1 September 2010 Textured image segmentation based on modulation models
Author Affiliations +
Optical Engineering, 49(9), 097009 (2010). doi:10.1117/1.3487747
We propose an approach for textured image segmentation based on amplitude-modulation frequency-modulation models. An image is modeled as a set of 2-D nonstationary sinusoids with spatially varying amplitudes and spatially varying frequency vectors. First, the demodulation procedure for the models furnishes a high-dimensional output at each pixel. Then, features including texture contrast, scale, and brightness are elaborately selected based on the high-dimensional output and the image itself. Next, a normalization and weighting scheme for feature combination is presented. Finally, simple K-means clustering is utilized for segmentation. The main characteristic of this work provides a feature vector that strengthens useful information and has fewer dimensionalities simultaneously. The proposed approach is compared with the dominant component analysis (DCA)+K-means algorithm and the DCA+ weighted curve evolution algorithm on three different datasets. The experimental results demonstrate that the proposed approach outperforms the others.
Qingqing Zheng, Nong Sang, Leyuan Liu, Changxin Gao, "Textured image segmentation based on modulation models," Optical Engineering 49(9), 097009 (1 September 2010). https://doi.org/10.1117/1.3487747

Image segmentation

Image processing algorithms and systems


Gaussian filters

Optical engineering

Feature extraction


Back to Top