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+<title>Object Detection Using Haar-like Features with Cascade of Boosted
+Classifiers</title>
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+
+<div class=Section1>
+
+<h1><span lang=EN-US>Rapid Object Detection With A Cascade of Boosted
+Classifiers Based on Haar-like Features</span></h1>
+
+<h2><span lang=EN-US>Introduction</span></h2>
+
+<p class=MsoNormal><span lang=EN-US>This document describes how to train and
+use a cascade of boosted classifiers for rapid object detection. A large set of
+over-complete haar-like features provide the basis for the simple individual
+classifiers. Examples of object detection tasks are face, eye and nose
+detection, as well as logo detection. </span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>The sample detection task in this document
+is logo detection, since logo detection does not require the collection of
+large set of registered and carefully marked object samples. Instead we assume
+that from one prototype image, a very large set of derived object examples can
+be derived (</span><span class=Typewch><span lang=EN-US>createsamples</span></span><span
+lang=EN-US> utility, see below).</span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>A detailed description of the training/evaluation
+algorithm can be found in [1] and [2].</span></p>
+
+<h2><span lang=EN-US>Samples Creation</span></h2>
+
+<p class=MsoNormal><span lang=EN-US>For training a training samples must be
+collected. There are two sample types: negative samples and positive samples.
+Negative samples correspond to non-object images. Positive samples correspond
+to object images.</span></p>
+
+<h3><span lang=EN-US>Negative Samples</span></h3>
+
+<p class=MsoNormal><span lang=EN-US>Negative samples are taken from arbitrary
+images. These images must not contain object representations. Negative samples
+are passed through background description file. It is a text file in which each
+text line contains the filename (relative to the directory of the description
+file) of negative sample image. This file must be created manually. Note that
+the negative samples and sample images are also called background samples or
+background samples images, and are used interchangeably in this document</span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>Example of negative description file:</span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>Directory structure:</span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US>/img</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US> img1.jpg</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US> img2.jpg</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US>bg.txt</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US> </span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span style='font-family:"Times New Roman";
+font-weight:normal'>File </span></span><span class=Typewch><span lang=EN-US>bg.txt:</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US>img/img1.jpg</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US>img/img2.jpg</span></span></p>
+
+<h3><span lang=EN-US>Positive Samples</span></h3>
+
+<p class=MsoNormal><span lang=EN-US>Positive samples are created by </span><span
+class=Typewch><span lang=EN-US>createsamples</span></span><span lang=EN-US>
+utility. They may be created from single object image or from collection of
+previously marked up images.<br>
+<br>
+</span></p>
+
+<p class=MsoNormal><span lang=EN-US>The single object image may for instance
+contain a company logo. Then are large set of positive samples are created from
+the given object image by randomly rotating, changing the logo color as well as
+placing the logo on arbitrary background.</span></p>
+
+<p class=MsoNormal><span lang=EN-US>The amount and range of randomness can be
+controlled by command line arguments. </span></p>
+
+<p class=MsoNormal><span lang=EN-US>Command line arguments:</span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- vec <vec_file_name></span></span><span
+lang=EN-US> </span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt'><span lang=EN-US>name of the
+output file containing the positive samples for training</span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- img <image_file_name></span></span><span
+lang=EN-US> </span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt'><span lang=EN-US>source object
+image (e.g., a company logo)</span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- bg <background_file_name></span></span><span
+lang=EN-US> </span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt'><span lang=EN-US>background
+description file; contains a list of images into which randomly distorted
+versions of the object are pasted for positive sample generation</span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- num <number_of_samples></span></span><span
+lang=EN-US> </span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt'><span lang=EN-US>number of
+positive samples to generate </span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- bgcolor <background_color></span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+lang=EN-US> background color (currently grayscale images are assumed); the
+background color denotes the transparent color. Since there might be
+compression artifacts, the amount of color tolerance can be specified by </span><span
+class=Typewch><span lang=EN-US>\96bgthresh</span></span><span class=Typewch><span
+lang=EN-US style='font-family:Arial;font-weight:normal'>. </span></span><span
+lang=EN-US>All pixels between </span><span class=Typewch><span lang=EN-US>bgcolor-bgthresh</span></span><span
+lang=EN-US> and </span><span class=Typewch><span lang=EN-US>bgcolor+bgthresh</span></span><span
+lang=EN-US> are regarded as transparent.</span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- bgthresh <background_color_threshold></span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- inv</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+lang=EN-US> if specified, the colors will be inverted</span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- randinv</span></span><span lang=EN-US> </span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+lang=EN-US> if specified, the colors will be inverted randomly</span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- maxidev <max_intensity_deviation></span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US> </span></span><span lang=EN-US>maximal
