lib/ccv_classic.c

ccv_hog

void ccv_hog(ccv_dense_matrix_t *a, ccv_dense_matrix_t **b, int b_type, int sbin, int size)

Histogram-of-Oriented-Gradients implementation, specifically, it implements the HOG described in Object Detection with Discriminatively Trained Part-Based Models, Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester and Deva Ramanan.

• a: The input matrix.
• b: The output matrix.
• b_type: The type of output matrix, if 0, ccv will try to match the input matrix for appropriate type.
• sbin: The number of bins for orientation (default to 9, thus, for b, it will have 9 * 2 + 9 + 4 = 31 channels).
• size: The window size for HOG (default to 8)

ccv_canny

void ccv_canny(ccv_dense_matrix_t *a, ccv_dense_matrix_t **b, int type, int size, double low_thresh, double high_thresh)

Canny edge detector implementation. For performance reason, this is a clean-up reimplementation of OpenCV’s Canny edge detector, it has very similar performance characteristic as the OpenCV one. As of today, ccv’s Canny edge detector only works with CCV_8U or CCV_32S dense matrix type.

• a: The input matrix.
• b: The output matrix.
• type: The type of output matrix, if 0, ccv will create a CCV_8U | CCV_C1 matrix.
• size: The underlying Sobel filter size.
• low_thresh: The low threshold that makes the point interesting.
• high_thresh: The high threshold that makes the point acceptable.

ccv_otsu

int ccv_otsu(ccv_dense_matrix_t *a, double *outvar, int range)

OTSU implementation.

• a: The input matrix.
• outvar: The inter-class variance.
• range: The maximum range of data in the input matrix.

return: The threshold, inclusively. e.g. 5 means 0~5 is in the background, and 6~255 is in the foreground.

ccv_optical_flow_lucas_kanade

void ccv_optical_flow_lucas_kanade(ccv_dense_matrix_t *a, ccv_dense_matrix_t *b, ccv_array_t *point_a, ccv_array_t **point_b, ccv_size_t win_size, int level, double min_eigen)

Lucas Kanade optical flow implementation with image pyramid extension.

• a: The first frame
• b: The next frame
• point_a: The points in first frame, of ccv_decimal_point_t type
• point_b: The output points in the next frame, of ccv_decimal_point_with_status_t type
• win_size: The window size to compute each optical flow, it must be a odd number
• level: How many image pyramids to be used for the computation
• min_eigen: The minimal eigen-value to pass optical flow computation

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