An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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This article proposes an approach to detection and description of interest points based C-HOG. The study of two interest point local descriptor methods, the SIFT and the SURF, allows us to understand their construction and extracts the various advantages (invariances, speeds, repeatability). Our goal is to couple these advantages to create a new system (detector and descriptor). The latter must be as invariant as possible for the image transformation (rotations, scales, viewpoints). We will have to find a compromise between a good matching rate and the number of points matched. All the detector and descriptor parameters (orientations, thresholds, analysis pattern, parameters) will be also detailed in this article.