The segmentation of images into meaningful and homogenous regions is a key method for image analysis within applications such as content based retrieval. The watershed transform is a well established tool for the segmentation of images. However, watershed segmentation is often not effective for textured image regions that are perceptually homogeneous. In order to segment such regions properly, the concept of the "texture gradient" is introduced. Texture information and its gradient are extracted using a novel nondecimated form of a complex wavelet transform. A novel marker location algorithm is subsequently used to locate significant homogeneous textured or non textured regions. A marker driven watershed transform is then used to segment the identified regions properly. The combined algorithm produces effective texture and intensity based segmentation for application to content based image retrieval.
|Translated title of the contribution||Image segmentation using a texture gradient based watershed transform|
|Pages (from-to)||1618 - 1633|
|Number of pages||16|
|Journal||IEEE Transactions on Image Processing|
|Publication status||Published - Dec 2003|
Bibliographical notePublisher: Institute of Electrical and Electronics Engineers (IEEE)
Rose publication type: Journal article
Sponsorship: The authors would like to acknowledge the help of N. Kingsbury of the University of Cambridge for providing the Matlab code for the DT-CWT
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- image segmentation
- image texture analysis
- image edge analysis
- wavelet transforms