Doxygen tutorials: python basic

doc/py_tutorials/py_feature2d/py_brief/py_brief.markdown

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+BRIEF (Binary Robust Independent Elementary Features) {#tutorial_py_brief}
+=====================================================
+
+Goal
+----
+
+In this chapter
+   -   We will see the basics of BRIEF algorithm
+
+Theory
+------
+
+We know SIFT uses 128-dim vector for descriptors. Since it is using floating point numbers, it takes
+basically 512 bytes. Similarly SURF also takes minimum of 256 bytes (for 64-dim). Creating such a
+vector for thousands of features takes a lot of memory which are not feasible for resouce-constraint
+applications especially for embedded systems. Larger the memory, longer the time it takes for
+matching.
+
+But all these dimensions may not be needed for actual matching. We can compress it using several
+methods like PCA, LDA etc. Even other methods like hashing using LSH (Locality Sensitive Hashing) is
+used to convert these SIFT descriptors in floating point numbers to binary strings. These binary
+strings are used to match features using Hamming distance. This provides better speed-up because
+finding hamming distance is just applying XOR and bit count, which are very fast in modern CPUs with
+SSE instructions. But here, we need to find the descriptors first, then only we can apply hashing,
+which doesn't solve our initial problem on memory.
+
+BRIEF comes into picture at this moment. It provides a shortcut to find the binary strings directly
+without finding descriptors. It takes smoothened image patch and selects a set of \f$n_d\f$ (x,y)
+location pairs in an unique way (explained in paper). Then some pixel intensity comparisons are done
+on these location pairs. For eg, let first location pairs be \f$p\f$ and \f$q\f$. If \f$I(p) < I(q)\f$, then its
+result is 1, else it is 0. This is applied for all the \f$n_d\f$ location pairs to get a
+\f$n_d\f$-dimensional bitstring.
+
+This \f$n_d\f$ can be 128, 256 or 512. OpenCV supports all of these, but by default, it would be 256
+(OpenCV represents it in bytes. So the values will be 16, 32 and 64). So once you get this, you can
+use Hamming Distance to match these descriptors.
+
+One important point is that BRIEF is a feature descriptor, it doesn't provide any method to find the
+features. So you will have to use any other feature detectors like SIFT, SURF etc. The paper
+recommends to use CenSurE which is a fast detector and BRIEF works even slightly better for CenSurE
+points than for SURF points.
+
+In short, BRIEF is a faster method feature descriptor calculation and matching. It also provides
+high recognition rate unless there is large in-plane rotation.
+
+BRIEF in OpenCV
+---------------
+
+Below code shows the computation of BRIEF descriptors with the help of CenSurE detector. (CenSurE
+detector is called STAR detector in OpenCV)
+@code{.py}
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+img = cv2.imread('simple.jpg',0)
+
+# Initiate STAR detector
+star = cv2.FeatureDetector_create("STAR")
+
+# Initiate BRIEF extractor
+brief = cv2.DescriptorExtractor_create("BRIEF")
+
+# find the keypoints with STAR
+kp = star.detect(img,None)
+
+# compute the descriptors with BRIEF
+kp, des = brief.compute(img, kp)
+
+print brief.getInt('bytes')
+print des.shape
+@endcode
+The function brief.getInt('bytes') gives the \f$n_d\f$ size used in bytes. By default it is 32. Next one
+is matching, which will be done in another chapter.
+
+Additional Resources
+--------------------
+
+-#  Michael Calonder, Vincent Lepetit, Christoph Strecha, and Pascal Fua, "BRIEF: Binary Robust
+    Independent Elementary Features", 11th European Conference on Computer Vision (ECCV), Heraklion,
+    Crete. LNCS Springer, September 2010.
+2.  LSH (Locality Sensitive Hasing) at wikipedia.
+