Adaptive and Generic Corner Detection Based on the Accelerated Segment Test

By Elmar Mair, Gregory Hager, Darius Burschka, Michael Suppa, G. Hirzinger. European Conference on Computer Vision, 2010

565 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8cda08ead0ae18b552f69469fcbf68099e498a9c

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Image Matching from Handcrafted to Deep Features: A Survey (2020, methodology): “FAST (Trajković and Hedley 1998) detector was the first attempt to use traditional learning for reliable andmatchable point identification, and similar strategies have been applied in many subsequent improvements (Mair et al. 2010; Rublee et al. 2011).”
Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images (2018, methodology): “We also investigated Adaptive and Generic Accelerated Segment Test (AGAST) [22] as a dedicated keypoint detector without descriptor.”
Pose Estimation for Augmented Reality: A Hands-On Survey (2016, unknown): “AGAST [77] further improved computational cost against FAST by dynamically changing the optimal configuration of pixels for similarity measurement.”
A review of multimodal image matching: Methods and applications (2021, methodology): “The same concept, with a comparison of the center against the pixels along a circular pattern, was used in the famous FAST detector [144], which was further improved to enhance the repeatability [136] and robustness [145].”
A Survey of Augmented Reality (2015, methodology): “, 2011], which used a feature detector inspired by AGAST [Mair et al., 2010] and a feature descriptor inspired by DAISY [Tola et al.”
Vision-based control of a quadrotor for perching on lines (2014, methodology): “The optical flow calculation is done using a set of AGAST [21] features which are tracked using the pyramidal Lukas-Kanade tracker present in OpenCV.”
FREAK: Fast Retina Keypoint (2012, background): “We briefly present state-of-the-art detectors and mainly focus on descriptors.”
BRISK: Binary Robust invariant scalable keypoints (2011, methodology): “The more recent FAST criterion [14] for keypoint detection has become increasingly popular in state-of-the-art methods with hard real-time constraints, with AGAST [10] extending this work for improved performance.”
Comparative Evaluation of Binary Features (2012, methodology): “In order to compute the feature locations, it uses the AGAST corner detector [31], which improves FAST by increasing speed while maintaining the same detection performance.”
Advanced closed loop visual navigation for micro aerial vehicles (2014, methodology): “This situation could b slightly improved by using th AGAST feature detector, that was developed by Mair et al. (2010) as an improvement of FAST.”