restored doc on latent svm that was lost in moving to rst

modules/objdetect/doc/latent_svm.rst

@@ -0,0 +1,167 @@
+Latent SVM
+===============================================================
+
+.. highlight:: cpp
+
+
+Discriminatively Trained Part Based Models for Object Detection
+---------------------------------------------------------------
+
+The object detector described below has been initially proposed by
+P.F. Felzenszwalb in [Felzenszwalb2010]_.  It is based on a
+Dalal-Triggs detector that uses a single filter on histogram of
+oriented gradients (HOG) features to represent an object category.
+This detector uses a sliding window approach, where a filter is
+applied at all positions and scales of an image. The first
+innovation is enriching the Dalal-Triggs model using a
+star-structured part-based model defined by a "root" filter
+(analogous to the Dalal-Triggs filter) plus a set of parts filters
+and associated deformation models. The score of one of star models
+at a particular position and scale within an image is the score of
+the root filter at the given location plus the sum over parts of the
+maximum, over placements of that part, of the part filter score on
+its location minus a deformation cost easuring the deviation of the
+part from its ideal location relative to the root. Both root and
+part filter scores are defined by the dot product between a filter
+(a set of weights) and a subwindow of a feature pyramid computed
+from the input image. Another improvement is a representation of the
+class of models by a mixture of star models. The score of a mixture
+model at a particular position and scale is the maximum over
+components, of the score of that component model at the given
+location.
+
+
+CvLSVMFilterPosition
+--------------------
+.. ocv:struct:: CvLSVMFilterPosition
+
+Structure describes the position of the filter in the feature pyramid.
+
+    .. ocv:member:: unsigned int l
+    
+        level in the feature pyramid
+        
+    .. ocv:member:: unsigned int x
+    
+        x-coordinate in level l
+        
+    .. ocv:member:: unsigned int y
+    
+        y-coordinate in level l
+        
+        
+CvLSVMFilterObject
+------------------
+.. ocv:struct:: CvLSVMFilterObject
+
+Description of the filter, which corresponds to the part of the object.
+
+    .. ocv:member:: CvLSVMFilterPosition V
+        
+        ideal (penalty = 0) position of the partial filter
+        from the root filter position (V_i in the paper)
+        
+    .. ocv:member:: float fineFunction[4]
+        
+        vector describes penalty function (d_i in the paper)
+        pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
+        
+    .. ocv:member:: int sizeX, sizeY
+        
+        Rectangular map (sizeX x sizeY),
+        every cell stores feature vector (dimension = p)
+        
+    .. ocv:member:: int numFeatures
+    
+        number of features
+        
+    .. ocv:member:: float *H
+    
+        matrix of feature vectors to set and get 
+        feature vectors (i,j) used formula H[(j * sizeX + i) * p + k], 
+        where k - component of feature vector in cell (i, j)
+        
+CvLatentSvmDetector
+-------------------
+.. ocv:struct:: CvLatentSvmDetector
+
+Structure contains internal representation of trained Latent SVM detector.
+
+    .. ocv:member:: int num_filters
+    
+        total number of filters (root plus part) in model
+        
+    .. ocv:member:: int num_components
+    
+        number of components in model
+    
+    .. ocv:member:: int* num_part_filters
+    
+        array containing number of part filters for each component
+        
+    .. ocv:member:: CvLSVMFilterObject** filters
+    
+        root and part filters for all model components
+        
+    .. ocv:member:: float* b
+    
+        biases for all model components
+        
+    .. ocv:member:: float score_threshold
+    
+        confidence level threshold
+        
+        
+CvObjectDetection
+-----------------
+.. ocv:struct:: CvObjectDetection
+
+Structure contains the bounding box and confidence level for detected object.
+
+    .. ocv:member:: CvRect rect
+    
+        bounding box for a detected object
+        
+    .. ocv:member:: float score
+    
+        confidence level
+
+
+cvLoadLatentSvmDetector
+-----------------------
+
+Loads trained detector from a file.
+
+.. ocv:function:: CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename)
+
+    :param filename: Name of the file containing the description of a trained detector
+    
+
+cvReleaseLatentSvmDetector
+--------------------------
+
+Release memory allocated for CvLatentSvmDetector structure.
+
+.. ocv:function:: void cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector)
+
+    :param detector: CvLatentSvmDetector structure to be released
+
+
+cvLatentSvmDetectObjects
+------------------------
+
+Find rectangular regions in the given image that are likely to contain objects 
+and corresponding confidence levels.
+
+.. ocv:function:: CvSeq* cvLatentSvmDetectObjects(IplImage* image, CvLatentSvmDetector* detector,  CvMemStorage* storage,  float overlap_threshold, int numThreads)
+    
+    :param image: image 
+    :param detector: LatentSVM detector in internal representation
+    :param storage: Memory storage to store the resultant sequence of the object candidate rectangles
+    :param overlap_threshold: Threshold for the non-maximum suppression algorithm
+    :param numThreads: Number of threads used in parallel version of the algorithm
+    
+    
+.. [Felzenszwalb2010] Felzenszwalb, P. F. and Girshick, R. B. and McAllester, D. and Ramanan, D. *Object Detection with Discriminatively Trained Part Based Models*. PAMI, vol. 32, no. 9, pp. 1627-1645, September 2010 
+
+

modules/objdetect/doc/objdetect.rst

@@ -8,3 +8,4 @@ objdetect. Object Detection
     :maxdepth: 2
 
     cascade_classification
+    latent_svm