Vanishing point detection based on an artificial bee colony algorithm

Abstract

Vanishing points (VPs) are crucial for inferring the three-dimensional structure of a scene and can be exploited in various computer vision applications. Previous VP detection algorithms have been proven effective but generally cannot guarantee a strong performance in both accuracy and computational time. We propose an artificial bee colony algorithm called dynamic clustering artificial bee colony (DCABC) that accurately and efficiently detects VPs in the image plane. The task is regarded as a dynamic line-clustering problem, and the line clusters are initialized by their orientation information. Inspired by the foraging behavior of bees, DCABC selects the clustering center and reclassifies the line segments based on a distance criterion until the terminating condition is met. The optimal line clusters determine the estimated VP. The dissimilarity among solutions is measured by the Hamming distance between two binary vectors, which simplifies the new solution construction. The performances of the proposed and existing algorithms are evaluated on the York Urban database. The results verify the efficiency and accuracy of our proposed algorithm.

Vanishing point detection using the teaching learning‐based optimisation algorithm

“…The deviation function aims to quantify the deviation that exists between a VP and a line segment . To this end, as propose in [25], our algorithm considers the orientation error, this is also consistently with the measuring method proposed in [27, 35]. Thereby, the deviation is computed with the absolute value of the sine of the angle formed by the line that passes through the segment , and the line that connects with the centre of .…”

“…Metaheuristics have delivered better solutions in comparison with traditional methods. Regarding vanishing point estimation, VP detectors have been proposed using an artificial bee colony (ABC) [25], the particle swarm optimisation (PSO) [26], the binary PSO (BPSO) [27], and the harmony search (HS) algorithm [28]. However, when using these metaheuristics, the need of many parameters tuning remains.…”

Vanishing point detection in corridor for autonomous mobile robots using monocular low-resolution fisheye vision

“…In addition to the above two categories, there is another type of VP detection methods, which are based on optimization algorithms. Typical examples include Dynamic Clustering Artificial Bee Colony (DCABC)based algorithm 10 and Multiple Population Genetic Algorithm (MPGA)-based algorithm. 11 They first consider VP detection as a dynamic line clustering problem and then search for VP using some optimization algorithms such as Artificial Bee Colony algorithm and Genetic algorithm.…”