A Quaternion Framework for Color Image Smoothing and Segmentation

Quaternion color texture segmentation

The quaternion representation of color is shown here to be effective in the context of segmenting color images into regions of similar color texture. The advantage of using quaternion arithmetic is that a color can be represented and analyzed as a single entity. A lowdimensional basis for the color textures found in a given image is derived via quaternion principal component analysis (QPCA) of ...

Unsupervised Texture Image Segmentation Using MRFEM Framework

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...

Unsupervised Texture Image Segmentation Using MRFEM Framework

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...

Quaternion K-SVD for Color Image Denoising

Color image segmentation and parameter estimation in a markovian framework

An unsupervised color image segmentation algorithm is presented, using a Markov random ®eld (MRF) pixel classi®cation model. We propose a new method to estimate initial mean vectors e€ectively even if the histogram does not have clearly distinguishable peaks. The only parameter supplied by the user is the number of classes. Ó 2001 Elsevier Science B.V. All rights reserved.