A multi-threshold approach and a realistic error measure for vanishing point detection in natural landscapes. (October 2019)

• Record Type: Journal Article
• Authors: García-Faura, Álvaro; Fernández-Martínez, Fernando; Kleinlein, Ricardo; San-Segundo, Rubén; Díaz-de-María, Fernando
• Is Part Of: Engineering applications of artificial intelligence. Volume 85(2019)
• Page Start: 713
• Page End: 726
• Publication Date: 2019-10
• Subjects: Vanishing point -- Multi-threshold -- Perspective -- Landscapes; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.08.001
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Vanishing Point (VP) detection is a computer vision task that can be useful in many different fields of application. In this work, we present a VP detection algorithm for natural landscape images based on an multi-threshold edge extraction process that combines several representations of an image, and on novel clustering and cluster refinement procedures. Our algorithm identifies a VP candidate in images with single-point perspective and improves detection results on two datasets that have already been tested for this task. Furthermore, we study how VP detection results have been reported in literature, pointing out the main drawbacks of previous approaches. To overcome these drawbacks, we present a novel error measure that is based on a probabilistic consistency measure between edges and VP hypothesis, and that can be tuned to vary the strictness on the results. Our reasoning on how our measure is more correct is supported by an intuitive analysis, simulations and an experimental validation. Highlights: Novel methodology for improved vanishing point detection in landscape images. Improved edge extraction process by combining different representations of an image. New cluster refinement method for discarding weak VP hypothesis. New probabilistic error measure for more robust vanishing point detection. Qualitative and quantitative analysis validating the reliability of the new measure.