Vanishing Point Based Image Segmentation and Clustering for Omnidirectional Image

Abstract

Regarding the autonomous of robot navigation, vanishing point (VP) plays an important role in visual robot applications such as iterative estimation of rotation angle for automatic control as well as scene understanding. Autonomous navigation systems must be able to recognize feature descriptors. Consequently, this navigating ability can help the system to identify roads, corridors, and stairs; ensuring autonomous navigation along the environments mentioned before the vanishing point detection is proposed. In this paper, the authors propose solutions for finding the vanishing point in based density-based spatial clustering of applications with noise (DBSCAN).First, the unlabeled data set is extracted from the training images by combining the red channel and the edge information. Then, the similarity metric of the specified number of clusters is analyzed via k-means algorithm. After this stage, the candidate area is extracted by using the hypothetical cluster set of targets. Second, we proposed to extract the longest segments of lines from the edge frame. Third, the set of intersection points for each pair of line segments are extracted by computing Lagrange coefficients. Finally, by using DBSCAN the VP is estimated. Preliminary results are performed and tested on a group of consecutive frames undertaken at Nam-gu, Ulsan, South Korea to prove its effectiveness.