A new separation algorithm based on contour segments and ellipse fitting is proposed to separate the ellipse-like touching grain kernels in digital images.The image is filtered and converted into a binary image first....A new separation algorithm based on contour segments and ellipse fitting is proposed to separate the ellipse-like touching grain kernels in digital images.The image is filtered and converted into a binary image first.Then the contour of touching grain kernels is extracted and divided into contour segments (CS) with the concave points on it.The next step is to merge the contour segments,which is the main contribution of this work.The distance measurement (DM) and deviation error measurement (DEM) are proposed to test whether the contour segments pertain to the same kernel or not.If they pass the measurement and judgment,they are merged as a new segment.Finally with these newly merged contour segments,the ellipses are fitted as the representative ellipses for touching kernels.To verify the proposed algorithm,six different kinds of Korean grains were tested.Experimental results showed that the proposed method is efficient and accurate for the separation of the touching grain kernels.展开更多
Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood v...Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood vessels and the herculean task involved in glaucoma detection,the exactly affected site of the optic disc of whether small or big size cup,is deemed challenging.Spatially Based Ellipse Fitting Curve Model(SBEFCM)classification is suggested based on the Ensemble for a reliable diagnosis of Glaucomain theOptic Cup(OC)and Optic Disc(OD)boundary correspondingly.This research deploys the Ensemble Convolutional Neural Network(CNN)classification for classifying Glaucoma or Diabetes Retinopathy(DR).The detection of the boundary between the OC and the OD is performed by the SBEFCM,which is the latest weighted ellipse fitting model.The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here.There is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels.The ascertaining of OCandODboundary,which characterizedmany output factors for glaucoma detection,has been developed by EnsembleCNNclassification,which includes detecting sensitivity,specificity,precision,andArea Under the receiver operating characteristic Curve(AUC)values accurately by an innovative SBEFCM.In terms of contrast,the proposed Ensemble CNNsignificantly outperformed the current methods.展开更多
Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction,disease diagnosis,and psychological and physiological studies.Ga...Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction,disease diagnosis,and psychological and physiological studies.Gaze-tracking systems are an important research topic in the human-computer interaction field.As one of the core modules of the head-mounted gaze-tracking system,pupil positioning affects the accuracy and stability of the system.By tracking eye movements to better locate the center of the pupil,this paper proposes a method for pupil positioning based on the starburst model.The method uses vertical and horizontal coordinate integral projections in the rectangular region of the human eye for accurate positioning and applies a linear interpolation method that is based on a circular model to the reflections in the human eye.In this paper,we propose a method for detecting the feature points of the pupil edge based on the starburst model,which clusters feature points and uses the RANdom SAmple Consensus(RANSAC)algorithm to perform ellipse fitting of the pupil edge to accurately locate the pupil center.Our experimental results show that the algorithm has higher precision,higher efficiency and more robustness than other algorithms and excellent accuracy even when the image of the pupil is incomplete.展开更多
This research investigated the size detecting and online grading of Red Globe grapes using images of entire cases,rather than individual grapes.Method of ellipse fitting based on iterative least median squares was pro...This research investigated the size detecting and online grading of Red Globe grapes using images of entire cases,rather than individual grapes.Method of ellipse fitting based on iterative least median squares was proposed and the process of grape grading includes the following four steps:stem removal from the RGB and NIR images collected by the 2-CCD camera;edge extraction by multiple methods of edge detection,image binarization,morphological processing,et al.;size determination of individual grapes by using image segmentation and ellipse fitting to calculate short axis length;Finally,grading based on the 15%downgrade principle,this means that if the case contains more than 15%of multiple grades,then the case is re-evaluated.Thirty-eight cases of Red Globe grapes were graded using these methods and 35 cases were correctly graded with an accuracy rate reaching 92.1%.The results showed that the accuracy and speed meet the requirements of grape automatic online detection.展开更多
