Andere Kunden interessierten sich auch für
![Investigation of Arabic Handwriting Recognition Based on Segmentation]( "Investigation of Arabic Handwriting Recognition Based on Segmentation")
Investigation of Arabic Handwriting Recognition Based on Segmentation58,99 €![A Complete Guide to Image Segmentation With Matlab Codes]( "A Complete Guide to Image Segmentation With Matlab Codes")
A Complete Guide to Image Segmentation With Matlab Codes40,99 €![Accurate Segmentation of Iris Image]( "Accurate Segmentation of Iris Image")
Accurate Segmentation of Iris Image39,99 €![XP challenges investigation in GSD]( "XP challenges investigation in GSD")
XP challenges investigation in GSD26,99 €![Satellite Image Classification Using K-Means and SVD Techniques]( "Satellite Image Classification Using K-Means and SVD Techniques")
Satellite Image Classification Using K-Means and SVD Techniques32,99 €![Integrated Geophysical and Electrical Depth Slicing Investigation]( "Integrated Geophysical and Electrical Depth Slicing Investigation")
Integrated Geophysical and Electrical Depth Slicing Investigation46,99 €![An Investigation into Voluntary Corporate Risk Disclosure by Firms]( "An Investigation into Voluntary Corporate Risk Disclosure by Firms")
An Investigation into Voluntary Corporate Risk Disclosure by Firms36,99 €
Image segmentation is the process of dividing an image into meaningful objects. Fuzzy connectedness (FC) based methods usually give robust segmentation results. However, most of these methods suffer from the weakness of "leaking through poorly defined boundary segments". One of these methods is the absolute or generalized fuzzy connectivity (GFC) method which has an additional weakness about determining the optimal threshold of the fuzzy connectedness scene. This threshold is usually selected manually rather than being automated. In this proposal, we introduce an algorithm based on GFC method that utilizes both fuzzy connectedness- and boundary-based information inheriting the strength qualities of them both, since they depend on different characteristics, in order to alleviate the previous weaknesses. This algorithm uses an affinity of FC that utilizes both region and boundary-based information. Moreover, it proposes an automatic selection procedure of the threshold parameter. An experiment is performed to quantitatively evaluate the proposed algorithm over a simulated data set of scenes.