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2015 Information Technology and Mechatronics Engineering Conference (ITOEC 2015)

作者： Fang Weijian Xin Zhou Chongqing Three Gorges University

As a vital part of Intelligent Transportation System,License Plates Recognition System is meaningful in Vehicle Positioning and Traffic *** consists of Vehicle License Plate Location,Character segmentation and Character *** the paper we put forward a new method of segmentation algorithm on the basis of comparison and analysis some common methods of License Plate Recognition,which firstly chooses the self-adaptive thresholds for license images and executes the binarization;then arranges with projection Method and fixed-edge methods on the basis of connected-area *** experiment's result shows this new method has better reco gnition performance.

关键词： License Plate Recognition Binarization Image segmentation segmentation algorithm

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The Research on Image Extraction and segmentation algorithm in License Plate Recognition

The Research on Image Extraction and Segmentation Algorithm ...

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2011 13th IEEE Joint International Computer Science and Information Technology Conference(JICSIT 2011)

作者： Weijian Fang Network Center Chongqing Three Gorges University Chongqing, China Xin Zhou College of Physics and Electronic Engineering Chongqing Three Gorges University Chongqing, China

As a vital part of Intelligent Transportation System, License Plates Recognition System is meaningful in Vehicle Positioning and Traffic Monitoring. It consists of Vehicle License Plate Location, Character segmentation and Character Recognition. In the paper we put forward a new method of segmentation algorithm on the basis of comparison and analysis some common methods of License Plate Recognition, which firstly chooses the self-adaptive thresholds for license images and executes the binarization;then arranges with projection Method and fixed-edge methods on the basis of connected-area methods. The experiment's result shows this new method has better recognition performance.

关键词： License Plate Recognition Binarization Image segmentation segmentation algorithm

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Research on segmentation algorithm of Adhesive Plant Grain Image

Research on Segmentation Algorithm of Adhesive Plant Grain I...

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第八届国际电子测量与仪器学术会议(Proceedings of 2007 8th International Conference on Electronic Measurement & Instruments)

作者： Gao Hua Wang Yaqin Ge Pingju College of Info.Science and Engineering SDAUTaian 271018China

The region and boundary characteristics of adhesive plant grain image are analyzed firstly in this paper. Then a method for judging the adhesion of grain is proposed by making use of image regional area threshold. Based on the analysis of various kinds of adhesion, a grain cluster segmentation algorithm for distinguishing split point of grain adhesion is put forward too, which makes full use of concavity-convexity of image boundary. Finally, the algorithm flow is presented in detail.

关键词： plant grain image processing segmentation algorithm boundary chain-code

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Cheese quality evaluation by image segmentation: Image segmentation algorithm for broken surface evaluation of Bulgarian white cheese in brine 19

Cheese quality evaluation by image segmentation: Image segme...

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Proceedings of the 20th International Conference on Computer Systems and Technologies

作者： Angel Danev Atanaska Bosakova-Ardenska Petya Boyanova Peter Panayotov Lena Kostadinova-Georgieva Computer Systems and Technologies University of Food Technologies Plovdiv Bulgaria Milk and Dairy products Technology University of Food Technologies Plovdiv Bulgaria

ISBN: (纸本)9781450371490

This paper presents an algorithm for image segmentation in HSI color space with application in cheese quality evaluation. The proposed algorithm uses two approaches to calculate segmentation thresholds for intensity, saturation and hue components- using medians and using average values. For experiments, there are used nineteen trademarks Bulgarian white cheese in brine that are produced of cow milk. The samples of cheese are evaluated by experts according to standard BDS 15612-83 for sensory evaluation of milk and dairy products. It is developed a special Likert scale to support expert's evaluation of broken surface of cheese (structure of this surface gives significant information about process of cheese production). All examined samples of cheese are captured with digital camera and with the proposed segmentation algorithm are processed their images. The ratio of white pixels to all, light gray, dark gray and black pixels to all are used for correlation analysis in comparison with score for broken surface which is given by experts using proposed Likert scale. The ratio of light gray pixels and white pixels to all pixels give the highest correlation coefficient with experts' evaluation and they could be used for objective evaluation of broken surface of white brined cheese.

关键词： white cheese in brine broken surface segmentation algorithm HSI Cheese quality

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Improvement Based on an Instance segmentation algorithm

Improvement Based on an Instance Segmentation Algorithm

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2018 4th International Conference on Systems,Computing,and Big Data(ICSCBD 2018)

作者： Chen Mingyang College of Computer Science and Technology Qingdao University

In recent years, Mask-R-CNN has achieved great success in the field of image segmentation. This paper proposes an accurate image segmentation method based on *** improve the boundary segmentation precision by modifying the mask branch structure. Used feature fusion on the mask branch to enhance the combination of semantic information and shallow information, and improve the segmentation effect. Expanded the size of the feature map of the RoIAlign layer, get more accurate location information. The experiment shows the accuracy of the algorithm.

