Active Contour Model Using Fast Fourier Transformation for Salient Object Detection

The active contour model is a comprehensive research technique used for salient object detection. Most models of saliency detection are developed in the context natural scenes, and their role with synthetic medical images not well investigated. Existing perform efficiently many complexities but facing challenges on due to limited time like, precise automatic fitted expensive initialization computational cost. Our intention detecting boundary without re-initialization which further evolution drive extract object. For this, we propose simple novel derivative numerical solution scheme, using fast Fourier transformation (FFT) (Snake) differential equations that has two major enhancements, namely it completely avoids approximation expansive spatial derivatives finite differences, regularization scheme can be generally extended more. Second, FFT significantly faster compared traditional domain. Finally, this practiced Fourier-force function fit curves naturally objects from background. Compared state-of-the-art methods, proposed method achieves at least 3% increase accuracy three diverse set images. Moreover, runs very fast, average running methods about one twelfth baseline.