Neural network classifier for detecting corners in 2-D images
P.G.T. Dias, Ashraf Ali Kassim, V. Srinivasan · 2002
Existing corner detection methods either extract boundaries and search for points having maximum curvature or apply a local operator in parallel to neighborhoods of a gray level picture. The key problem in these methods is the conversion of a pixel into a value reflecting a property of cornerness at that point. A neural network's ability to learn and to adapt together with its inherent parallelism and robustness has made it a natural choice for machine vision applications. This paper presents the application of neural networks to the problem of detecting corners in 2-D images. The performance of the system suggests its robustness and great potential.