Corner Detection Are Useful for Computer Vision Applications Essay

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Corner detection and its parameters: position, model and orientation are useful for many computer vision applications, such as object recognition, matching, segmentation, 3D reconstruction, motion estimation [2, 3, 4, 34.] indexing, retrieval, robot navigation and in our case edge tracking from geometry design. This need has driven the development of a large number of corner detectors [1, 5, 6, 7, 8, 9, 10, 11, 12, 13.]. Other methods for corner detection are described in [14, 15]. These detectors compete with each other in terms of precision localization, accuracy, speed, and information they provide. Model classification and orientation are the most interest information needed in process of edge tracking.
For some of these approaches,
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Corner detection and its parameters: position, model and orientation are useful for many computer vision applications, such as object recognition, matching, segmentation, 3D reconstruction, motion estimation [2, 3, 4, 34.] indexing, retrieval, robot navigation and in our case edge tracking from geometry design. This need has driven the development of a large number of corner detectors [1, 5, 6, 7, 8, 9, 10, 11, 12, 13.]. Other methods for corner detection are described in [14, 15]. These detectors compete with each other in terms of precision localization, accuracy, speed, and information they provide. Model classification and orientation are the most interest information needed in process of edge tracking.
For some of these approaches, the CRF (Corner-Response-Function) can be shown to be invariant in scale, rotation or even affine transformations.
Here we review the literature to place our contribution in context. The attempt to simultaneous realization of corner detection and description of its properties is proved to be a complex work. By contrast, the decoupling of these two operations in two distinct stages simplifies and creates efficient problem solving.
A. Corner detection:
A broad variety of corner detectors are presented in literature. One of the first interest operators was developed by Moravec (Moravec, 1977). The methods of detection can be grouped broadly into three categories: grey-level based methods [1, 5, 11, 12, 13, 18, 36], contour based methods [24, 25,

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