A multiresolution approach for texture synthesis using the circular harmonic functions

Abstract

In this paper, an unsupervised model-based texture reproduction technique is described. In accordance with the Julesz's (1962) conjecture, the statistical properties of the prototype up to the second order are copied in order to generate a synthetic texture perceptually indistinguishable from the given sample. However, this task is accomplished using a hybrid approach which operates partially in the spatial domain and partially in a multiresolution domain. The latter employed is the circular harmonic function (CHF) domain since it has been proven to be well suited for mimicking the behavior of the human visual system (HVS). This approach allows, for a wide range of textures typologies, obtaining synthetic textures that better match the prototype with respect to the ones obtained using techniques based on the Julesz's conjecture operating only in the spatial domain, and to dramatically reduce the computational complexity of similar methods operating only in the multiresolution domain.

Design of steerable filters for feature detection using canny-like criteria

“…al., Manduchi et al, Simoncelli and Farid, and Teo and Hel-Or used this framework to approximate and design orientation-selective feature detectors [5], [6], [7], [8]. The concept of steerablity was also applied successfully in other areas of image processing such as texture analysis [9], [10] and image denoising [11].…”

On the Inter-Conversion Between Hermite and Laguerre Local Image Expansions

“…The backward index transformation formulas are and . In this notation, the linear transformation can be written as (10) for , where (11) and (12) The coefficients , arranged for and , give origin to a complex-valued transformation matrix , of size , which is unitary, since both 2-D HG and LG-CH subsets appearing in (10) are composed by orthonormal functions [9].…”

“…The use of the LG-CH image expansion has been advocated for different image analysis applications, such as local tomographic analysis [6], [7], morphological analysis [8], orientation estimation [6], [9], and texture analysis [10], [11].…”

Automatic texture exemplar extraction based on global and local textureness measures

“…In the last two decades, a number of texture synthesis methods [9][10][11][12] have been presented for texture synthesis, relying on optimizing the target texturing effect (it should be seamless in color or gradient domains). Turk [13] gave a sophisticated algorithm to synthesize a texture on a geometric model, which may have irregular deformations on the surface.…”