+intensity deviation of foreground samples pixels</span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- maxxangle <max_x_rotation_angle>,</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- maxyangle <max_y_rotation_angle>,</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- maxzangle <max_z_rotation_angle></span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+lang=EN-US> maximum rotation angles in radians</span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>-show</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+lang=EN-US> if specified, each sample will be shown. Pressing \91Esc\92 will
+continue creation process without samples showing. Useful debugging option.</span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- w <sample_width></span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US> </span></span><span class=Typewch><span
+lang=EN-US style='font-family:"Times New Roman";font-weight:normal'>width (in
+pixels) of the output samples</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- h <sample_height></span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US> </span></span><span class=Typewch><span
+lang=EN-US style='font-family:"Times New Roman";font-weight:normal'>height (in
+pixels) of the output samples</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US> </span></span></p>
+
+<p class=MsoNormal><span lang=EN-US>For following procedure is used to create a
+sample object instance:</span></p>
+
+<p class=MsoNormal><span lang=EN-US>The source image is rotated random around
+all three axes. The chosen angle is limited my</span><span class=Typewch><span
+lang=EN-US> -max?angle</span></span><span lang=EN-US>. Next pixels of
+intensities in the range of </span><span class=Typewch><span lang=EN-US>[bg_color-bg_color_threshold;
+bg_color+bg_color_threshold]</span></span><span lang=EN-US> are regarded as
+transparent. White noise is added to the intensities of the foreground. If </span><span
+class=Typewch><span lang=EN-US>\96inv</span></span><span lang=EN-US> key is
+specified then foreground pixel intensities are inverted. If </span><span
+class=Typewch><span lang=EN-US>\96randinv</span></span><span lang=EN-US> key is
+specified then it is randomly selected whether for this sample inversion will
+be applied. Finally, the obtained image is placed onto arbitrary background
+from the background description file, resized to the pixel size specified by </span><span
+class=Typewch><span lang=EN-US>\96w</span></span><span lang=EN-US> and </span><span
+class=Typewch><span lang=EN-US>\96h</span></span><span lang=EN-US> and stored
+into the file specified by the </span><span class=Typewch><span lang=EN-US>\96vec</span></span><span
+lang=EN-US> command line parameter.</span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>Positive samples also may be obtained from
+a collection of previously marked up images. This collection is described by
+text file similar to background description file. Each line of this file
+corresponds to collection image. The first element of the line is image file
+name. It is followed by number of object instances. The following numbers are
+the coordinates of bounding rectangles (x, y, width, height).</span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>Example of description file:</span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>Directory structure:</span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US>/img</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US> img1.jpg</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US> img2.jpg</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US>info.dat</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US> </span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US style='font-family:
+"Times New Roman";font-weight:normal'>File </span></span><span class=Typewch><span
+lang=EN-US>info.dat:</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US>img/img1.jpg 1 140
+100 45 45</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US>img/img2.jpg 2 100
+200 50 50 50 30 25 25</span></span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>Image </span><span class=Typewch><span
+lang=EN-US>img1.jpg</span></span><span lang=EN-US> contains single object
+instance with bounding rectangle (140, 100, 45, 45). Image </span><span
+class=Typewch><span lang=EN-US>img2.jpg</span></span><span lang=EN-US> contains
+two object instances.</span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>In order to create positive samples from
+such collection </span><span class=Typewch><span lang=EN-US>\96info</span></span><span
+lang=EN-US> argument should be specified instead of </span><span class=Typewch><span
+lang=EN-US>\96img</span></span><span class=Typewch><span style='font-family:"Times New Roman";
+font-weight:normal'>:</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- info <collection_file_name></span></span><span
+lang=EN-US> </span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt'><span lang=EN-US>description file
+of marked up images collection</span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>The scheme of sample creation in this case
+is as follows. The object instances are taken from images. Then they are
+resized to samples size and stored in output file. No distortion is applied, so
+the only affecting arguments are </span><span class=Typewch><span lang=EN-US>\96w</span></span><span
+lang=EN-US>, </span><span class=Typewch><span lang=EN-US>-h</span></span><span
+lang=EN-US>, </span><span class=Typewch><span lang=EN-US>-show</span></span><span
+lang=EN-US> and </span><span class=Typewch><span lang=EN-US>\96num</span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'>.</span></span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US>createsamples</span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> utility may be used for examining samples stored in positive samples
