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Deer Body Adaptive Threshold Segmentation Algorithm Based on color space

Computers, Materials & Continua 2020年第8期64卷 1317-1328页

作者： Yuheng Sun Ye Mu Qin Feng Tianli Hu He Gong Shijun Li Jing Zhou College of Information Technology Jilin Agricultural UniversityChangchun130118China Jilin Province Agricultural Internet of Things Technology Collaborative Innovation Center Changchun130118China Jilin Province Intelligent Environmental Engineering Research Center Changchun130118China Jilin Province Colleges and Universities and the 13th Five-Year Engineering Research Center Changchun130118China Wuhan Maritime Communication Research Institute Wuhan430205China Fruit Research and Extension Center Department of Agricultural and Biological EngineeringPenn State UniversityPAUSA

In large-scale deer farming image analysis,K-means or maximum between-class variance(Otsu)algorithms can be used to distinguish the deer from the background.However,in an actual breeding environment,the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer.Also,when the target and background grey values are similar,the multiple background targets cannot be completely separated.To better identify the posture and behaviour of deer in a deer shed,we used digital image processing to separate the deer from the background.To address the problems mentioned above,this paper proposes an adaptive threshold segmentation algorithm based on color space.First,the original image is pre-processed and optimized.On this basis,the data are enhanced and contrasted.Next,color space is used to extract the several backgrounds through various color channels,then the adaptive space segmentation of the extracted part of the color space is performed.Based on the segmentation effect of the traditional Otsu algorithm,we designed a comparative experiment that divided the four postures of turning,getting up,lying,and standing,and successfully separated multiple target deer from the background.Experimental results show that compared with K-means,Otsu and hue saturation value(HSV)+K-means,this method is better in performance and accuracy for adaptive segmentation of deer in artificial breeding scenes and can be used to separate artificially cultivated deer from their backgrounds.Both the subjective and objective aspects achieved good segmentation results.This article lays a foundation for the effective identification of abnormal behaviour in sika deer.

关键词： Artificial breeding color space deer body recognition image segmentation K-means multi-target recognition Otsu

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Automatic Segmentation of Pallet Images Using color Statistics and Mixture color space

Automatic Segmentation of Pallet Images Using Color Statisti...

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2012 International Conference on Information and Computer Applications (ICICA 2012)

作者： Ssu-Wei Chen Luke K.Wang Jen-Hong Lan Department of Electrical Engineering National Kaohsiung University of Applied SciencesKaohsiungTaiwan.

In this paper,we propose the mixture color model segmentation and detection method.Destination is to enable forklifts to search for pallets,but it is able to meet fully automated system with realtime.We focused on pallets for image segmentation and detection.First,we manually segmented 30 pallet images and statistics of the best color threshold,this method must find the threshold of different color space and mixture of two important color spaces containing HSV and YCbCr,we extracted the H and the Cb composition mixtures to find the best color threshold,then used the logic function to keep our information after obtaining the color image segmentation,the noise of the image must be removed,this algorithm can be used on video sequences efficiently.Finally,experimental results show that the method has effective segmentation and detection in the video sequences.

关键词： Image Segmentation Image Detection color space Machine Vision.

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Identification Method of Forest Fire Based on color space

Identification Method of Forest Fire Based on Color Space

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2010 The 2nd International Conference on Industrial Mechatronics and Automation (ICIMA 2010)

作者： Fei Yan Xing Xu Ning Han School of Technology Beijing Forestry University Beijing China

Forest fire is a kind of natural disasters with huge effect on the ecological environment of the earth.Flame color and brightness are two inherent features of the flame itself which are important static identifiable information.In order to identify the flame effectively and fight the forest fire in time the flame region may be identified based on these features in the RGB color space and HSV color space.The strengths and weaknesses of extracting the flame region with these two different color spaces are compared.At the same time because the value range of parameter H is discontinuous when converting the RGB color space to the HSV color space,the conversion formula is modified.Many experiments and tests indicate that this method is verified to be effective.

关键词： Forest Fire Identification RGB HSV color space

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Illumination-Free Clustering Using Improved Slime Mould Algorithm for Acute Lymphoblastic Leukemia Image Segmentation

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Journal of Bionic Engineering 2023年第6期20卷 2916-2934页

作者： Krishna Gopal Dhal Swarnajit Ray Sudip Barik Arunita Das Department of Computer Science and Application Midnapore College(Autonomous)Paschim MedinipurMidnaporeWest BengalIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Kalyani Government Engineering CollegeKalyaniWest BengalIndia

Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image segmentation.But,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local optima.In addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing process.Therefore,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)segmentation.The ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some extent.This paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image clustering.Numerical and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image segmentation.ISMA-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus segmentation.Whereas,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,respectively.On the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).

