TY  - JOUR
T1  - Image Segmentation using Modified Region-Based Active Contour Model
AU - Rajendran, Parvathy AU - Qun Wong, Ooi 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 16
SP  - 5710
EP  - 5718
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.5710.5718
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.5710.5718
KW  - fitting term
KW  -active contour
KW  -region-based method
KW  -segmentation
KW  -Object detection
KW  -level set equation
AB  - Image segmentation using active contour models to improve image processing enhances object
detection. Various segmentation methods have been proposed over in the past to improve the accuracy of
segmentation results such as clustering, edge-based, region-based, template matching and hybrid methods.
However, the image segmentation results of these methods are not ideal. Therefore, a small improvement in the
results will have a huge impact on image processing, particularly for autonomous unmanned aircraft application.
Recently, the Chan-Vese Model, a region-based method that uses active contour models, gained considerable
research attention because of its improved image segmentation capability. This study presents a model that
enhances the Chan-Vese algorithm model. The main idea of the proposed method is to reduce the computational
time in image segmentation without affecting the segmentation result. Fitting term is defined as constant in the
proposed model and the level set equation of the main domain continues to evolve the curve toward the
boundary of the object. A total of 467 images from the Berkeley segmentation database are used to test the
proposed method and analyze its performance. Results indicate that the proposed model achieves better
segmentation result with low computational time compared with existing image segmentation methods
ER  -