Abstract
An efficient and geometric-distortion-free approach, namely the fast binary robust local feature (FBRLF), is proposed. The FBRLF searches the stable features from an image with the proposed multiscale adaptive and generic corner detection based on the accelerated segment test (MAGAST) to yield an optimum threshold value based on adaptive and generic corner detection based on the accelerated segment test (AGAST). To overcome the problem of image noise, the Gaussian template is applied, which is efficiently boosted by the adoption of an integral image. The feature matching is conducted by incorporating the voting mechanism and lookup table method to achieve a high accuracy with low computational complexity. The experimental results clearly demonstrate the superiority of the proposed method compared with the former schemes regarding local stable feature performance and processing efficiency.
Highlights
Nowadays, the feature point descriptor is one of the important topics in computer vision and machine learning and is being widely applied to image retrieval [1,2], object recognition [3,4], and even multi-view 3D reconstruction [5,6] or image stitching [7,8]
In 2006, the study [10] revealed the speeded-up robust features (SURF) on the basis of the algorithm proposed by [9] to resolve the problem of how local stable features in the scale invariant feature transform (SIFT) cannot be applied to real-time systems
The fast binary robust local feature (FBRLF) first applies multiscale adaptive and generic corner detection based on the accelerated segment test (MAGAST)
Summary
The feature point descriptor is one of the important topics in computer vision and machine learning and is being widely applied to image retrieval [1,2], object recognition [3,4], and even multi-view 3D reconstruction [5,6] or image stitching [7,8]. Proposed the scale invariant feature transform (SIFT) algorithm in 2004, which integrates and optimizes steps to achieve a processing speed that is nearly real-time. In 2006, the study [10] revealed the speeded-up robust features (SURF) on the basis of the algorithm proposed by [9] to resolve the problem of how local stable features in the SIFT cannot be applied to real-time systems. Alcantarilla et al proposed KAZE features in 2012 [13] and
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