Corners in images represent a lot of important information. Extracting corners accurately is significant to image processing, which can reduce much of the calculations. In this paper, three widely used corner detection algorithms, FAST (Features from Accelerated Segment Test), Eigen Value and Harris corner detection algorithms which are all based on intensity, were compared in stability, noise ...

A fast two-stage corner detector with noise tolerance is presented in this paper. At the first stage, candidate-corner pixels are selected by a proposed candidate pruning approach. At the second stage, real corners are recognized by the Harris detector among the candidate-corner pixels. In general, corners are considered as the junction of edges. Therefore, edge pixels with a high gradient in m...

Corner models in the literature have lagged behind edge models with respect to color and shading. We use both a region model, based on distributions of pixel colors, and an edge model, which removes false positives, to perform corner detection on color images whose regions contain texture. We show results on a variety of natural images at di erent scales that highlight the problems that occur w...

A novel feature based corner detection method is proposed to extract accurate corner positions than unstable corner points in existing algorithms such as Intensity-based Harris corner method and Edge-based corner method. The former algorithm cannot detect accurate corner positions in noisy environments while the latter cannot identify stable corners due to incomplete edge detection. The propose...