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

• interp : interpolate value.
• extrap : extrapolate 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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