+file. In order to do this only </span></span><span class=Typewch><span
+lang=EN-US>\96vec</span></span><span class=Typewch><span lang=EN-US
+style='font-family:"Times New Roman";font-weight:normal'>, </span></span><span
+class=Typewch><span lang=EN-US>\96w</span></span><span class=Typewch><span
+lang=EN-US style='font-family:"Times New Roman";font-weight:normal'> and </span></span><span
+class=Typewch><span lang=EN-US>\96h</span></span><span class=Typewch><span
+lang=EN-US style='font-family:"Times New Roman";font-weight:normal'> parameters
+should be specified.</span></span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>Note that for training, it does not matter
+how positive samples files are generated. So the </span><span class=Typewch><span
+lang=EN-US>createsamples</span></span><span lang=EN-US> utility is only one way
+to collect/create a vector file of positive samples.</span></p>
+
+<h2><span lang=EN-US>Training</span></h2>
+
+<p class=MsoNormal><span lang=EN-US>The next step after samples creation is
+training of classifier. It is performed by the </span><span class=Typewch><span
+lang=EN-US>haartraining</span></span><span lang=EN-US> utility.</span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>Command line arguments:</span><span
+class=Typewch><span lang=EN-US> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- data <dir_name></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> directory name in which the trained classifier is stored</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- vec <vec_file_name></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> file name of positive sample file (created by </span></span><span
+class=Typewch><span lang=EN-US>trainingsamples</span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> utility or by any other means)</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- bg <background_file_name></span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> background description file</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- npos <number_of_positive_samples>,</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- nneg <number_of_negative_samples></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> number of positive/negative samples used in training of each
+classifier stage. Reasonable values are npos = 7000 and nneg = 3000.</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- nstages <number_of_stages></span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US> </span></span><span class=Typewch><span
+lang=EN-US style='font-family:"Times New Roman";font-weight:normal'>number of
+stages to be trained</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- nsplits <number_of_splits></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> determines the weak classifier used in stage classifiers. If </span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman"'>1</span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'>, then a simple stump classifier is used, if </span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman"'>2</span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> and more, then CART classifier with </span></span><span class=Typewch><span
+lang=EN-US>number_of_splits</span></span><span class=Typewch><span lang=EN-US
+style='font-family:"Times New Roman";font-weight:normal'> internal (split)
+nodes is used</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- mem <memory_in_MB></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> Available memory in MB for precalculation. The more memory you
+have the faster the training process</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- sym (default),</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- nonsym</span></span><span class=Typewch><span
+lang=EN-US style='font-family:"Times New Roman";font-weight:normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> specifies whether the object class under training has vertical
+symmetry or not. Vertical symmetry speeds up training process. For instance,
+frontal faces show off vertical symmetry</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- minhitrate <min_hit_rate></span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> minimal desired hit rate for each stage classifier. Overall hit
+rate may be estimated as </span></span><span class=Typewch><span lang=EN-US>(min_hit_rate^number_of_stages)</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- maxfalsealarm <max_false_alarm_rate></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> maximal desired false alarm rate for each stage classifier. </span></span><span
+class=Typewch><span style='font-family:"Times New Roman";font-weight:normal'>Overall
+false alarm rate may be estimated as</span></span><span class=Typewch><span
+lang=EN-US> (max_false_alarm_rate^number_of_stages)</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- weighttrimming <weight_trimming></span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US> </span></span><span class=Typewch><span
+lang=EN-US style='font-family:"Times New Roman";font-weight:normal'>Specifies
+wheter and how much weight trimming should be used. A decent choice is 0.90.</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- eqw</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- mode <BASIC (default) | CORE | ALL></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> selects the type of haar features set used in training. BASIC use
+only upright features, while ALL uses the full set of upright and 45 degree
+rotated feature set. See [1] for more details.</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- w <sample_width>,</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- h <sample_height></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> Size of training samples (in pixels). Must have exactly the same
+values as used during training samples creation (utility </span></span><span
+class=Typewch><span lang=EN-US>trainingsamples</span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'>)</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US style='font-family:
