\cvarg{image}{The image. Keypoints (corners) will be detected on this.}
\cvarg{keypoints}{Keypoints detected on the image.}
\cvarg{threshold}{Threshold on difference between intensity of center pixel and
pixels on circle around this pixel. See description of the algorithm.}
\cvarg{nonmaxSupression}{If it is true then non-maximum supression will be applied to detected corners (keypoints). }
\end{description}
\ifCPy
\ifPy
\cvclass{CvSURFPoint}
A SURF keypoint, represented as a tuple \texttt{((x, y), laplacian, size, dir, hessian)}.
\begin{description}
\cvarg{x}{x-coordinate of the feature within the image}
\cvarg{y}{y-coordinate of the feature within the image}
\cvarg{laplacian}{-1, 0 or +1. sign of the laplacian at the point. Can be used to speedup feature comparison since features with laplacians of different signs can not match}
\cvarg{size}{size of the feature}
\cvarg{dir}{orientation of the feature: 0..360 degrees}
\cvarg{hessian}{value of the hessian (can be used to approximately estimate the feature strengths; see also params.hessianThreshold)}
\end{description}
\fi
\cvCPyFunc{ExtractSURF}
Extracts Speeded Up Robust Features from an image.
\cvarg{mask}{The optional input 8-bit mask. The features are only found in the areas that contain more than 50\% of non-zero mask pixels}
\ifC
\cvarg{keypoints}{The output parameter; double pointer to the sequence of keypoints. The sequence of CvSURFPoint structures is as follows:}
\begin{lstlisting}
typedef struct CvSURFPoint
{
CvPoint2D32f pt; // position of the feature within the image
int laplacian; // -1, 0 or +1. sign of the laplacian at the point.
// can be used to speedup feature comparison
// (normally features with laplacians of different
// signs can not match)
int size; // size of the feature
float dir; // orientation of the feature: 0..360 degrees
float hessian; // value of the hessian (can be used to
// approximately estimate the feature strengths;
// see also params.hessianThreshold)
}
CvSURFPoint;
\end{lstlisting}
\cvarg{descriptors}{The optional output parameter; double pointer to the sequence of descriptors. Depending on the params.extended value, each element of the sequence will be either a 64-element or a 128-element floating-point (\texttt{CV\_32F}) vector. If the parameter is NULL, the descriptors are not computed}
\else
\cvarg{keypoints}{sequence of keypoints.}
\cvarg{descriptors}{sequence of descriptors. Each SURF descriptor is a list of floats, of length 64 or 128.}
\fi
\cvarg{storage}{Memory storage where keypoints and descriptors will be stored}
\ifC
\cvarg{params}{Various algorithm parameters put to the structure CvSURFParams:}
\begin{lstlisting}
typedef struct CvSURFParams
{
int extended; // 0 means basic descriptors (64 elements each),
// 1 means extended descriptors (128 elements each)
double hessianThreshold; // only features with keypoint.hessian
// larger than that are extracted.
// good default value is ~300-500 (can depend on the
// average local contrast and sharpness of the image).
// user can further filter out some features based on
// their hessian values and other characteristics.
int nOctaves; // the number of octaves to be used for extraction.
// With each next octave the feature size is doubled
// (3 by default)
int nOctaveLayers; // The number of layers within each octave
// (4 by default)
}
CvSURFParams;
CvSURFParams cvSURFParams(double hessianThreshold, int extended=0);
// returns default parameters
\end{lstlisting}
\else
\cvarg{params}{Various algorithm parameters in a tuple \texttt{(extended, hessianThreshold, nOctaves, nOctaveLayers)}:
\begin{description}
\cvarg{extended}{0 means basic descriptors (64 elements each), 1 means extended descriptors (128 elements each)}
\cvarg{hessianThreshold}{only features with hessian larger than that are extracted. good default value is ~300-500 (can depend on the average local contrast and sharpness of the image). user can further filter out some features based on their hessian values and other characteristics.}
\cvarg{nOctaves}{the number of octaves to be used for extraction. With each next octave the feature size is doubled (3 by default)}
\cvarg{nOctaveLayers}{The number of layers within each octave (4 by default)}
\end{description}}
\fi
\end{description}
The function cvExtractSURF finds robust features in the image, as
described in \cite{Bay06}. For each feature it returns its location, size,
orientation and optionally the descriptor, basic or extended. The function
can be used for object tracking and localization, image stitching etc.
\ifC
See the
\texttt{find\_obj.cpp} demo in OpenCV samples directory.
