Adaptive Segmentation Approach to Extraction of Road and Sky Regions (도로와 하늘 영역 추출을 위한 적응적 분할 방법)

박경환; Nam, Kwang-Woo (남광우); Rhee Yang Weon (이양원); Chang Woo Lee (이창우)

Adaptive Segmentation Approach to Extraction of Road and Sky Regions

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2011, 16(7), pp.105-116
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

박경환 ¹, Nam, Kwang-Woo ¹, Rhee Yang Weon ¹, Chang Woo Lee ¹

¹군산대학교

Accredited

ABSTRACT

In Vision-based Intelligent Transportation System(ITS) the segmentation of road region is a very basic functionality. Accordingly, in this paper, we propose a region segmentation method using adaptive pattern extraction technique to segment road regions and sky regions from original images. The proposed method consists of three steps; firstly we perform the initial segmentation using Mean Shift algorithm, the second step is the candidate region selection based on a static-pattern matching technique and the third is the region growing step based on a dynamic-pattern matching technique. The proposed method is able to get more reliable results than the classic region segmentation methods which are based on existing split and merge strategy. The reason for the better results is because we use adaptive patterns extracted from neighboring regions of the current segmented regions to measure the region homogeneity. To evaluate advantages of the proposed method, we compared our method with the classical pattern matching method using static-patterns. In the experiments, the proposed method was proved that the better performance of 8.12% was achieved when we used adaptive patterns instead of static-patterns. We expect that the proposed method can segment road and sky areas in the various road condition in stable, and take an important role in the vision-based ITS applications.

KEYWORDS

ITS, Adaptive Pattern Extraction, Region Merge, Road Segmentation, Sky Segmentation

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65