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 Boosted Random Ferns (BRFs)

 Description:
   This program computes the Boosted Random Ferns classifier (BRFs)
   used to perfom efficient detection of object categories in images.

   Particularly, the BRFs classifier [3] is computed using Real AdaBoost 
   in order to select and combine -automatically- the most discriminative 
   weak classifiers (WCs) and where each one consists of a specific random 
   fern [1]. The random ferns are computed over local Histograms of
   Oriented Gradients (HOG) with the goal of increasing its robustness
   against lighting and intra-class changes.

   In contrast to the original version of BRFs [3], in this program the 
   BRFs classifier uses shared random ferns [4] to speed up the detection 
   step since the feature computation -convolution of ferns in the image- 
   is done in advance and independent of the number of weak classifiers. 

   For more detail about the BRFs classifier refer to references [2][3][4].

 Comments:
   The parameters of the classifier can be found in the fun_parameters.m 
   function (/files/functions/). The fun_experiments.m function file allows
   to compute and evaluate different classifiers, each one computed with 
   different parameters.

   For evaluation, the UIUC Cars Dataset [5] is used. This dataset contains
   108 testing images with cars -side view- at multiple scales. For training,
   500 positive and negative imageas are used. If anyone uses this dataset, 
   please cite the following references [5][6].

    If you make use of this code for research articles, we kindly encourage
    to cite the references [2][3][4], listed below. This code is only for 
    research and educational purposes.

 Steps:
   Steps to exucute the program:
     1. Run the prg_setup.m file to configure the program paths.
     2. Run the prg_brfs.m file (/files/) to compute the classifier and to
        perfom detection over the given dataset. 
     3. Run the fun_uiuc_cars_detection_performance.m (/files/datasets/) 
        to compute the detection performance plots of the classifier and 
        to determine the detection threshold (EER threshold).
     4. Run the fun_uiuc_cars_detection_images.m file to observe the 
        detection results in the images. The images are saved at the
        /images/detections/ folder. Set the detection threshold (detThr)
        in accordance to the EER.

 References:
   [1] Fast keypoint recognition in ten lines of code. M. Ozuysal, P. Fua,
       V. Lepetit. Computer Vision and Pattern Recognition (CVPR), 2007.

   [2] Bootstrapping Boosted Random Ferns for Discriminative and Efficient
       Object Classification. M. Villamizar, J. Andrade-Cetto, A. Sanfeliu
       and F. Moreno-Noguer. Pattern Recognition, 2012.

   [3] Efficient Rotation Invariant Object Detection using Boosted Random 
       Ferns. M. Villamizar, F. Moreno-Noguer, J. Andrade-Cetto and 
       A. Sanfeliu. Conference on Computer Vision and Pattern Recognition
       (CVPR), San Francisco, USA, June 2010.

   [4] Shared Random Ferns for Efficient Detection of Multiple Categories.
       M. Villamizar, F. Moreno-Noguer, J. Andrade-Cetto and A. Sanfeliu.
       International Conference on Pattern Recognition (ICPR). Istanbul, 
       Turkey, August 2010.
   [5] Shivani Agarwal, Aatif Awan, and Dan Roth, Learning to detect 
       objects in images via a sparse, part-based representation.
       IEEE Transactions on Pattern Analysis and Machine Intelligence, 
       26(11):1475-1490, 2004.
   [6] Shivani Agarwal and Dan Roth, Learning a sparse representation for 
       object detection. In Proceedings of the Seventh European Conference 
       on Computer Vision, Part IV, pages 113-130, Copenhagen, Denmark, 2002.

 Contact: 
   Michael Villamizar
   mvillami@iri.upc.edu
   http://www.iri.upc.edu/people/mvillami/
   Institut de Robòtica i Informàtica Industrial CSIC-UPC
   Barcelona - Spain
   2014