Implements a weighted, polynomial expansion locally at each point in a 2D
scalar field. The weighting, specified by the "applicability" matrix, would
typically be Gaussian for many applications, but that is not required. The
basis set consists of the matrices (1, x, y, x2, x * y, y2). To use, simply
convolve each of the generated kernels over the field. Each convolution
will produce the coefficent for the corresponding basis element.
The outputs are the six kernels: "dcKernel", "xKernel", "yKernel",
"xxKernel", "xyKernel" and "yyKernel"
See "Spatial domain methods for orientation and velocity estimation" by
Gunnar Farneback, chapter 3, for a detailed description.
Implements a weighted, polynomial expansion locally at each point in a 2D scalar field. The weighting, specified by the "applicability" matrix, would typically be Gaussian for many applications, but that is not required. The basis set consists of the matrices (1, x, y, x2, x * y, y2). To use, simply convolve each of the generated kernels over the field. Each convolution will produce the coefficent for the corresponding basis element.
The outputs are the six kernels: "dcKernel", "xKernel", "yKernel", "xxKernel", "xyKernel" and "yyKernel"
See "Spatial domain methods for orientation and velocity estimation" by Gunnar Farneback, chapter 3, for a detailed description.
Use QuadraticBasis instead