关键词： segmentation algorithm Improvement

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Atmospheric Scattering Model Combined With Image segmentation in Foggy Image Enhancement

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IEEE ACCESS 2025年 13卷 30732-30744页

作者： Liu, Qiaoling Yu, Na Cheng, Jinfu Chengdu Univ Sch Elect Informat & Elect Engn Chengdu 610106 Peoples R China Datong Vocat & Tech Coll Coal Dept Mech & Elect Engn Datong 037000 Peoples R China

In order to solve the problem of obtaining clear images in foggy weather, this study conducted research on combining physical imaging models with Ostu threshold image segmentation algorithms, aiming to optimize computer defogging algorithms, improve the defogging processing performance and detail restoration ability of foggy images. The study analyzed the changes in transmittance of the research scene based on the atmospheric scattering model, and achieved image restoration of different fog concentration areas through image segmentation, thereby improving the defogging efficiency. The comparative test results show that the algorithm performs excellently in terms of convergence speed, fitness value, accuracy, and recall rate. In addition, the average errors of the model in the training and test sets were 1.35 and 0.98, respectively, which were smaller than those of other comparison algorithms, demonstrating superior analytical capabilities. The inference time of the proposed algorithm on the training and test sets was 0.97 s and 0.95 s, respectively, which was the fastest among all algorithms. At the same time, the algorithm processed 2.5 million samples on the training and test sets, using 124MB and 120MB of memory respectively, with floating-point operations of 8.4 GFLOP and 8.5 GFLOP, respectively. It demonstrated a good balance between efficiency and resource utilization. The experimental results of the defogging effect showed that the proposed algorithm outperformed other compared algorithms in terms of mean square error, peak signal-to-noise ratio, and structural similarity when processing foggy images in different scenarios, indicating that the algorithm could make the restored images closer to fog free images in different scenarios. Moreover, the image visibility and visual contrast of this algorithm were 87.83% and 86.52%, respectively, which were 37.48% and 46.19% higher than traditional algorithms. The experimental results have verified the effectiveness a

关键词： Image enhancement image defogging atmospheric scattering model segmentation algorithm dark channel prior Image enhancement image defogging atmospheric scattering model segmentation algorithm dark channel prior

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Enhancing diabetic retinopathy diagnosis: automatic segmentation of hyperreflective foci in OCT via deep learning

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INTERNATIONAL OPHTHALMOLOGY 2025年第1期45卷 1-18页

作者： Li, Yixiao Yu, Boyu Si, Mingwei Yang, Mengyao Cui, Wenxuan Zhou, Yi Fu, Shujun Wang, Hong Liu, Xuya Zhang, Han Shandong Univ Shandong Prov Hosp Cheeloo Coll Med Dept Ophthalmol Jinan 250012 Shandong Peoples R China Chang Guang Satellite Technol Co Ltd Changchun 130102 Jilin Peoples R China Shandong Univ Qilu Hosp Dept Ophthalmol Jinan 250012 Shandong Peoples R China Shandong Univ Sch Math Jinan 250100 Shandong Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250101 Shandong Peoples R China Xuzhou Med Univ Affiliated Hosp Dept Ophthalmol Xuzhou 221002 Jiangsu Peoples R China

ObjectiveHyperreflective foci (HRF) are small, punctate lesions ranging from 20 to 50 mu\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document}m and exhibiting high reflective intensity in optical coherence tomography (OCT) images of patients with diabetic retinopathy (DR). The purpose of the model proposed in this paper is to precisely identify and segment the HRF in OCT images of patients with DR. This method is essential for assisting ophthalmologists in the early diagnosis and assessing the effectiveness of treatment and prognosis. In this study, we introduce an HRF segmentation algorithm based on KiU-Net, the algorithm that comprises the Kite-Net branch using up-sampling coding to collect more detailed information and a three-layer U-Net branch to extract high-level semantic information. To enhance the capacity of a single-branch network, we also design a cross-attention block (CAB) which combines the information extracted from two branches. The experimental results demonstrate that the number of parameters of our model is significantly reduced, and the sensitivity (SE) and the dice similarity coefficient (DSC) are respectively improved to 72.90%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} and 66.84%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}. Considering the SE and precision(P) of the segmentation, as well as the recall ratio and recall P of HRF, we believe that this m

关键词： Hyperreflective foci Deep learning segmentation algorithm Diabetic retinopathy

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Automatic body condition scoring system for dairy cows in group state based on improved YOLOv5 and video analysis

ARTIFICIAL INTELLIGENCE IN AGRICULTURE

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ARTIFICIAL INTELLIGENCE IN AGRICULTURE 2025年第2期15卷 350-362页

作者： Li, Jingwen Zeng, Pengbo Yue, Shuai Zheng, Zhiyang Qin, Lifeng Song, Huaibo Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligent Yangling 712100 Shaanxi Peoples R China

