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Airlight Estimation in Underwater image Restoration

Airlight Estimation in Underwater Image Restoration

引用

2023 International conference on Algorithm, Imaging processing, and machine vision, AIPMV 2023

作者： Chu, Jinlei Zhang, Zhanying Yu, Dongsheng Fang, Weikai Cai, Yi Xu, Chidong Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9781510672444

Underwater imaging is plagued by light absorption and scattering, resulting in distorted, blurry, and low-contrast. This paper introduces an innovative underwater image restoration algorithm that combines natural lighting-based airlight estimation with the refined dark channel prior. The algorithm directly estimates airlight, considering various underwater conditions such as depth, water quality, and camera-object distance, using the Jaffe-McGlamery underwater image formation model tailored for real-world underwater scenarios. A transmission map formula rooted in the refined dark channel prior is then derived. Finally, the algorithm employs the estimated airlight and transmission map to restore the image. Experimental results validate the algorithm's effectiveness in removing airlight artifacts, enhancing image contrast, and providing a clearer and more natural visual output. This approach promises to advance the quality of underwater imaging and its applicability across various domains. © 2024 SPIE.

关键词： image reconstruction

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A Few-shot Learning based Environmental Microorganism image Classification Method 2

A Few-shot Learning based Environmental Microorganism Image ...

引用

2nd International conference on machine vision, image processing and Imaging Technology, MVIPIT 2024

作者： Yuan, Jin Zhao, Peng Xu, Ning Li, Chen Jiang, Tao Northeastern University Shenyang China Shuangyang District Science and Technology Museum Changchun China Liaoning Petrochemical University Shenyang China Chengdu University of Traditional Chinese Medicine Chengdu China

ISBN: (纸本)9798331543037

Because of the difficulty of collecting Environmental Microorganisms (EMs) and the limited availability of publicly accessible environmental microbial image datasets, the progress of related scientific research has been hindered. The research of EMs is also in trouble because of the lack of samples. To this end, we establish the EMDS-6 dataset that can be used for deep learning. This dataset includes 21 types of EMs, with each type represented by 40 original images and 40 corresponding Ground Truth images, for a total of 1680 EMs images. Nevertheless, image classification of small EMs datasets on convolutional neural networks continues to yield suboptimal outcomes. In order to further solve the small data problem, we did a study on Few-shot Learning (FSL) on this basis. Furthermore, leveraging self-supervision, our approach enhanced the accuracy to 57.40% across 21 EMs classes, demonstrating the potential of FSL and Self-Supervision in the image classification of compact EMs datasets. ©2024 IEEE.

关键词： Self-supervised learning

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A Preprocessing Method for Fragment Perforation image Detection 2

A Preprocessing Method for Fragment Perforation Image Detect...

引用

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

作者： Lu, Guangzhao Sun, Yong Huang, Dan Zhao, Jinqing China Research and Development Academy of Machinery Equipment Beijing China

ISBN: (纸本)9798350331417

The perforation data left by the fragments generated by the explosion of ammunition warheads on the surrounding metal plates can reflect the dispersion characteristics of the fragments, which is a commonly used ammunition power testing method. Using image detection based methods to analyze metal plate perforation images can quickly and accurately detect the number and size of fragment perforation. Therefore, image quality is crucial for the accuracy of the analysis results. This study proposes an image preprocessing method for detecting metal plate perforation images, and compares and analyzes the effects of different filtering methods, morphological processing methods, and perforation edge detection methods on the image preprocessing results, The results show that using median filtering, open operation morphological processing methods, and edge detection methods based on Gaussian fitting models can obtain preprocessed images that better reflect the true bullet hole information. © 2023 IEEE.

关键词： image preprocessing metal plate perforation

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Classification of Philippine Soybean Variety Using image processing Technique and machine Learning Method

Classification of Philippine Soybean Variety Using Image Pro...

引用

IEEE International conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Diamante, John Christian Dioses, Isaac Angelo M. Dioses, Jesusimo L., Jr. Tomas, John Paul Q. Mapua Univ Sch Informat Technol Makati Metro Manila Philippines Isabela State Univ Coll Comp Studies Informat & Commun Technol San Fabian Echague Isabela Philippines

ISBN: (纸本)9798350372113;9798350372106

Together with dairy and wheat, soybeans are a major agricultural import into the Philippines. Historically, imports accounted for 99% of the country's supply of soybeans from 1995 to 2014, with local manufacturers making up the remaining 1%. The emergence of novel technologies has enabled the classification of diverse agricultural commodities, such as soybean cultivars, by merging computer vision and machine learning methodologies that utilize edge detection algorithms. Precise categorization of seed variants is essential for farmers and seed manufacturers to maintain variety purity, which in turn affects crop productivity and the quality of soybeans provided to nearby retailers. Morphological features were retrieved from pre-processed soybean pictures using the regionprops function, which made use of edge detection methods. After the extraction of features, the data was subjected to pre-processing and machine learning analysis. The KNearest Neighbors (KNN) model classified the data using Euclidean distance. A 75:25 split of the dataset was made into training and testing subsets, with five neighbors being used for categorization. The CL1 and PSB SY2 soybean varieties were classified by the KNN model with an accuracy rate of 85%, indicating a first step in variety classification research.

