SFAST: A NEW ROBUST REGRESSION BASED COMBINED EDGES AND CORNER DETECTION

Muthukrishnan R, R1, Ravi. J

Abstract


Edge and Corner detection is a fundamental task in image processing and computer vision. Many procedures have been established during the past few decades. Yet there is a need of procedures which are less time consumingin nature and more accurate when considering blurred images. Recently, Feature Acclerated Segment Test (FAST), a corner detection model, had been presented which outperforms other procedures in both computational performance and repeatability. The FAST mainly uses simple regression and is used by machine learning approaches. This paper proposes a robust regression scale of residual (SSAC)-estimator based FAST algorithm namely, SFAST, can significantly improve its performance. SFAST is combined edge based corner detection method. The main feature of this proposed method is to use the edge points and their accumulated information for corner detection, for fast and more accurate results. The experimental results show that the proposed SFAST algorithm is fast, reliable and can be used in environments with noise.

Keywords


Robust statistics, Edge detection, Corner detection, Computer Vision.

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DOI: https://doi.org/10.26483/ijarcs.v8i7.4238

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