This study proposes an automated scoring system for cow body condition using improved YOLOv5 to assess the body condition distribution of herd cows, which significantly impacts herd productivity and feeding management. A dataset was created by capturing images of the cow's hindquarters using an image sensor at the entrance of the milking hall. This system enhances feature extraction ability by introducing dual path networks and convolutional block attention modules and improves efficiency by replacing some modules from the standard YOLOv5s with deep separable convolution to reduce parameters. Furthermore, the system employs an automatic detection and segmentation algorithm to achieve individual cow segmentation and body condition acquisition in the video. Subsequently, the system computes the body condition distribution of cows in a group state. The experimental findings demonstrate that the proposed model outperforms the original YOLOv5 network with higher accuracy and fewer computations and parameters. The precision, recall, and mean average precision of the model are 94.3 %, 92.5 %, and 91.8 %, respectively. The algorithm achieved an overall detection rate of 94.2 % for individual cow segmentation and body condition acquisition in the video, with a body condition scoring accuracy of 92.5% among accurately detected cows and an overall body condition scoring accuracy of 87.1 % across the 10 video tests. (c) 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： BCS Distribution statistics YOLOv5 segmentation algorithm Dairy cows

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Researches on the tender leaf identification and mechanically perceptible plucking finger for high-quality green tea

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JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2025年第4期105卷 2169-2178页

作者： Zhang, Wei Chen, Yong Wang, Qianqian Chen, Jun Nanjing Normal Univ Sch Elect & Automat Engn Nanjing 210023 Peoples R China Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing Peoples R China

BACKGROUNDIntelligent identification and precise plucking are the keys to intelligent tea harvesting robots, which are currently of increasing significance. Aiming at plucking tender leaves for high-quality green tea production, in this study, a tender leaf identification algorithm and a mechanically perceptible plucking finger have been *** on the segmentation algorithm and color features, the tender leaf identification algorithm shows an average identification accuracy of over 92.8%. The mechanically perceptible plucking finger plucks tender leaves in a way that a human hand does, aiming to maintain the high quality of tea products. Though finite element analysis, we determine the ideal size of grippers and the location of strain gauge attachment on a gripper to enable the employment of feedback control of desired gripping force. As revealed in our experiments, the success rate of tender leaf plucking reaches 92.5%, demonstrating the effectiveness of our *** results show that the tender leaf identification algorithm and the mechanically perceptible plucking finger are effective for identification of tender leaves and plucking, providing a foundation for the development of an intelligent tender leaf plucking robot. (c) 2024 Society of Chemical Industry.

关键词： high-quality green tea mechanically perceptible plucking finger tender leaf identification algorithm segmentation algorithm

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A concept for fully automated segmentation of bone in ultrasound imaging

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SCIENTIFIC REPORTS 2025年第1期15卷 1-18页

作者： Ramakrishnan, Ananth Hari Rajappa, Muthaiah Krithivasan, Kannan Chockalingam, Nachiappan Chatzistergos, Panagiotis E. Amirtharajan, Rengarajan SASTRA Deemed Univ Sch Comp Thanjavur 613401 India Staffordshire Univ Ctr Biomech & Rehabil Technol Sch Hlth Sci & Wellbeing Stoke On Trent ST4 2DE England SASTRA Deemed Univ Sch Elect & Elect Engn Thanjavur 613401 India Univ Dundee Biomed Engn Dundee Scotland

This study proposes a novel concept for the automated and computerised segmentation of ultrasound images of bone based on motion information. Force is applied on the heel region using the ultrasound probe and then removed while recording the video of the bone using ultrasound. The interface between the bone and surrounding tissues is the region that moves with maximum speed. This concept is utilised to determine a map of movement, where speed is the criterion used for the bone segmentation from the surrounding tissues. To achieve that, the image is subdivided into regions of uniform sizes, followed by tracking individual regions in the successive frames of the video using an optical flow algorithm. The average movement speed is calculated for the regions. Then, the regions with the higher speed are identified as bone surfaces. It is given as the initial contour for the Chan-Vese algorithm to achieve smoother bone surfaces. Then, the final output from the Chan-Vese is post-processed using a boundary tracing algorithm to get the last automated bone segmented output. The segmented outcomes are compared against the manually segmented images from the experts to determine the accuracy. Bhattacharyya distances are used to calculate the accuracy of the algorithmic and manual output. The quantitative results from Bhattacharyya distances indicated an excellent overlap between algorithmic and manual works (average +/- STDEV Bhattacharyya distance: 0.06285 +/- 0.002051). The bone-segmented output from the optical flow algorithm is compared with the model output and the texture-based segmentation method's output. The work from the motion estimation methods has better segmentation accuracy than the model and texture segmentation methods. The results of this study suggest that this method is the first attempt to segment the heel bone from the ultrasound image using motion information.

关键词： Heel bone Map of movement segmentation algorithm Optical-flow Chan-Vese

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