Knowledge-based hierarchical region-of-interest detection

Detecting regions of interest (ROIs) in a complex image is a critical step in many image processing applications. In this paper, we present a new algorithm that addresses several challenges in ROI detection. The novelty of our algorithm includes: (i) every ROI contains one and only one object; (ii) the detected ROIs can have irregular shapes as opposed to the rectangular shapes that are typical of other algorithms; (iii) the algorithm is applicable to images that contain connected objects, or when the objects are broken into pieces; (iv) the algorithm is not sensitive to contrast levels in the image, and is robust to noise. These characteristics make the proposed algorithm applicable to low-resolution, real-world imagery without costly post-processing. The proposed algorithm is shown to provide outstanding performance with low-quality imagery, and is shown to be fast and robust.