decomposition method to estimate the degree of complexity of each PolSAR image patch before applying it to the convolutional neural network (CNN). Also, an accumulative mini-batch pacing function is used to introduce more difficult patches to CNN. Experiments on the widely used data set of AIRSAR Flevoland reveal that the proposed curriculum learning method can not only increase classification accuracy but also lead to faster training convergence. © 2022 IEEE.

关键词： Synthetic aperture radar

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Video Denoising using Temporal Coherency of Video Frames and Sparse Representation 12

Video Denoising using Temporal Coherency of Video Frames and...

引用

12th iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Torkashvand, Azadeh Behrad, Alireza Shahed University Electrical Engineering Department Tehran Iran

ISBN: (纸本)9781665412162

Sparse representation based on dictionary learning has been widely used in many applications over the past decade. In this article, a new method is proposed for removing noise from video images using sparse representation and a trained dictionary. To enhance the noise removal capability, the proposed method is combined with a block matching algorithm to take the advantage of the temporal dependency of video images and increase the quality of the output images. The simulations performed on different test data show the appropriate response of the proposed algorithm in terms of video image output quality. © 2022 IEEE.

关键词： image reconstruction

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Integrated machine vision and PLC Commanding for Efficient Bottle Label Detection in Industrial Processes: A Unified Approach for Quality Control 10

Integrated Machine Vision and PLC Commanding for Efficient B...

引用

10th International conference on Control, Automation and Robotic (ICCAR)

作者： Akhmetov, Miras Kanymkulov, Damir Amirov, Amir Askhatova, Almira Alizadeh, Tohid Nazarbayev Univ Dept Robot & Mechatron Astana Kazakhstan Heinrich Heine Univ Dusseldorf Fac Math & Nat Sci Dusseldorf Germany Nazarbayev Univ Dept Elect & Comp Engn Astana Kazakhstan

ISBN: (纸本)9798350373172;9798350373189

This paper presents an integrated approach to bottle label detection in industrial processes, combining machine vision techniques with Programmable Logic Controller (PLC) commanding for an efficient and reliable quality control system. The logic of the label detection station was developed using ladder diagrams to simulate the process and determine whether to retain or remove a bottle on the conveyor based on label presence. The image processing component, executed in Python, manipulated the main variable indicating the label's presence on the bottle. CODESYS was employed to visually represent the process, and in the simulation phase, the bottle traversed the conveyor, halting when the proximity sensor activated. Python code provided the labeling status, and the corresponding image was displayed on a simulation screen. To bridge the gap between image processing and logic control, the functionality of image processing in Python was linked with the CODESYS logic. An OPC UA server in CODESYS facilitated external connections, enabling Python to access and configure CODESYS variables. Challenges, such as scanning rate disparities between Python and CODESYS, were addressed through the introduction of sessions synchronized by a counter. Technical steps involved using an OPC UA client program to monitor server availability and access CODESYS program variables. The installation of the Security plugin in CODESYS ensured secure external connections. The project's key realization was the seamless linkage between image processing and PLC logic, demonstrating an effective integration of machine vision into industrial processes. For bottle label detection, the paper employed OpenCV for object recognition. The image segmentation method, utilizing adaptive thresholding, distinguished the bottle from its background, optimizing the separation process. Contours were identified using findContours(), and a thorough cleanup using arcLength() and approxPolyDP() functions ensured only relev

关键词： Bottle Label Detection machine vision image processing PLC Commanding Quality Control Industrial Automation OPC UA server

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HyperSpectral image Classification using an 3D Convolutional Mixer Block 32

HyperSpectral Image Classification using an 3D Convolutional...

引用

32nd International conference on Electrical Engineering (ICEE)

作者： Dianat, Sara Yazdi, Mehran Shiraz Univ Sch Elect & Comp Engn Dept Elect & Commun Engn Shiraz Iran

ISBN: (纸本)9798350376357;9798350376340

HyperSpectral image (HSI) processing has many applications in agriculture, Earth Change Monitoring, etc. Classification of HSIs is an important stage in most applications. Recently transformers have shown outstanding performance in computer vision tasks and some transformer-based methods have been introduced for HSI classification. In this paper, we propose a novel classification algorithm using the convolutional mixer for HSI classification. Convolutional mixer is similar to transformers and is used to mix spatial and spectral information which are gathered separately by using depth wise convolution followed by a point wise convolution. We have used 3D convolutions in mixer block due to the 3D shape of hyperspectral cuboids to extract spectral and spatial features simultaneously, which have not been done before, to the best of our knowledge. Experimental results show that our method is superior to similar state of the art works in terms of overall accuracy and Kappa Coefficient.

关键词： Hyperspectral image classification deep learning transformers 3D convolution

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Underwater image Enhancement using a Light Convolutional Neural Network and 2D Histogram Equalization 12

Underwater Image Enhancement using a Light Convolutional Neu...

引用

12th iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Khandouzi, Ali Ezoji, Mehdi Babol Noshirvani University of Technology Faculty of Electrical and Computer Engineering Babol Iran

ISBN: (纸本)9781665412162

Underwater images usually have low contrast, blurring, and extreme color distortion because the light is refracted, scattered, and absorbed as it passes through the water. These features can lead to challenges in image-based processing and analysis. In this paper, a two-step method based on a deep convolution network is proposed for solving these problems and enhancing underwater images. First, through a light global-local structure, the initial image enhancement is performed and the color distortion and degradation of the images are partially covered. Then, two-dimensional histogram equalization is used as an appropriate complement to the network. Two-dimensional histogram equalizing is able to produce clear results and prevents excessive contrast. The results show that the proposed method performs better than other methods in this field in terms of qualitative and quantitative criteria. © 2022 IEEE.

关键词： image enhancement

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Research on internal defect detection method based on machine vision 24

Research on internal defect detection method based on machin...

引用

1st International conference on image processing machine Learning and Pattern Recognition

作者： Dou, Xiaohan Chen, Lei Xu, Chugang Jiang, Zhihao Yuan, Haotian Yangtze Univ Jingzhou Peoples R China CNPC Bohai Drilling Engn Co Ltd Eastern Well Control Emergency Response Ctr Tianjin Peoples R China

ISBN: (纸本)9798400707032

The narrow internal space, inadequate lighting, and complex geometry of internally threaded pipes pose significant challenges for accurate detection. This paper presents a computer vision-based solution that enhances image acquisition efficiency through innovative hardware design. By utilizing cylindrical stitching algorithms, we achieved panoramic image stitching of internal threads, and automated defect detection was implemented using a YOLO-based object recognition algorithm. This research addresses specific industrial needs for internal thread detection in oil pipes. Compared to existing technologies, this method significantly increases detection speed, reduces the risk of human error, and improves production efficiency.

关键词： Object detection Object identification Hardware-software code-sign

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