Post-processing Algorithm Based on Edge Information to Improve the Accuracy of Semantic Image Segmentation

Abstract

Semantic image segmentation technology in the field of computer vision is a technology that classifies an image by dividing it into pixels. This technique is also rapidly improving performance using a machine learning method, and a high possibility of utilizing information in units of pixels is drawing attention. However, this technology has been raised from the early days until recently for ‘lack of detailed segmentation’ problem. Since this problem was caused by increasing the size of the label map, it was expected that the label map could be improved by using the edge map of the original image with detailed edge information. Therefore, in this paper, we propose a post-processing algorithm that maintains semantic image segmentation based on learning, but modifies the resulting label map based on the edge map of the original image. After applying the algorithm to the existing method, when comparing similar applications before and after, approximately 1.74% pixels and 1.35% IoU (Intersection of Union) were applied, and when analyzing the results, the precise targeting fine segmentation function was improved. ■ keyword :∣Computer Vision∣Machine Learning∣Deep Learning∣Image Processing∣Semantic Segmentation∣ 접수일자 : 2020년 11월 19일 수정일자 : 2021년 01월 20일 심사완료일 : 2021년 01월 20일 교신저자 : 최형일, e-mail : hic@ssu.ac.kr 한국콘텐츠학회논문지 '21 Vol. 21 No. 3 24