In the process of tracking the target of the pig,with the change of the size of the tracking target in the video image,the estimated tracking target scale cannot be adaptively updated in real-time,resulting in the low...In the process of tracking the target of the pig,with the change of the size of the tracking target in the video image,the estimated tracking target scale cannot be adaptively updated in real-time,resulting in the low accuracy of the tracking target.In this study,a multi-channel color feature adaptive fusion algorithm was proposed,and the target scale of the pig was updated in real-time by utilizing the contour information of the target pig.Experiments show that the proposed algorithm had a distance precision of 89.7%and an overlap precision of 87.5%,and the average running speed of this algorithm was 50.1 fps.The robustness of the proposed algorithm in tracking target deformation and scale variation were significantly improved,which satisfies the accuracy and real-time requirements of pig target tracking.展开更多
Continuous live weight and carcass traits estimation are important for the pig production and breeding industry.It is widely known that top-view images of a pig’s body(excluding its head and neck)reveal surface dimen...Continuous live weight and carcass traits estimation are important for the pig production and breeding industry.It is widely known that top-view images of a pig’s body(excluding its head and neck)reveal surface dimension parameters,which are correlated with live weight and carcass traits.However,because a pig is not constrained when an image is captured,the body does not always have a straight posture.This creates a big challenge when extracting the body surface dimension parameters,and consequently the live weight and carcass traits estimation has a high level of uncertainty.The primary goal of this study is to propose an algorithm to automatically extract pig body surface dimension parameters,with a better accuracy,from top-view pig images.Firstly,the backbone line of a pig was extracted.Secondly,lengths of line segments perpendicular to the backbone line were calculated,and then feature points on the pig’s contour line were extracted based on the lengths variation of the perpendicular line segments.Thirdly,the head and neck of the pig were removed from the pig’s contour by an ellipse.Finally,four length and one area parameters were calculated.The proposed algorithm was implemented in Matlab®(R2012b)and applied to 126 depth images of pigs.Taking the results of the manual labeling tool as the gold standard,the length and area parameters could be obtained by the proposed algorithm with an accuracy of 97.71%(SE=1.64%)and 97.06%(SE=1.82%),respectively.These parameters can be used to improve pig live weight and carcass traits estimation accuracy in the future work.展开更多
基金Project supported by the Grant of the Korean Ministry of Education,Science and Technology under the Regional Core Research Program
文摘A new separation algorithm based on contour segments and ellipse fitting is proposed to separate the ellipse-like touching grain kernels in digital images.The image is filtered and converted into a binary image first.Then the contour of touching grain kernels is extracted and divided into contour segments (CS) with the concave points on it.The next step is to merge the contour segments,which is the main contribution of this work.The distance measurement (DM) and deviation error measurement (DEM) are proposed to test whether the contour segments pertain to the same kernel or not.If they pass the measurement and judgment,they are merged as a new segment.Finally with these newly merged contour segments,the ellipses are fitted as the representative ellipses for touching kernels.To verify the proposed algorithm,six different kinds of Korean grains were tested.Experimental results showed that the proposed method is efficient and accurate for the separation of the touching grain kernels.
文摘Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood vessels and the herculean task involved in glaucoma detection,the exactly affected site of the optic disc of whether small or big size cup,is deemed challenging.Spatially Based Ellipse Fitting Curve Model(SBEFCM)classification is suggested based on the Ensemble for a reliable diagnosis of Glaucomain theOptic Cup(OC)and Optic Disc(OD)boundary correspondingly.This research deploys the Ensemble Convolutional Neural Network(CNN)classification for classifying Glaucoma or Diabetes Retinopathy(DR).The detection of the boundary between the OC and the OD is performed by the SBEFCM,which is the latest weighted ellipse fitting model.The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here.There is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels.The ascertaining of OCandODboundary,which characterizedmany output factors for glaucoma detection,has been developed by EnsembleCNNclassification,which includes detecting sensitivity,specificity,precision,andArea Under the receiver operating characteristic Curve(AUC)values accurately by an innovative SBEFCM.In terms of contrast,the proposed Ensemble CNNsignificantly outperformed the current methods.
基金This research was funded by the Science and Technology Support Plan Project of Hebei Province(grant numbers 17210803D and 19273703D)the Science and Technology Spark Project of the Hebei Seismological Bureau(grant number DZ20180402056)+1 种基金the Education Department of Hebei Province(grant number QN2018095)the Polytechnic College of Hebei University of Science and Technology.