关键词： Pathology image Image segmentation Clustering color space White blood cell Optimization Swarm intelligence Fuzzy clustering Rough clustering

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Research on Non-linear Dynamic Systems Employing color space

Research on Non-linear Dynamic Systems Employing Color Space

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2000年第16届世界计算机大会

作者： Li Shujun Wang Peng Mu Xuanqin Cai Yuanlong Image Processing Center of Xi’an Jiaotong Univ.

＜正＞This paper introduces a new method of research on evolvement of non-linear dynamic system employing color space,such as fractal sets and chaos systems.Contrast to traditionary methods,it has more intuitionistic display and can express more information of systems,and we can observe more hidden features of dynamic systems by employing it. And you can also obtain the bifurcation figures,final iterative figures.This method is mainly used for researches on those iterative systems whose dimesion is lower than 3.It also can be used for researches of low dimensions mapping characteristics of those highdimension dynamic systems.This paper gives three samples on Mandelbrot/Julia set,Logistic mapping and Henon trajectory.

关键词： dynamics color space fractal chaos bifurcation Mandelbrot Julia Logistic

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Combination of effective color information and machine learning for rapid prediction of soil water content

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第9期15卷 2441-2457页

作者： Guanshi Liu Shengkui Tian Guofang Xu Chengcheng Zhang Mingxuan Cai State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan430071China Guangxi Key Laboratory of Rock and Soil Mechanics and Engineering Guilin University of TechnologyGuilin541004China

Soil water content(SWC)is one of the critical indicators in various fields such as geotechnical engineering and agriculture.To avoid the time-consuming,destructive,and laborious drawbacks of conventional SWC measurements,the image-based SWC prediction is considered based on recent advances in quantitative soil color analysis.In this study,a promising method based on the Gaussian-fitting gray histogram is proposed for extracting characteristic parameters by analyzing soil images,aiming to alleviate the interference of complex surface conditions with color information extraction.In addition,an identity matrix consisting of 32 characteristic parameters from eight color spaces is constituted to describe the multi-dimensional information of the soil images.Meanwhile,a subset of 10 parameters is identified through three variable analytical methods.Then,four machine learning models for SWC prediction based on partial least squares regression(PLSR),random forest(RF),support vector machines regression(SVMR),and Gaussian process regression(GPR),are established using 32 and 10 characteristic parameters,and their performance is compared.The results show that the characteristic parameters obtained by Gaussian-fitting can effectively reduce the interference from soil surface conditions.The RGB,CIEXYZ,and CIELCH color spaces and lightness parameters,as the inputs,are more suitable for the SWC prediction models.Furthermore,it is found that 10 parameters could also serve as optimal and generalizable predictors without considerably reducing prediction accuracy,and the GPR model has the best prediction performance(R^(2)≥0.95,RMSE≤2.01%,RPD≥4.95,and RPIQ≥6.37).The proposed image-based SWC predictive models combined with effective color information and machine learning can achieve a transient and highly precise SWC prediction,providing valuable insights for mapping soil moisture fields.

关键词： Soil water content(SWC) Digital image Soil color color space Machine learning

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Saturation-Value Blind color Image Deblurring with Geometric Spatial-Feature Prior

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Communications in Computational Physics 2023年第3期33卷 795-823页

作者： Hao Zhang Yingying Fang Hok Shing Wong Lihua Li Tieyong Zeng Department of Mathematics The Chinese University of Hong KongHong Kong National Heart and Lung Institute Imperial College LondonLondonUnited Kingdom College of Life Information Science and Instrument Engineering Hangzhou Dianzi UniversityHangzhouChina.

Blind deblurring for color images has long been a challenging computer vision task.The intrinsic color structures within image channels have typically been disregarded in many excellent works.We investigate employing regularizations in the hue,saturation,and value(HSV)color space via the quaternion framework in order to better retain the internal relationship among the multiple channels and reduce color distortions and color artifacts.We observe that a geometric spatial-feature prior utilized in the intermediate latent image successfully enhances the kernel accuracy for the blind deblurring variational models,preserving the salient edges while decreasing the unfavorable structures.Motivated by this,we develop a saturation-value geometric spatial-feature prior in the HSV color space via the quaternion framework for blind color image deblurring,which facilitates blur kernel estimation.An alternating optimization strategy combined with a primal-dual projected gradient method can effectively solve this novel proposed model.Extensive experimental results show that our model outperforms state-of-the-art methods in blind color image deblurring by a wide margin,demonstrating the effectiveness of the proposed model.

