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A SIFT-Rank descriptor is generated from a standard SIFT descriptor, by setting each histogram bin to its rank in a sorted array of bins. The Euclidean distance between SIFT-Rank descriptors is invariant to arbitrary monotonic changes in histogram bin values, and is related to Spearman's rank correlation coefficient .
It is based on the FAST keypoint detector and a modified version of the visual descriptor BRIEF (Binary Robust Independent Elementary Features). Its aim is to provide a fast and efficient alternative to SIFT.
In computer vision, speeded up robust features (SURF) is a local feature detector and descriptor, with patented applications. It can be used for tasks such as object recognition, image registration, classification, or 3D reconstruction. It is partly inspired by the scale-invariant feature transform (SIFT) descriptor. The standard version of ...
Rank SIFT algorithm is the revised SIFT (Scale-invariant feature transform) algorithm which uses ranking techniques to improve the performance of the SIFT algorithm.In fact, ranking techniques can be used in key point localization or descriptor generation of the original SIFT algorithm.
These vectors are called feature descriptors. A good descriptor should have the ability to handle intensity, rotation, scale and affine variations to some extent. One of the most famous descriptors is the scale-invariant feature transform (SIFT). [6] SIFT converts each patch to 128-dimensional vector.
A woman has gone viral for her reaction to her surprise proposal. For American-Cambodian swimmer Apsara Sakbun, 2024 was full of rings. She spent her summer representing Cambodia at the 2024 Paris ...
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The blob descriptors obtained from these blob detectors with automatic scale selection are invariant to translations, rotations and uniform rescalings in the spatial domain. The images that constitute the input to a computer vision system are, however, also subject to perspective distortions.