Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
International Journal of Computer Vision
SIAM Journal on Scientific Computing
An efficient core-area detection algorithm for fast noise-free image query processing
Proceedings of the 2001 ACM symposium on Applied computing
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Identification of Perceptually Important Regions in an Image
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A binary level set model and some applications to Mumford-Shah image segmentation
IEEE Transactions on Image Processing
Minimization of Region-Scalable Fitting Energy for Image Segmentation
IEEE Transactions on Image Processing
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Identifying the Region or Object of Interest in a natural scene is a complex task because the content of natural images consists of the multiple non-uniform sub-regions and the intensity inhomogeneities. In this paper, we present a novel Region of Interest (ROI) detection method to minimize the ROI in the images automatically. We applied the geometric active contours that forces the variational level set function to be close to object boundaries. In addition, the mean-shift algorithm was used to reduce the sensitivity of parameter change in variational level set equation. In order to achieve the experiment, varieties of natural images in different modalities were tested. We compared the efficiency of the proposed method with the method using the human segmentation of the images. In a less complex background, the precision and recall are 92.77% and 88.95%, respectively. In a complex background, the precision and recall are 88.93% and 89.10%, respectively. The experimental results show that our method is imitating human decision making for ROI detection and evaluation.