Discrete derivative approximations with scale-space properties
A commonly occurring problem in computer vision concerns how to compute derivative approximations from discrete data. This problem arises, for example, when computing image descriptors such as features or differential invariants from image data and when relating image properties to phenomena in the outside world. Since differential geometry is a natural framework for describing geometric relations, formulations in terms of derivatives can be expected to arise in a large number of vision problems.
KeywordsGaussian Kernel Filter Coefficient Discrete Approximation Discrete Analogue Discrete Signal
Unable to display preview. Download preview PDF.