cMinMax: A Fast Algorithm to Find the Corners of an N-dimensional Convex Polytope

TL;DR

The paper introduces cMinMax, a highly efficient and parallelizable algorithm for detecting corners on convex polygons, significantly faster than Harris, suitable for real-time AR applications and extendable to N-dimensional shapes.

Contribution

cMinMax provides a novel, faster, and parallelizable method for corner detection on convex polygons, improving real-time AR processing capabilities.

Findings

cMinMax is approximately 5 times faster than Harris Corner Detection.

The algorithm is highly parallelizable, enhancing real-time performance.

It can be extended to N-dimensional convex polyhedrons.

Abstract

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