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This paper addresses a new hybrid feature extractor algorithm, which in essence integrates a Fast-Hessian detector into the SIFT (Scale Invariant Feature Transform) algorithm. Feature extractors mainly consist of two essential parts: feature detector and descriptor extractor. This study proposes to integrate (Speeded-Up Robust Features) SURF’s hessian detector into the SIFT algorithm so as to boost the total number of true matched pairs. This is a critical requirement in image processing and widely used in various corresponding fields from image stitching to object recognition. The proposed hybrid algorithm has been tested under different experimental conditions and results are quite encouraging in terms of obtaining higher matched pairs and precision score

A Hybrid Feature Extractor using Fast Hessian Detector and SIFT