Constant-Time Filtering Using Shiftable Kernels

Filtering algorithms using shiftable kernels

It was recently demonstrated in [4] that the non-linear bilateral filter [18] can be efficiently implemented using an O(1) or constanttime algorithm. At the heart of this algorithm was the idea of approximating the Gaussian range kernel of the bilateral filter using trigonometric functions. In this letter, we explain how the idea in [4] can be extended to few other linear and non-linear filters...

C. Gotsman / Constant-time Filtering

11 (a) (b) (c) (d) (e) (f) Figure 6: Using Gaussian ltering to warp an image: (a) A s a m p l e 256 256 pixel black and white checkerboard image. Each colored square in the image has 8 8 pixels. (b) Point sampled w a r p o f (a) with warp functions x(uu v) = 256u=v, y(uu v) = v. (c) Gaussian kernel shapes required to lter the warp at some texture p ositions. The ellipses are tri-parametric-(aa ...

Using Cloudy Kernels for Imprecise Linear Filtering

Selecting a particular summative (i.e., formally equivalent to a probability distribution) kernel when filtering a digital signal can be a difficult task. To circumvent this difficulty, one can work with maxitive (i.e., formally equivalent to a possibility distribution) kernels. These kernels allow to consider at once sets of summative kernels with upper bounded bandwith. They also allow to per...

Real-time range image segmentation using adaptive kernels and Kalman filtering

Segmentation is ajimdamental process affecting the overall quality and utility of a machine vision system. Range Profile Tracking (RPT) is a systematic approach for stable, accurate and high speed segmentation of range images that is based on Kalman filtering. Tests of RPT have produced stable decompositions of second order surfaces bounded by jump and crease discontinuities, having a volumetri...

Seismic migration using the complex shiftable Pyramid