Enhanced Checkerboard Detection Using Gaussian Processes

Michaël Hillen (), Ivan De Boi, Thomas De Kerf, Seppe Sels, Edgar Cardenas De La Hoz, Jona Gladines, Gunther Steenackers, Rudi Penne and Steve Vanlanduit
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Michaël Hillen: InViLab, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium
Ivan De Boi: InViLab, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium
Thomas De Kerf: InViLab, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium
Seppe Sels: InViLab, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium
Edgar Cardenas De La Hoz: InViLab, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium
Jona Gladines: InViLab, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium
Gunther Steenackers: InViLab, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium
Rudi Penne: InViLab, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium
Steve Vanlanduit: InViLab, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium

Mathematics, 2023, vol. 11, issue 22, 1-13

Abstract: Accurate checkerboard detection is of vital importance for computer vision applications, and a variety of checkerboard detectors have been developed in the past decades. While some detectors are able to handle partially occluded checkerboards, they fail when a large occlusion completely divides the checkerboard. We propose a new checkerboard detection pipeline for occluded checkerboards that has a robust performance under varying levels of noise, blurring, and distortion, and for a variety of imaging modalities. This pipeline consists of a checkerboard detector and checkerboard enhancement with Gaussian processes (GP). By learning a mapping from local board coordinates to image pixel coordinates via a Gaussian process, we can fill in occluded corners, expand the board beyond the image borders, allocate detected corners that do not fit an initial grid, and remove noise on the detected corner locations. We show that our method can improve the performance of other publicly available state-of-the-art checkerboard detectors, both in terms of accuracy and the number of corners detected. Our code and datasets are made publicly available. The checkerboard detector pipeline is contained within our Python checkerboard detection library, called PyCBD. The pipeline itself is modular and easy to adapt to different use cases.

Keywords: checkerboard detection; Gaussian processes; occlusions (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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