\else
To extract strong SURF features from an image
\begin{lstlisting}
>>> import cv
>>> im = cv.LoadImageM("building.jpg", cv.CV_LOAD_IMAGE_GRAYSCALE)
int responseThreshold; // threshold for the approximatd laplacian,
// used to eliminate weak features
int lineThresholdProjected; // another threshold for laplacian to
// eliminate edges
int lineThresholdBinarized; // another threshold for the feature
// scale to eliminate edges
int suppressNonmaxSize; // linear size of a pixel neighborhood
// for non-maxima suppression
}
CvStarDetectorParams;
\end{lstlisting}
\else
\cvarg{params}{Various algorithm parameters in a tuple \texttt{(maxSize, responseThreshold, lineThresholdProjected, lineThresholdBinarized, suppressNonmaxSize)}:
\begin{description}
\cvarg{maxSize}{maximal size of the features detected. The following values of the parameter are supported: 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128}
\cvarg{responseThreshold}{threshold for the approximatd laplacian, used to eliminate weak features}
\cvarg{lineThresholdProjected}{another threshold for laplacian to eliminate edges}
\cvarg{lineThresholdBinarized}{another threshold for the feature scale to eliminate edges}
\cvarg{suppressNonmaxSize}{linear size of a pixel neighborhood for non-maxima suppression}
\end{description}
}
\fi
\end{description}
The function GetStarKeypoints extracts keypoints that are local
scale-space extremas. The scale-space is constructed by computing
approximate values of laplacians with different sigma's at each
pixel. Instead of using pyramids, a popular approach to save computing
time, all of the laplacians are computed at each pixel of the original
high-resolution image. But each approximate laplacian value is computed
in O(1) time regardless of the sigma, thanks to the use of integral
images. The algorithm is based on the paper
Agrawal08
, but instead
of a square, hexagon or octagon it uses an 8-end star shape, hence the name,
consisting of overlapping upright and tilted squares.
\ifC
Each computed feature is represented by the following structure:
\begin{lstlisting}
typedef struct CvStarKeypoint
{
CvPoint pt; // coordinates of the feature
int size; // feature size, see CvStarDetectorParams::maxSize
float response; // the approximated laplacian value at that point.
}
CvStarKeypoint;
inline CvStarKeypoint cvStarKeypoint(CvPoint pt, int size, float response);
\end{lstlisting}
\else
Each keypoint is represented by a tuple \texttt{((x, y), size, response)}:
\begin{description}
\cvarg{x, y}{Screen coordinates of the keypoint}
\cvarg{size}{feature size, up to \texttt{maxSize}}
\cvarg{response}{approximated laplacian value for the keypoint}
int num\_quant\_bits = DEFAULT\_NUM\_QUANT\_BITS, bool print\_status = true);
}
\begin{description}
\cvarg{base\_set}{Vector of \texttt{BaseKeypoint} type. Contains keypoints from the image are used for training}
\cvarg{rng}{Random numbers generator is used for training}
\cvarg{make\_patch}{Patch generator is used for training}
\cvarg{num\_trees}{Number of randomized trees used in RTreeClassificator}
\cvarg{depth}{Maximum tree depth}
%\cvarg{views} {}
\cvarg{reduced\_num\_dim}{Number of dimensions are used in compressed signature}
\cvarg{num\_quant\_bits}{Number of bits are used for quantization}
\cvarg{print\_status}{Print current status of training on the console}
\end{description}
\cvCppFunc{RTreeClassifier::getSignature}
Returns signature for image patch
\cvdefCpp{
void getSignature(IplImage *patch, uchar *sig)
}
\cvdefCpp{
void getSignature(IplImage *patch, float *sig)
}
\begin{description}
\cvarg{patch}{Image patch to calculate signature for}
\cvarg{sig}{Output signature (array dimension is \texttt{reduced\_num\_dim)}}
\end{description}
\cvCppFunc{RTreeClassifier::getSparseSignature}
The function is simular to \texttt{getSignature} but uses the threshold for removing all signature elements less than the threshold. So that the signature is compressed
\cvarg{num\_quant\_bits}{Number of bits are used for quantization}
\end{description}
Below there is an example of \texttt{RTreeClassifier} usage for feature matching. There are test and train images and we extract features from both with SURF. Output is $best\_corr$ and $best\_corr\_idx$ arrays which keep the best probabilities and corresponding features indexes for every train feature.
% ===== Example. Using RTreeClassifier for features matching =====
