Christopher Le Brese

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Image matching is a well researched topic of computer vision. Several new algorithms have been developed in recent times to deal with repetitive pattern matching and affine invariant matching. This paper presents two improvements over the state-of-the-art Affine-Scale Invariant Feature Transform (ASIFT) algorithm. The first improvement enables ASIFT to(More)
Reliably matching feature points is an important part of many computer vision applications. This task is made harder when matching scenes containing repetitive patterns. The description of many feature points may be identical causing ambiguity in the matching results. This paper presents a filtering algorithm to remove erroneous matches caused by repetitive(More)
In recent years several algorithms have been developed that allow feature matching methods to operate on images with large baseline variations such as Affine-Scale Invariant Feature Transform (ASIFT) and its variants. These algorithms solve the base line problem through simulating various potential transforms between image pairs. These simulated views may(More)
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