English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The wavelet transform as an important multi resolution analysis tool has already been commonly applied to texture analysis and classification. Mathematical morphology is very attractive for automatic image segmentation because it efficiently deals with geometrical descriptions such as size, area, shape, or connectivity that can be considered as segmentation-oriented features. This paper presents an image-segmentation system based on some well-known strategies implemented in a different methodology. The segmentation process is divided into three basic steps, namely: texture gradient extraction, marker extraction, and boundary decision. Texture information and its gradient are extracted using the decimated form of a complex wavelet packet transform. A novel marker location algorithm is subsequently used to locate significant homogeneous textured or non textured regions. The goal of boundary decision is to precisely locate the boundary of regions detected by the marker extraction. This decision is based on a region-growing algorithm which is a modified flooding based watershed algorithm.

Image Segmentation using Multiresolution Texture Gradient and Watershed Algorithm