Preferential processing of target features in texture segmentation

Unsupervised Texture Segmentation: Comparison of Texture Features

Texture is an important image-content that has been utilized for different machine intelligent tasks, like those in machine vision and remote sensing, which identify objects of interest by segmenting the image texture. This paper aims at comparing texture features based on DFT (Discrete Fourier Transform) with ones based on Gabor wavelets for unsupervised image segmentation. The comparison is r...

BRIEF Features for Texture Segmentation

the impact of skopos on syntactic features of the target text

the present study is an experimental case study which investigates the impacts, if any, of skopos on syntactic features of the target text. two test groups each consisting of 10 ma students translated a set of sentences selected from advertising texts in the operative and informative mode. the resulting target texts were then statistically analyzed in terms of the number of words, phrases, si...

Wold features for unsupervised texture segmentation

An efficient texture representation for unsupervised segmentation is addressed based on the concept of Wold decomposition. Textures are described by the wavelet tuned to various scales and rotations to describe its deterministic component, and by the autogressive model to describe its indeterministic component. The wavelet features and the AR parameters capturing the perceptual properties, "per...

Two-Stage Texture Segmentation Using Complementary Features

In this paper, a two-stage texture segmentation approach is proposed where an initial segmentation map is obtained through unsupervised clustering of MRSAR features and is followed by self-supervised or bootstrapped classi cation of wavelet features. The selfsupervised stage is based on a segmentation con dence map, where the regions of \high con dence" and \low con dence" are identi ed on the ...