+"Times New Roman";font-weight:normal'> </span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US style='font-family:
+"Times New Roman";font-weight:normal'>Note: in order to use multiprocessor
+advantage a compiler that supports OpenMP 1.0 standard should be used.</span></span></p>
+
+<h2><span lang=EN-US>Application</span></h2>
+
+<p class=MsoNormal><span lang=EN-US>OpenCV cvHaarDetectObjects() function (in
+particular haarFaceDetect demo) is used for detection.</span></p>
+
+<h3><span lang=EN-US>Test Samples</span></h3>
+
+<p class=MsoNormal><span lang=EN-US>In order to evaluate the performance of
+trained classifier a collection of marked up images is needed. When such
+collection is not available test samples may be created from single object
+image by </span><span class=Typewch><span lang=EN-US>createsamples</span></span><span
+lang=EN-US> utility. The scheme of test samples creation in this case is
+similar to training samples creation since each test sample is a background
+image into which a randomly distorted and randomly scaled instance of the
+object picture is pasted at a random position. </span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>If both </span><span class=Typewch><span
+lang=EN-US>\96img</span></span><span lang=EN-US> and </span><span class=Typewch><span
+lang=EN-US>\96info</span></span><span lang=EN-US> arguments are specified then
+test samples will be created by </span><span class=Typewch><span lang=EN-US>createsamples</span></span><span
+lang=EN-US> utility. The sample image is arbitrary distorted as it was
+described below, then it is placed at random location to background image and
+stored. The corresponding description line is added to the file specified by </span><span
+class=Typewch><span lang=EN-US>\96info</span></span><span lang=EN-US> argument.</span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>The </span><span class=Typewch><span
+lang=EN-US>\96w</span></span><span lang=EN-US> and </span><span class=Typewch><span
+lang=EN-US>\96h</span></span><span lang=EN-US> keys determine the minimal size of
+placed object picture.</span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>The test image file name format is as
+follows:</span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US>imageOrderNumber_x_y_width_height.jpg</span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'>, where </span></span><span class=Typewch><span lang=EN-US>x</span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'>, </span></span><span class=Typewch><span lang=EN-US>y</span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'>, </span></span><span class=Typewch><span lang=EN-US>width</span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> and </span></span><span class=Typewch><span lang=EN-US>height</span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> are the coordinates of placed object bounding rectangle.</span></span></p>
+
+<p class=MsoNormal><span class=Typewch><span lang=EN-US style='font-family:
+"Times New Roman";font-weight:normal'>Note that you should use a background
+images set different from the background image set used during training.</span></span></p>
+
+<h3><span class=Typewch><span lang=EN-US style='font-family:"Times New Roman"'>Performance
+Evaluation</span></span></h3>
+
+<p class=MsoNormal><span lang=EN-US>In order to evaluate the performance of the
+classifier </span><span class=Typewch><span lang=EN-US>performance</span></span><span
+lang=EN-US> utility may be used. It takes a collection of marked up images,
+applies the classifier and outputs the performance, i.e. number of found
+objects, number of missed objects, number of false alarms and other
+information.</span></p>
+
+<p class=MsoNormal><span lang=EN-US> </span></p>
+
+<p class=MsoNormal><span lang=EN-US>Command line arguments:</span><span
+class=Typewch><span lang=EN-US> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- data <dir_name></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> directory name in which the trained classifier is stored</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- info <collection_file_name></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> file with test samples description</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- maxSizeDiff <max_size_difference></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'>,</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- maxPosDiff <max_position_difference></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> determine the criterion of reference and detected rectangles
+coincidence. Default values are 1.5 and 0.3 respectively.</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- sf <scale_factor></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'>,</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> detection parameter. Default value is 1.2.</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- w <sample_width>,</span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US>- h <sample_height></span></span><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> </span></span></p>
+
+<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
+class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
+normal'> Size of training samples (in pixels). Must have exactly the same
+values as used during training (utility </span></span><span class=Typewch><span
+lang=EN-US>haartraining</span></span><span class=Typewch><span lang=EN-US
+style='font-family:"Times New Roman";font-weight:normal'>)</span></span></p>
+
+<h2><span lang=EN-US>References</span></h2>
+
+<p class=MsoNormal><span lang=EN-US>[1] Rainer Lienhart and Jochen Maydt. An
+Extended Set of Haar-like Features for Rapid Object Detection. Submitted to
+ICIP2002.</span></p>
+
+<p class=MsoNormal><span lang=EN-US>[2] Alexander Kuranov, Rainer Lienhart, and
+Vadim Pisarevsky. An Empirical Analysis of Boosting Algorithms for Rapid
+Objects With an Extended Set of Haar-like Features. Intel Technical Report
+MRL-TR-July02-01, 2002.</span></p>
+
+</div>
+
+</body>
+
+</html>
projects / opencv / blobdiff