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

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 (D3T-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.

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