关键词： machine learning KNN MATLAB image processing edge detection canny

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Research on Shaking Video image Stabilization Algorithm for Moving Object Detection 2

Research on Shaking Video Image Stabilization Algorithm for ...

引用

2nd International conference on Algorithm, image processing and machine vision, AIPMV 2024

作者： Wang, Chuangchuang Jia, Zhenhong Huang, Xiaohui Song, Sensen Wang, Jiajia Zhou, Gang Shi, Fei Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi China

ISBN: (纸本)9798350390254

Video captured by surveillance equipment will jitter due to the shaking of the equipment, this jitter will affect the detection results of moving target detection algorithms that rely on stable video frames. This paper proposes an improved ORB algorithm to solve the video jitter problem, so that the moving target detection algorithm can accurately detect moving targets in jittery videos. First, wavelet transform is used to locate the high-frequency area of the image, and then feature extraction is performed on the area, which improves the efficiency of ORB feature extraction. And use Boosted Efficient Binary Local image Descriptor (BEBLID) to replace the descriptors of directional FAST and rotation BRIEF (ORB) to improve matching accuracy. In the feature matching stage, a neighborhood query method is proposed to replace the global search of the traditional BFMatcher, which improves the matching speed. Finally, Progressive Sample Consistency (PROSAC) is employed to ensure accurate matching of point pairs, resulting in a motion matrix for video stabilization. Finally, a Gaussian mixture model with adaptive distribution numbers is combined to quickly detect moving targets. Comparative experiments with scale-invariant feature transform(SIFT), Accelerated-KAZE(AKAZE), ORB, Qtree _ ORB and SIRB prove the superior accuracy and speed of this algorithm. © 2024 IEEE.

关键词： Wavelet transforms

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image segmentation of rail surface defects based on fractional order particle swarm optimization 2D-Otsu algorithm

Image segmentation of rail surface defects based on fraction...

引用

2023 International conference on Algorithm, Imaging processing, and machine vision, AIPMV 2023

作者： Geng, Na Sheng, Hu Sun, Weizhi Wang, Yifeng Yu, Tan Liu, Zihan School of Computer and Communication Engineering Dalian Jiaotong University Liaoning Province Dalian116028 China Innovation Center The 47th Research Institute of China Electronics Technology Group Corporation Liaoning Province Shenyang110000 China

ISBN: (纸本)9781510672444

Under the influence of high density operation and natural environment, the rail surface will appear abrasion damage, which will affect the safety and comfort of the train. Rail surface defect detection is an important part to ensure the safe and efficient operation of railway system. In order to distinguish whether there are defects on the rail surface, a method of rail surface defect image segmentation based on FPSO 2D-Otsu algorithm is proposed. The rail image is denoised and enhanced by adaptive fractional calculus, and then the rail image is segmented by FPSO 2D-Otsu algorithm. In order to verify the accuracy of the algorithm, the proposed algorithm is compared with PSO 2D-Otsu image segmentation algorithm. The experimental results show that the accuracy of FPSO 2D-Otsu algorithm in rail image segmentation is improved from 48.76% to 83.59% compared with PSO 2D-Otsu algorithm. © 2024 SPIE.

关键词： Surface defects

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Visual damage detection technology for conveyor belts in coal-fired power plants 3

Visual damage detection technology for conveyor belts in coa...

引用

3rd International conference on Energy and Power Engineering, Control Engineering (EPECE)

作者： Cheng, Weiliang Yin, Xinwei Wang, Yanchun North China Elect Power Univ Sch Energy Power & Mech Engn Beijing 102206 Peoples R China Datang Environm Ind Grp Co Ltd Beijing 100097 Peoples R China

