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Principal curvature-based region detector
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Principal curvature-based region detector : ウィキペディア英語版
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.
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.
==Why a new detector?==
In many object recognition tasks, within-class changes in pose, lighting, color, and texture can cause considerable variation in local intensities. Consequently, local intensity no longer provides a stable detection cue. As such, intensity-based interest operators (e.g., SIFT, Harris-Affine)–and the object recognition systems based on them–often fail to identify discriminative features. An alternative to local intensity cues is to capture semi-local structural cues such as edges and curvilinear shapes. These structural cues tend to be more robust to intensity, color, and pose variations. As such, they provide the basis for a more stable interest operator, which in turn improves object recognition accuracy. ''PCBR'' detector was developed to exploit these more reliable image structural cues.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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