Texture synthesis-by-analysis method based on a multiscale early-vision model
نویسندگان
چکیده
Antonio Tabernero Universidad Politécnica de Madrid Facultad de Informática Boadilla del Monte 28660 Madrid, Spain E-mail: [email protected] Abstract. A new texture synthesis-by-analysis method, applying a visually based approach that has some important advantages over more traditional texture modeling and synthesis techniques is introduced. The basis of the method is to encode the textural information by sampling both the power spectrum and the histogram of homogeneously textured images. The spectrum is sampled in a log-polar grid using a pyramid Gabor scheme. The input image is split into a set of 16 Gabor channels (using four spatial frequency levels and four orientations), plus a lowpass residual (LPR). The energy and equivalent bandwidths of each channel, as well as the LPR power spectrum and the histogram, are measured and the latter two are compressed. The synthesis process consists of generating 16 Gabor filtered independent noise signals with spectral centers equal to those of the Gabor filters, whose energy and equivalent bandwidths are calculated to reproduce the measured values. These bandpass signals are mixed into a single image, whose LPR power spectrum and histogram are modified to match the original features. Despite the coarse sampling scheme used, very good results have been achieved with nonstructured textures as well as with some quasiperiodic textures. Besides being applicable to a wide range of textures, the method is robust (stable, fully automatic, linear, and with a fixed code length) and compact (it uses only 69 parameters). © 1996 Society of PhotoOptical Instrumentation Engineers.
منابع مشابه
Robust method for texture synthesis-by-analysis based on a multiscale Gabor scheme
We propose a new texture synthesis-by-analysis method inspired by current models of biological early vision and based on a multiscale Gabor scheme. The analysis stage starts with a log-polar sampling of the estimated power spectral density of the texture by a set of 4x4 Gabor filters, plus a low-pass residual (LPR). Then, for each channel, we compute its energy and its two (X,Y) bandwidths. The...
متن کاملTitle: “TEXTURE SYNTHESIS-BY-ANALYSIS BASED ON A MULTISCALE EARLY- VISION MODEL”. Authors:
This paper introduces a new texture synthesis-by-analysis method, applying a visual-based approach which has some important advantages over more traditional texture modeling and synthesis techniques. The basis of the method is to encode the textural information by sampling both the power spectrum and the histogram of homogeneously textured images. The spectrum is sampled in a log-polar grid by ...
متن کاملLeveling Cartoons, Texture Energy Markers, and Image Decomposition
The variational u+ v model for image decomposition aims at separating the image into a ‘cartoon component’ u, which consists of relatively flat plateaus for the object regions surrounded by abrupt edges, and a ‘texture component’ v, which contains smaller-scale oscillations plus possibly noise. Exploiting this model leads to improved performance in several image analysis and computer vision pro...
متن کاملA Novel Noise-Robust Texture Classification Method Using Joint Multiscale LBP
In this paper we describe a novel noise-robust texture classification method using joint multiscale local binary pattern. The first step in texture classification is to describe the texture by extracting different features. So far, several methods have been developed for this topic, one of the most popular ones is Local Binary Pattern (LBP) method and its variants such as Completed Local Binary...
متن کاملMultiscale Texture Orientation Analysis Using Spectral Total-Variation Decomposition
Multi-level texture separation can considerably improve texture analysis, a significant component in many computer vision tasks. This paper aims at obtaining precise local texture orientations of images in a multiscale manner, characterizing the main obvious ones as well as the very subtle ones. We use the total variation spectral framework to decompose the image into its different textural sca...
متن کامل