A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Linear Time Euclidean Distance Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving object detection with boosted histograms
Image and Vision Computing
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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In this paper we describe an object class model and a detection scheme based on feature maps, i.e. binary images indicating occurrences of various local features. Any type of local feature and any number of features can be used to generate feature maps. The choice of which features to use can thus be adapted to the task at hand, without changing the general framework. An object class is represented by a boosted decision tree classifier (which may be cascaded) based on normalized distances to feature occurrences. The resulting object class model is essentially a linear combination of a set of flexible configurations of the features used. Within this framework we present an efficient detection scheme that uses a hierarchical search strategy. We demonstrate experimentally that this detection scheme yields a significant speedup compared to sliding window search. We evaluate the detection performance on a standard dataset [7], showing state of the art results. Features used in this paper include edges, corners, blobs and interest points.