ISBN: (纸本)9798350366105;9798350366099

Chinese coal-fired power plants generally use cantilever bucket wheel machines to stack and retrieve coal from their coal yards. The belt conveyor of the bucket wheel machine is the main transportation equipment for coal fuels. During the operation of a belt conveyor, the conveyor belt is prone to damage and often experiences longitudinal tearing. Once longitudinal tearing occurs, if not detected in time, it may damage the entire conveyor belt and cause huge losses to the production of coal-fired power plants. It is of great significance to develop a visual based conveyor belt damage detection technology to improve the efficiency and accuracy of fault detection for coal yard conveyor belts in coal-fired power plants, and ensure the safety of bucket wheel equipments. Long wave and medium wave infrared CCD ( Charge coupled device) cameras are used for image acquisition. After algorithmic processing of the infrared radiation intensity with the image information, it is converted into corresponding grayscale images. The traditional two-dimensional Otus image processing algorithm was optimized using the lion group algorithm, and the chaotic sequence mechanism was introduced to achieve the optimal binary image threshold segmentation. The FAST(Features from accelerated segment test) algorithm is used to discover image corners for image features and fault recognition. Based on the refinement algorithm, the longitudinal tearing defect of the images is further determined using the Hough transform. Based on this, this article proposes a vision based conveyor belt damage detection and analysis method, which overcomes the harsh environmental requirements of actual sites and adds optimization and secondary processing processes. The research results indicate that this method can effectively monitor and identify damage to conveyor belts, with recognition accuracy of over 98%, and corresponding prediction measures can be proposed to further prevent the increase of damage.

关键词： Algorithm Computer vision Belt conveyor Damage detection Bucket wheel machine

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Research on Computer vision in Object Detection

Research on Computer Vision in Object Detection

引用

2022 International conference on Mathematics, Modeling, and Computer Science, MMCS 2022

作者： Wei, Xiang Ren, Youli Huang, Mian Yunnan Land and Resources Vocational College Yunnan China

ISBN: (纸本)9781510664593

Object detection is a hot topic in the field of machine vision. In the innovation and development of modern technology, the integration of machine learning, pattern recognition, image processing and other multidisciplinary knowledge can effectively solve the image problem. In computer vision and digital image processing technology in the development of innovation, target detection theory research and practical application has achieved excellent results, although there are many problems need to solve, but a lot of new technology and new method has yet to be developed, so countries scholars gradually strengthen the scientific research strength, strong adaptability are put forword technique, high precision and good robustness. In this paper, based on the understanding of the research status of object detection, according to the basic theory of image processing, the application of computer vision in object detection is analyzed. The final experimental results show that shadow detection, image enhancement and filtering methods can effectively improve the application effect. © 2023 SPIE.

关键词： Object detection

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Research and design of machine vision-based defect detection method for 3C digital screens with Multi-feature fusion 3

Research and design of machine vision-based defect detection...

引用

3rd International conference on image processing and Media Computing (ICIPMC)

作者： Zhao Jianjun Dong Xiang Zhu Zhenggen Zheng Zhong Anhui Univ Sch Elect Engn & Automat Hefei 230601 Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386660;9798350386677

This paper aims at the current digital screen defect detection in the field of the feature region is not clear, a single screen samples on the screen and the line part of the complexity of the problem of simultaneous detection. It proposes an inspection system design method that realizes digital screen defect detection and classification by superimposing the sample bright field and dark field images, complementing traditional machine vision and computer vision solutions, and adopting a multi-feature fusion method to detect the rows of wires that cannot be detected due to overexposure in the bright field by using dark field images;and detecting the main part of the screen with lower contrast in the dark field by using bright field images. The algorithm adopts the improved YOLOv5 deep learning model, and the feature extraction network selects the lightweight GhostNet, and the feature information extracted from the screen pictures is fused, defect recognition and classification through the bidirectional multi-scale fusion network. Comparison with the manual detection results shows that: the detection rate accuracy of the combined bright and dark field defect detection device using the improved YOLOv5 algorithm is 6.8% higher than that of the original YOLOv5 algorithm device;and the detection time of a single piece of defect detection is reduced by 67.4% compared with that of the manual defect detection. The experimental results prove that the defect detection device has high value and research significance.

关键词： Multi-feature YOLOv5 Defect Detection

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Intelligent patrol wheeled robot based on image recognition 3

Intelligent patrol wheeled robot based on image recognition

引用

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

作者： Wu, Wei Lv, Xinyue Pei, Zeyu An, Changying Ning, Jixiang Gao, Zhenzhen College of Engineering China University of Petroleum-Beijing at Karamay Karamay834000 China

ISBN: (纸本)9781510681880

In recent years, with the development of artificial intelligence technology, intelligent robots are more and more widely used in many fields. In this paper, an intelligent patrol wheeled robot based on image recognition is designed and implemented. The robot utilizes Raspberry PI as its core control unit and integrates sensing equipment such as cameras and ultrasonic sensors. The functions of autonomous navigation, intelligent obstacle avoidance and path tracking are improved through computer vision and machine learning technology. The main technologies include OpenCV for image processing, HOG feature extraction and SVM algorithm for traffic sign recognition, and traffic light detection system based on color recognition. In addition, this paper also discusses the use of positional PID control algorithm to achieve visual lane keeping scheme. Through detailed system design and experimental verification, this paper shows the performance benefits of the intelligent robot in practical applications, and provides a new idea for the future research of intelligent robots. It is expected that the robot has a wide range of application potential in intelligent transportation, public safety and other fields. © 2024 SPIE.

关键词： Intelligent robots

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