Active Contours in the Complex Domain for Salient Object Detection

Abstract

The combination of active contour models (ACMs) for both contour and salient object detection is an attractive approach for researchers in image segmentation. Existing active contour models fail when improper initialization is performed. We propose a novel active contour model with salience detection in the complex domain to address this issue. First, the input image is converted to the complex domain. The complex transformation gives salience cue. In addition, it is well suited for cyclic objects and it speeds up the iteration of the active contour. During the process, we utilize a low-pass filter that lets the low spatial frequencies pass, while attenuating, or completely blocking, the high spatial frequencies to reduce the random noise connected with favorable or higher frequencies. Furthermore, the model introduces a force function in the complex domain that dynamically shrinks a contour when it is outside of the object of interest and expands it when the contour is inside the object. Comprehensive tests on both synthetic images and natural images show that our proposed algorithm produces accurate salience results that are close to the ground truth. At the same time, it eliminates re-initialization and, thus, reduces the execution time.