Image alignment and stitching: a tutorial
Foundations and Trends® in Computer Graphics and Vision
Flexible Spatial Configuration of Local Image Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian optimization of the scale saliency filter
Image and Vision Computing
Exploiting Information Theory for Filtering the Kadir Scale-Saliency Detector
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
The quantitative characterization of the distinctiveness and robustness of local image descriptors
Image and Vision Computing
Registering aerial video images using the projective constraint
IEEE Transactions on Image Processing
Sparse flexible models of local features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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We introduce a new method that characterizes typical local image features (e.g., SIFT [9], phase feature [3]) in terms of their distinctiveness, detectability, and robustness to image deformations. This is useful for the task of classifying local image features in terms of those three properties. The importance of this classification process for a recognition system using local features is as follows: a) reduce the recognition time due to a smaller number of features present in the test image and in the database of model features; b) improve the recognition accuracy since only the most useful features for the recognition task are kept in the model database; and c) increase the scalability of the recognition system given the smaller number of features per model. A discriminant classifier is trained to select well behaved feature points. A regression network is then trained to provide quantitative models of the detection distributions for each selected feature point. It is important to note that both the classifier and the regression network use image data alone as their input. Experimental results show that the use of these trained networks not only improves the performance of our recognition system, but it also significantly reduces the computation time for the recognition process.