苏格兰高地岩石:AGAST Corner Detector: faster than FAST and even FAST-ER
AGAST Corner Detector: faster than FAST and even FAST-ER
Motivation
Corner response of the OAST9 mask.Corners are preferred cues in Computer Vision due to their two dimensional constraint and fast algorithms to detect them. Recently, Edward Rosten et al. presented the corner detection approach FAST, which outperforms conventional algorithms not only regarding their performance but also their repeatability. This detector is based on the accelerated segment test (AST), which is a modification of the SUSAN corner detector, as described in the FAST-paper.
The accelerated segment test (AST) is a so called twenty question problem. Rosten computes the decision tree by learning the distribution of the corner configuration from a training set of a specific environment. This approach has some drawbacks:
- If the camera is rotated, the pixel configuration of a corner may change significantly. Thus, the adaption to a scene results in a speed up of the corner detector only as long as the camera or the environment does not move (especially rotate). Any motion can lead to corner configurations which are rather slow to evaluate.
- Some corner configurations may be missing in the training set which leads to false positive and false negative responses of the corner detector.
- The decision tree has to be learned for every new environment from scratch.
- ID3 is used to build the decision tree, which is a greedy algorithm and, therefore, the result can be quite suboptimal.
- FAST builds a ternary decision tree for a binary target machine - a binary tree would be more efficient.
AGAST: Adaptive and Generic Accelerated Segment Test
In our approach, we propose a technique to compute a binary decision tree (corner detector) which is generic and does not have to be adapted to new environments. It is complete by definition (no false positive or false negative responses) and the only parameters are the memory access times to weight the various pixel comparisons. The tree is optimal for a certain probability of similar pixels in the AST mask.