Speeded-Up Robust Features (SURF)

Abstract

This article presents a novel scale-and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (specifically, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps.The paper encompasses a detailed description of the detector and descriptor and then explores the effect of the most important parameters. We conclude the article with SURF's application to two challenging, yet converse goals: camera calibration as a special case of image registration, and object recognition. Our experiments underline SURF's usefulness in a broad range of topics in computer vision.

Integration of Sensor Orientation Data Into an Augmented Reality Technology / Jutiklių Orientacijos Duomenų Integracija Papildytosios Realybės Technologijoje

“…Naudojant šią priemonę, priklausomai nuo žymeklio vietos vaizde, realiuoju laiku galima apskaičiuoti kameros poziciją ir orientaciją erdvėje bei atvaizduoti virtualų turinį (1 pav.). Naujesniems papildytosios realybės technologijos sprendimams dažniausiai taikomi FAST (Rosten et al 2010) ir SURF (Bay et al 2008) vaizdų apdorojimo metodai ir įvairios šių metodų modifikacijos. Tokiu atveju identifikuojamas sekamas objektas, kai yra kliūčių.…”

An analysis of Feature extraction and Classification Algorithms for Dangerous Object Detection

“…SURF follows three main steps, first ‗interest points' need to be identified from different distinctive locations of the image [13]. This detection is based on Hessian matrix and blob-like structures are identified from different parts of the image.…”

Side-Scan Sonar Image Mosaic Using Couple Feature Points with Constraint of Track Line Positions

“…By matching the descriptors of the features in different SSS images, similar features can be obtained. The matching is based on a distance between the feature vectors, e.g., the Euclidean distance [26]. To eliminate the incorrect matched FPs, RANSAC algorithm is then adopted.…”

“…The SURF algorithm, which has invariant ability in rotation, scale, brightness and contrast [26][27][28], can locate key points of high variation and focus on the spatial distribution of gradient information. Thus, the SURF algorithm can be used to find the candidate interest points or these typical features in the SSS images.…”