Encapsulates histogram-of-gradient (HOG) feature computation. More...

Public Member Functions
int	numFeatures () const
	returns the number of features (numBlocks times _numOrientations)

cv::Size	numCells (const cv::Size &imgSize) const
	returns the size of the feature maps in terms of cells

cv::Size	numBlocks (const cv::Size &imgSize) const
	returns the size of the feature maps in terms of blocks

cv::Size	padImageSize (const cv::Size &imgSize) const
	returns the size of the padded (enlarged) image over which features are computed

pair< cv::Mat, cv::Mat >	gradientMagnitudeAndOrientation (const cv::Mat &img) const
	pre-process gradient magnitude and orientation (can be provided to computeFeatures)

void	computeFeatures (const cv::Mat &img, std::vector< cv::Mat > &features)
	feature calculation from greyscale image returns features as a vector of matrices of size numBlocks

void	computeFeatures (const pair< cv::Mat, cv::Mat > &gradMagAndOri, std::vector< cv::Mat > &features)
	features calculation from gradient magnitude and orientation (returned by gradientMagnitudeAndOrientation)

void	computeDenseFeatures (const cv::Mat &img, std::vector< cv::Mat > &features)
	compute features at each pixel location returns features as a vector of images the same size as the original image

cv::Mat	visualizeCells (const cv::Mat &img, int scale=2)
	visualization

Static Public Attributes
static int	DEFAULT_CELL_SIZE = 8
	default cell size (in pixels)

static int	DEFAULT_BLOCK_SIZE = 2
	default block size (in cells)

static int	DEFAULT_BLOCK_STEP = 1
	default block increment (in cells)

static int	DEFAULT_ORIENTATIONS = 9
	default number of quantized orientations

static drwnHOGNormalization	DEFAULT_NORMALIZATION = DRWN_HOG_L2_NORM
	default normalization method

static double	DEFAULT_CLIPPING_LB = 0.1
	default lower-bound clipping in [0, 1)

static double	DEFAULT_CLIPPING_UB = 0.5
	default upper-bound clipping in (0, 1]

static bool	DEFAULT_DIM_REDUCTION = false
	true for analytic dimensionality reduction

Protected Member Functions
void	computeCanonicalOrientations (vector< float > &x, vector< float > &y) const

void	computeCellHistograms (const pair< cv::Mat, cv::Mat > &gradMagAndOri, vector< cv::Mat > &cellHistorgams) const

void	computeBlockFeatures (const vector< cv::Mat > &cellHistograms, vector< cv::Mat > &features) const

void	normalizeFeatureVectors (std::vector< cv::Mat > &features) const

void	clipFeatureVectors (std::vector< cv::Mat > &features) const

Protected Attributes
int	_cellSize
	size of each cell in pixels

int	_blockSize
	number of cells in a block

int	_blockStep
	step to next block in cells

int	_numOrientations
	number of orientations in histogram

drwnHOGNormalization	_normalization
	normalization method

pair< double, double >	_clipping
	clipping for renormalization (0.0, 1.0 means none)

bool	_bDimReduction
	use dimensionality reduction trick of Felzenszwalb et al, PAMI 2010

Detailed Description

Encapsulates histogram-of-gradient (HOG) feature computation.

The HOG features are described in:

N. Dalal and B. Triggs, "Histogram of Oriented Gradients for Human Detection," CVPR 2005.
Felzenszwalb et al., "Object Detection with Discriminitively Trained Part Based Model," PAMI 2010.

The class can also be used for dense feature HOG calculation.

The documentation for this class was generated from the following files:

Public Member Functions