Oh, Ju-Young Park, Jung-Min 2024-01-19T13:31:59Z 2024-01-19T13:31:59Z 2022-01-10 2021-11 2076-3417 https://pubs.kist.re.kr/handle/201004/116211 As more and more fields utilize deep learning, there is an increasing demand to make suitable training data for each field. The existing interactive object segmentation models can easily make the mask label data because these can accurately segment the area of the target object through user interaction. However, it is difficult to accurately segment the target part in the object using the existing models. We propose a method to increase the accuracy of part segmentation by using the proposed interactive object segmentation model trained only with edge images instead of color images. The results evaluated with the PASCAL VOC Part dataset show that the proposed method can accurately segment the target part compared to the existing interactive object segmentation model and the semantic part-segmentation model. English MDPI Interactive Part Segmentation Using Edge Images Article 10.3390/app112110106 1 APPLIED SCIENCES-BASEL, v.11, no.21 APPLIED SCIENCES-BASEL 11 21 scie scopus 000719526100001 2-s2.0-85118164092 Chemistry, Multidisciplinary Engineering, Multidisciplinary Materials Science, Multidisciplinary Physics, Applied Chemistry Engineering Materials Science Physics Article interactive segmentation part segmentation object segmentation edge image convolutional neural network