文摘Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction,disease diagnosis,and psychological and physiological studies.Gaze-tracking systems are an important research topic in the human-computer interaction field.As one of the core modules of the head-mounted gaze-tracking system,pupil positioning affects the accuracy and stability of the system.By tracking eye movements to better locate the center of the pupil,this paper proposes a method for pupil positioning based on the starburst model.The method uses vertical and horizontal coordinate integral projections in the rectangular region of the human eye for accurate positioning and applies a linear interpolation method that is based on a circular model to the reflections in the human eye.In this paper,we propose a method for detecting the feature points of the pupil edge based on the starburst model,which clusters feature points and uses the RANdom SAmple Consensus(RANSAC)algorithm to perform ellipse fitting of the pupil edge to accurately locate the pupil center.Our experimental results show that the algorithm has higher precision,higher efficiency and more robustness than other algorithms and excellent accuracy even when the image of the pupil is incomplete.
基金Natural Science Fund of Hubei Province(2012FKB02910)Research and Development Projects in Hubei Province(2011BHB01).
文摘This research investigated the size detecting and online grading of Red Globe grapes using images of entire cases,rather than individual grapes.Method of ellipse fitting based on iterative least median squares was proposed and the process of grape grading includes the following four steps:stem removal from the RGB and NIR images collected by the 2-CCD camera;edge extraction by multiple methods of edge detection,image binarization,morphological processing,et al.;size determination of individual grapes by using image segmentation and ellipse fitting to calculate short axis length;Finally,grading based on the 15%downgrade principle,this means that if the case contains more than 15%of multiple grades,then the case is re-evaluated.Thirty-eight cases of Red Globe grapes were graded using these methods and 35 cases were correctly graded with an accuracy rate reaching 92.1%.The results showed that the accuracy and speed meet the requirements of grape automatic online detection.
基金This work was supported in part by the National Key Research and Development Plan for the 13th Five-Year Plan under Grant 2016YFD0700200This work was supported in part by the National High Technology Research and Development Program of China(2013AA102306).
文摘In the process of tracking the target of the pig,with the change of the size of the tracking target in the video image,the estimated tracking target scale cannot be adaptively updated in real-time,resulting in the low accuracy of the tracking target.In this study,a multi-channel color feature adaptive fusion algorithm was proposed,and the target scale of the pig was updated in real-time by utilizing the contour information of the target pig.Experiments show that the proposed algorithm had a distance precision of 89.7%and an overlap precision of 87.5%,and the average running speed of this algorithm was 50.1 fps.The robustness of the proposed algorithm in tracking target deformation and scale variation were significantly improved,which satisfies the accuracy and real-time requirements of pig target tracking.
基金This work was enclosed in the Flemish IWT funded project“Sustainable precision feeding”(Grant No.AIC-221.42.D.02),in collaboration with Agrifirm Innovation Center and Fancom.This work was also supported by the Fundamental Research Funds for the Central Universities of China(Grant No.KYZ201561)the Joint Innovation Fund of Production,Learning,and Research-Prospective Joint Research Project,Jiangsu,China(Grant No.BY2015071-06)the fund of China Scholarship Council(Grant No.201506855017).
文摘Continuous live weight and carcass traits estimation are important for the pig production and breeding industry.It is widely known that top-view images of a pig’s body(excluding its head and neck)reveal surface dimension parameters,which are correlated with live weight and carcass traits.However,because a pig is not constrained when an image is captured,the body does not always have a straight posture.This creates a big challenge when extracting the body surface dimension parameters,and consequently the live weight and carcass traits estimation has a high level of uncertainty.The primary goal of this study is to propose an algorithm to automatically extract pig body surface dimension parameters,with a better accuracy,from top-view pig images.Firstly,the backbone line of a pig was extracted.Secondly,lengths of line segments perpendicular to the backbone line were calculated,and then feature points on the pig’s contour line were extracted based on the lengths variation of the perpendicular line segments.Thirdly,the head and neck of the pig were removed from the pig’s contour by an ellipse.Finally,four length and one area parameters were calculated.The proposed algorithm was implemented in Matlab®(R2012b)and applied to 126 depth images of pigs.Taking the results of the manual labeling tool as the gold standard,the length and area parameters could be obtained by the proposed algorithm with an accuracy of 97.71%(SE=1.64%)and 97.06%(SE=1.82%),respectively.These parameters can be used to improve pig live weight and carcass traits estimation accuracy in the future work.
