Berend van Starkenburg - Academia.edu (original) (raw)
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Papers by Berend van Starkenburg
Applications for interest point detectors and descriptors are just as broad as the field of comput... more Applications for interest point detectors and descriptors are just as broad as the field of computer vision as a whole. Each technique has a slightly different approach to cover its niche. With the growing number of new detectors and descriptors, it is crucial to evaluate in which context the detector is most potent and where its struggles lie. In this paper, we will evaluate a collection of novel and established local detectors and descriptors. First, we design a dataset so that the methods are exposed to a diverse set of conditions. With this dataset, the stability of interest point detectors is tested, after which the matching capabilities of the descriptors are put to the test. The methods will also be evaluated on their capability to detect copies from a large set of images. Finally, an existing method will be improved to make it more robust to transformations. This study aims to help researchers identify which method is best appropriate for their purposes.
Applications for interest point detectors and descriptors are just as broad as the field of comput... more Applications for interest point detectors and descriptors are just as broad as the field of computer vision as a whole. Each technique has a slightly different approach to cover its niche. With the growing number of new detectors and descriptors, it is crucial to evaluate in which context the detector is most potent and where its struggles lie. In this paper, we will evaluate a collection of novel and established local detectors and descriptors. First, we design a dataset so that the methods are exposed to a diverse set of conditions. With this dataset, the stability of interest point detectors is tested, after which the matching capabilities of the descriptors are put to the test. The methods will also be evaluated on their capability to detect copies from a large set of images. Finally, an existing method will be improved to make it more robust to transformations. This study aims to help researchers identify which method is best appropriate for their purposes.