Using Stochastic optimization to improve the detection of small checkerboards

The popularity of mobile devices has fostered the emergence of plenty of new services, most of which rely on the use of their cameras. Among these, diet monitoring based on computer vision can be of particular interest. However, estimation of the amount of food portrayed in an image requires a size reference. A small checkerboard is a simple pattern which can be effectively used to that end. Unfortunately, most existing off-the-shelf checkerboard detection algorithms have problems detecting small patterns since they are used in tasks such as camera calibration, which require that the pattern cover most of the image area. This work presents a stochastic model-based approach, which relies on Differential Evolution (DE), to detecting small checkerboards. In the method we propose the checkerboard pattern is first roughly located within the image using DE. Then, the region detected in the first step is cropped in order to meet the requirements of off-the-shelf algorithms for checkerboard detection and let them work at their best. Experimental results show that, doing so, it is possible to achieve not only a significant increase of detection accuracy but also a relevant reduction of processing time.