关键词： Blind color image deblurring quaternion geometric spatial-feature prior color space

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A Two-Stage color Image Segmentation Method Based on Saturation-Value Total Variation

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Advances in Applied Mathematics and Mechanics 2023年第1期15卷 94-117页

作者： Tiange Wang Hok Shing Wong Department of Mathematics The Chinese University of Hong KongHong Kong

color image segmentation is crucial in image processing and computer vision.Most traditional segmentation methods simply regard an RGB color image as the direct combination of the three monochrome images and ignore the inherent color structures within channels,which contain some key feature information of the image.To better describe the relationship of color channels,we introduce a quaternion-based regularization that can reflect the image characteristics more intuitively.Our model combines the idea of the Mumford-Shah model-based two-stage segmentation method and the Saturation-Value Total Variation regularization for color image segmentation.The new strategy first extracts features from the color image and then subdivides the image in a new color feature space which achieves better performance than methods in RGB color space.Moreover,to accelerate the optimization process,we use a new primal-dual algorithm to solve our novel model.Numerical results demonstrate clearly that the performance of our proposed method is excellent.

关键词： color space pure quaternion image segmentation total variation primal-dual algorithm

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Efficient Stereo Matching Scheme Based on Graph Cuts

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Journal of Beijing Institute of Technology 2010年第4期19卷 445-449页

作者：侯广琦魏平王淑华 Key Laboratory of Photo-Electronic Imaging Technology and System Beijing Institute of Technology School of Optoelectronics Beijing Institute of Technology Beijing Institute of Environmental Features

A new stereo matching scheme from image pairs based on graph cuts is given,which can solve the problem of large color differences as the result of fusing matching results of graph cuts from different color spaces.This scheme builds normalized histogram and reference histogram from matching results,and uses clustering algorithm to process the two histograms.Region histogram statistical method is adopted to retrieve depth data to achieve final matching results.Regular stereo matching library is used to verify this scheme,and experiments reported in this paper support availability of this method for automatic image processing.This scheme renounces the step of manual selection for adaptive color space and can obtain stable matching results.The whole procedure can be executed automatically and improve the integration level of image analysis process.

关键词： stereo matching graph cuts Markov random fields color space

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Image processing methods to evaluate tomato and zucchini damage in post-harvest stages

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International Journal of Agricultural and Biological Engineering 2017年第5期10卷 126-133页

作者： José Antonio Alvarez-Bermejo Cynthia Giagnocavo Li Ming Encarnación Castillo Morales Diego P.Morales Santos Yang Xinting Department of Informatics CeiA3University of AlmeriaAlmeriaCP 04120Spain Cátedra(Chair)Coexphal-UAL of Horticulture Cooperative Studies and Sustainable DevelopmentUniversity of AlmeríaSpain Beijing Research Center for Information Technology in Agriculture/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability/Key Laboratory for Information Technologies in Agriculture Ministry of AgricultureBeijing 100097China Department of Electronics and Computer Technology Facultad de CienciasGranadaCP 18071Spain

Through the supply chain,the quality or quality change of the products can generate important losses.The quality control in some steps is made manually that supposes a high level of subjectivity,controlling the quality and its evolution using automatic systems can suppose a reduction of the losses.Testing some automatic image analysis techniques in the case of tomatoes and zucchini is the main objective of this study.Two steps in the supply chain are considered,the feeding of the raw products into the handling chain(because low quality generates a reduction of the chain productivity)and the cool storage of the processed products(as the value at the market is reduced).It was proposed to analyze the incoming products at the head the processing line using CCD cameras to detect low quality and/or dirty products(corresponding to specific farmers/suppliers,it should be asked to improve to maintain the productivity of the line).The second stage is analyzing the evolution of the products along the cool chain(storage and transport),the use of an App developed to be use under Android was proposed to substitute the“visual”evaluation used in practice.The algorithms used,including stages of pre-treatment,segmentation,analysis and presentation of the results take account of the short time available and the limited capacity of the batteries.High performance techniques were applied to the homography stage to discard some of the images,resulting in better performance.Also threads and renderscript kernels were created to parallelize the methods used on the resulting images being able to inspect faster the products.The proposed method achieves success rates comparable to,and improving,the expert inspection.

关键词： image processing color space smartphone efficient stitching homography controlled supervision artificial vision embedded parallel processing injury assessment traceability post-harvest control feature detection

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