Image Segmentation using Invariant Texture Features from the Double Dyadic Dual-Tree Complex Wavelet Transform

Abstract

In this paper we propose a new texture segmentation technique that produces segmentation results which more closely match the manual segmentation that would be performed by a human operator. To perform this type of segmentation, we propose a new texture feature based on the double dyadic dual-tree complex wavelet transform (D<sup>3</sup>T-CWT) which provides the ability to analyse a signal at and between dyadic scales. This new texture feature is invariant to shift, rotation and scale and hence can group the texture features in a single object (which may have different sizes and orientations) into a single more meaningful segment. When compared with other texture segmentation approaches, the proposed approach provides segmentation results which more closely match the semantically meaningful objects in the scene.

DOI: 10.1109/ICASSP.2007.365981

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Cite this paper

@article{Lo2007ImageSU, title={Image Segmentation using Invariant Texture Features from the Double Dyadic Dual-Tree Complex Wavelet Transform}, author={Edward H. S. Lo and Mark R. Pickering and Michael R. Frater and John F. Arnold}, journal={2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07}, year={2007}, volume={1}, pages={I-609-I-612} }