Skyline Detection Based on Region Segmentation and Boundary Feature Classification

• Main Authors: Yun-Wei Zhou, 周運緯
• Other Authors: Hsu-Yung Cheng
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/an7749

碩士 === 國立中央大學 === 資訊工程學系 === 105 === The skyline information in an outdoor image scene can provide a lot information such as the horizon and background information for automatic robots. Most existing works find skylines in suburban scenes because the urban scenes are more diverse than suburban scenes. In our paper, we try to find skylines in not only suburban scenes but also urban scenes. We propose a new method to find skylines using region segmentation and boundary feature classification. In the proposed method, we first apply region segmentation methods to obtain region boundaries. Then we use the segmentation result to split the region boundaries to edges. Afterwards, we perform sampling on the segmented region boundaries. Gray-Level Co-Occurrence Matrix (GLCM), color patch descriptors are extracted as the features of sampled pixels on the boundaries. We apply machine learning techniques and use both suburban and urban scenes to perform training. Support vector machine classifiers are used to classify the boundary. The sampled pixels on the boundaries are classified as sky pixel, ground pixel, or skyline pixel. At last, to use the region filling to get the skyline (or sky region). In the experiments, we have tested the performance of the framework using different combinations of region segmentation methods and features. The proposed method exhibits better detection results compared with existing skyline detection methods.