16 citations · 17 across the 4 of their papers we have counts for
5 papers
Memory-Efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment
Nenad Markuš, Ivan Gogić, Igor S. Pandžić +1
Ren et al. recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector a…
Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning from Unlabeled Videos and 3D-Shape Retrieval
Nenad Markuš, Igor S. Pandžić, Jörgen Ahlberg
Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs. However, data of this kind is not always available since detailed keypoin…
Constructing Binary Descriptors with a Stochastic Hill Climbing Search
Nenad Markuš, Igor S. Pandžić, Jörgen Ahlberg
Binary descriptors of image patches provide processing speed advantages and require less storage than methods that encode the patch appearance with a vector of real numbers. We pro…
Fast Localization of Facial Landmark Points
Nenad Markuš, Miroslav Frljak, Igor S. Pandžić +2
Localization of salient facial landmark points, such as eye corners or the tip of the nose, is still considered a challenging computer vision problem despite recent efforts. This i…
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
Nenad Markuš, Miroslav Frljak, Igor S. Pandžić +2
We describe a method for visual object detection based on an ensemble of optimized decision trees organized in a cascade of rejectors. The trees use pixel intensity comparisons in…