Individual fish segmentation is a prerequisite for feature extraction and object identification in any machine vision system.In this paper,a method for segmentation of overlapping fish images in aquaculture was propos...Individual fish segmentation is a prerequisite for feature extraction and object identification in any machine vision system.In this paper,a method for segmentation of overlapping fish images in aquaculture was proposed.First,the shape factor was used to determine whether an overlap exists in the picture.Then,the corner points were extracted using the curvature scale space algorithm,and the skeleton obtained by the improved Zhang-Suen thinning algorithm.Finally,intersecting points were obtained,and the overlapped region was segmented.The results show that the average error rate and average segmentation efficiency of this method was 10%and 90%,respectively.Compared with the traditional watershed method,the separation point is accurate,and the segmentation accuracy is high.Thus,the proposed method achieves better performance in segmentation accuracy and effectiveness.This method can be applied to multi-target segmentation and fish behavior analysis systems,and it can effectively improve recognition precision.展开更多
Image segmentation directly determines the performance of automatic screening technique. However,there are overlapping nuclei in nuclei images. It raises a challenge to nuclei segmentation. To solve the problem,a segm...Image segmentation directly determines the performance of automatic screening technique. However,there are overlapping nuclei in nuclei images. It raises a challenge to nuclei segmentation. To solve the problem,a segmentation method of overlapping cervical nuclei based on the identification is proposed. This method consists of three stages： classifier training,recognition and fine segmentation. In the classifier training,feature selection and classifier selection are used to obtain a classifier with high recognition rate. In the recognition,the outputs of the rough segmentation are classified and processed according to their labels. In the fine segmentation,the severely overlapping nuclei are further segmented based on the prior knowledge provided by the recognition. Experiments show that this method can accurately segment overlapping nuclei.展开更多
基金The research was supported by the National Key Technology R&D Program of China(2019YFD090086)the Beijing Excellent Talents Development Project(2017000057592G125)the Beijing Natural Science Foundation(4184089).
文摘Individual fish segmentation is a prerequisite for feature extraction and object identification in any machine vision system.In this paper,a method for segmentation of overlapping fish images in aquaculture was proposed.First,the shape factor was used to determine whether an overlap exists in the picture.Then,the corner points were extracted using the curvature scale space algorithm,and the skeleton obtained by the improved Zhang-Suen thinning algorithm.Finally,intersecting points were obtained,and the overlapped region was segmented.The results show that the average error rate and average segmentation efficiency of this method was 10%and 90%,respectively.Compared with the traditional watershed method,the separation point is accurate,and the segmentation accuracy is high.Thus,the proposed method achieves better performance in segmentation accuracy and effectiveness.This method can be applied to multi-target segmentation and fish behavior analysis systems,and it can effectively improve recognition precision.
基金supported by the National Natural Science Foundation of China(61673142,61471145,61305001)the Foundation of Education Department of Heilongjiang Province(12511096)+1 种基金the Research Fund for the Doctoral Program of Higher Education of China(20132303120003)the Science Funds for the Young Innovative Talents of HUST(20152)
文摘Image segmentation directly determines the performance of automatic screening technique. However,there are overlapping nuclei in nuclei images. It raises a challenge to nuclei segmentation. To solve the problem,a segmentation method of overlapping cervical nuclei based on the identification is proposed. This method consists of three stages： classifier training,recognition and fine segmentation. In the classifier training,feature selection and classifier selection are used to obtain a classifier with high recognition rate. In the recognition,the outputs of the rough segmentation are classified and processed according to their labels. In the fine segmentation,the severely overlapping nuclei are further segmented based on the prior knowledge provided by the recognition. Experiments show that this method can accurately segment overlapping nuclei.
