lib/ccv_numeric.c

ccv_minimize

void ccv_minimize(ccv_dense_matrix_t *x, int length, double red, ccv_minimize_f func, ccv_minimize_param_t params, void *data)

Linear-search to minimize function with partial derivatives. It is formed after minimize.m.

• x: The input vector.
• length: The length of line.
• red: The step size.
• func: (int ccv_minimize_f)(const ccv_dense_matrix_t* x, double* f, ccv_dense_matrix_t* df, void* data). Compute the function value, and its partial derivatives.
• params: A ccv_minimize_param_t structure that defines various aspect of the minimize function.
• data: Any extra user data.

ccv_minimize_param_t

• extrap: Extrapolate value.
• interp: Interpolate value.
• max_iter: Maximum iterations.
• ratio: Increase ratio.
• rho: Decrease ratio.
• sig: Sigma.

ccv_filter

void ccv_filter(ccv_dense_matrix_t *a, ccv_dense_matrix_t *b, ccv_dense_matrix_t **d, int type, int padding_pattern)

Convolve on dense matrix a with dense matrix b. This function has a soft dependency on FFTW3. If no FFTW3 exists, ccv will use KissFFT shipped with it. FFTW3 is about 35% faster than KissFFT.

• a: Dense matrix a.
• b: Dense matrix b.
• d: The output matrix.
• type: The type of output matrix, if 0, ccv will try to match the input matrix for appropriate type.
• padding_pattern: ccv doesn’t support padding pattern for now.

ccv_filter_kernel

void ccv_filter_kernel(ccv_dense_matrix_t *x, ccv_filter_kernel_f func, void *data)

Fill a given dense matrix with a kernel function.

• x: The matrix to be filled with.
• func: (double ccv_filter_kernel_f(double x, double y, void* data), compute the value with given x, y.
• data: Any extra user data.

ccv_distance_transform

void ccv_distance_transform(ccv_dense_matrix_t *a, ccv_dense_matrix_t **b, int type, ccv_dense_matrix_t **x, int x_type, ccv_dense_matrix_t **y, int y_type, double dx, double dy, double dxx, double dyy, int flag)

Distance transform. The current implementation follows Distance Transforms of Sampled Functions. The dynamic programming technique has O(n) time complexity.

• a: The input matrix.
• b: The output matrix.
• type: The type of output matrix, if 0, ccv will try to match the input matrix for appropriate type.
• x: The x coordinate offset.
• x_type: The type of output x coordinate offset, if 0, ccv will default to CCV_32S | CCV_C1.
• y: The y coordinate offset.
• y_type: The type of output x coordinate offset, if 0, ccv will default to CCV_32S | CCV_C1.
• dx: The x coefficient.
• dy: The y coefficient.
• dxx: The x^2 coefficient.
• dyy: The y^2 coefficient.
• flag: CCV_GSEDT, generalized squared Euclidean distance transform. CCV_NEGATIVE, negate value in input matrix for computation; effectively, this enables us to compute the maximum distance transform rather than minimum (default one).

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