Principal curvature-based region detector
The principal curvature-based region detector, also called PCBR is a feature detector used in the fields of computer vision and image analysis. Specifically the PCBR detector is designed for object recognition applications.
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| Feature detection |
|---|
| Edge detection |
| Corner detection |
| Blob detection |
| Ridge detection |
| Hough transform |
| Structure tensor |
| Affine invariant feature detection |
| Feature description |
| Scale space |
Local region detectors can typically be classified into two categories: intensity-based detectors and structure-based detectors.
- Intensity-based detectors depend on analyzing local differential geometry or intensity patterns to find points or regions that satisfy some uniqueness and stability criteria. These detectors include SIFT, Hessian-affine, Harris-Affine and MSER etc.
- Structure-based detectors depend on structural image features such as lines, edges, curves, etc. to define interest points or regions. These detectors include edge-based region (EBR) and scale-invariant shape features (SISF)
From the detection invariance point of view, feature detectors can be divided into fixed scale detectors such as normal Harris corner detector, scale invariant detectors such as SIFT and affine invariant detectors such as Hessian-affine.
The PCBR detector is a structure-based affine-invariant detector.
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