Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Statistical object recognition including color modeling
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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In this article we introduce and compare two approaches towards automatic classification of 3D objects in 2D images. The first one is based on statistical modeling of wavelet features. It estimates probability density functions for all possible object classes considered in a particular recognition task. The second one uses sparse local features. For training, SURF features are extracted from the training images. During the recognition phase, features from the image are matched geometrically, providing the best fitting object for the query image. Experiments were performed for different training sets using more than 40 000 images with different backgrounds. Results show very good classification rates for both systems and point out special characteristics for each approach, which make them more suitable for different applications.