restored doc on latent svm that was lost in moving to rst
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modules/objdetect/doc/latent_svm.rst
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modules/objdetect/doc/latent_svm.rst
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Latent SVM
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===============================================================
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.. highlight:: cpp
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Discriminatively Trained Part Based Models for Object Detection
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---------------------------------------------------------------
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The object detector described below has been initially proposed by
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P.F. Felzenszwalb in [Felzenszwalb2010]_. It is based on a
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Dalal-Triggs detector that uses a single filter on histogram of
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oriented gradients (HOG) features to represent an object category.
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This detector uses a sliding window approach, where a filter is
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applied at all positions and scales of an image. The first
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innovation is enriching the Dalal-Triggs model using a
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star-structured part-based model defined by a "root" filter
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(analogous to the Dalal-Triggs filter) plus a set of parts filters
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and associated deformation models. The score of one of star models
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at a particular position and scale within an image is the score of
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the root filter at the given location plus the sum over parts of the
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maximum, over placements of that part, of the part filter score on
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its location minus a deformation cost easuring the deviation of the
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part from its ideal location relative to the root. Both root and
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part filter scores are defined by the dot product between a filter
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(a set of weights) and a subwindow of a feature pyramid computed
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from the input image. Another improvement is a representation of the
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class of models by a mixture of star models. The score of a mixture
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model at a particular position and scale is the maximum over
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components, of the score of that component model at the given
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location.
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CvLSVMFilterPosition
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--------------------
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.. ocv:struct:: CvLSVMFilterPosition
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Structure describes the position of the filter in the feature pyramid.
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.. ocv:member:: unsigned int l
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level in the feature pyramid
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.. ocv:member:: unsigned int x
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x-coordinate in level l
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.. ocv:member:: unsigned int y
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y-coordinate in level l
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CvLSVMFilterObject
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------------------
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.. ocv:struct:: CvLSVMFilterObject
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Description of the filter, which corresponds to the part of the object.
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.. ocv:member:: CvLSVMFilterPosition V
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ideal (penalty = 0) position of the partial filter
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from the root filter position (V_i in the paper)
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.. ocv:member:: float fineFunction[4]
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vector describes penalty function (d_i in the paper)
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pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
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.. ocv:member:: int sizeX, sizeY
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Rectangular map (sizeX x sizeY),
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every cell stores feature vector (dimension = p)
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.. ocv:member:: int numFeatures
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number of features
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.. ocv:member:: float *H
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matrix of feature vectors to set and get
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feature vectors (i,j) used formula H[(j * sizeX + i) * p + k],
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where k - component of feature vector in cell (i, j)
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CvLatentSvmDetector
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-------------------
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.. ocv:struct:: CvLatentSvmDetector
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Structure contains internal representation of trained Latent SVM detector.
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.. ocv:member:: int num_filters
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total number of filters (root plus part) in model
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.. ocv:member:: int num_components
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number of components in model
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.. ocv:member:: int* num_part_filters
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array containing number of part filters for each component
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.. ocv:member:: CvLSVMFilterObject** filters
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root and part filters for all model components
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.. ocv:member:: float* b
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biases for all model components
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.. ocv:member:: float score_threshold
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confidence level threshold
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CvObjectDetection
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-----------------
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.. ocv:struct:: CvObjectDetection
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Structure contains the bounding box and confidence level for detected object.
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.. ocv:member:: CvRect rect
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bounding box for a detected object
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.. ocv:member:: float score
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confidence level
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cvLoadLatentSvmDetector
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-----------------------
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Loads trained detector from a file.
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.. ocv:function:: CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename)
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:param filename: Name of the file containing the description of a trained detector
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cvReleaseLatentSvmDetector
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--------------------------
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Release memory allocated for CvLatentSvmDetector structure.
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.. ocv:function:: void cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector)
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:param detector: CvLatentSvmDetector structure to be released
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cvLatentSvmDetectObjects
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------------------------
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Find rectangular regions in the given image that are likely to contain objects
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and corresponding confidence levels.
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.. ocv:function:: CvSeq* cvLatentSvmDetectObjects(IplImage* image, CvLatentSvmDetector* detector, CvMemStorage* storage, float overlap_threshold, int numThreads)
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:param image: image
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:param detector: LatentSVM detector in internal representation
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:param storage: Memory storage to store the resultant sequence of the object candidate rectangles
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:param overlap_threshold: Threshold for the non-maximum suppression algorithm
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:param numThreads: Number of threads used in parallel version of the algorithm
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.. [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
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@ -8,3 +8,4 @@ objdetect. Object Detection
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:maxdepth: 2
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cascade_classification
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latent_svm
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