The algorithm cMinMax was published as a paper at conference GRAPP 2021.

Fig. 1 Corner Detection

Abstract

During the last years, the emerging field of Augmented & Virtual Reality (AR-VR) has seen tremendous growth. At the same time there is a trend to develop low cost high-quality AR systems where computing power is in demand. Feature points are extensively used in these real-time frame-rate and 3D applications, therefore efficient high-speed feature detectors are necessary. Corners are such special features and often are used as the first step in the marker alignment in Augmented Reality (AR). Corners are also used in image registration and recognition, tracking, SLAM, robot path finding and 2D or 3D object detection and retrieval. Therefore there is a large number of corner detection algorithms but most of them are too computationally intensive for use in real-time applications of any complexity. Many times the border of the image is a convex polygon. For this special, but quite common case, we have developed a specific algorithm, cMinMax. The proposed algorithm is faster, approximately by a factor of 5 compared to the widely used Harris Corner Detection algorithm. In addition is highly parallelizable. The algorithm is suitable for the fast registration of markers in augmented reality systems and in applications where a computationally efficient real time feature detector is necessary. The algorithm can also be extended to N-dimensional polyhedrons.

Description

The algorith is able to detect all the corner from a convex polygon, it is simple and fast. The basic idea is that if we project our object on x and y axis and calculate the minimum and maximum values then those points represent corners. From this idea the name of the algorithm also derives : cMinMax, corners min max. The next step is to rotate the object by some degrees and recalculate min and max values. After a number of rotation steps we are sure that we have detected all the corners. You can see the paper by clicking the button below for more details.