DCT-based rotational invariant unsupervised texture segmentation
by T. Ray; P. Patra
International Journal of Instrumentation Technology (IJIT), Vol. 3, No. 1, 2021

Abstract: Texture segmentation mainly focuses on dividing an object into different textures or regions with particular local properties. Usually, unsupervised texture segmentation is more challenging than supervised texture segmentation. In unsupervised texture segmentation, feature-based technique is more effective than model-based methods due to simplicity and fastness. The present paper proposes an unsupervised texture segmentation based on mask-based DCT as a feature-based technique. The proposed features are interesting as compared to plain wavelet and conventional DCT features for textures with and without rotation. Texture image database ranges from Brodatz album to natural scene. The proposed method performs well with the addition of white Gaussian noise. Moreover, post-processing on feature images is not required.

Online publication date: Thu, 26-May-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Instrumentation Technology (IJIT):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com