Small object detection in cluttered image using a correlation based active contour model

This paper presents a novel energy function for active contour models based on autocorrelation function, which is capable of detecting small objects against a cluttered background. In the proposed method, image features are calculated using a combination of short-term autocorrelations (STA) computed from the image pixels to represent region information. The obtained features are exploited to define an energy function for the localized region-based active contour model called normalized accumulated short-term autocorrelation (NASTA). Minimizing this energy function, we can accurately detect small objects in images containing cluttered and textured backgrounds. Moreover, the proposed method provides high robustness against random noise and can precisely locate small objects in noisy backgrounds, difficult to be detected with naked eye. Experimental results indicate remarkable advantages of our approach comparing to existing methods.

► A novel energy function for active contour models based on autocorrelation function is presented.
► A new feature image called normalized accumulated short-term autocorrelation (NASTA) is introduced.
► The NASTA is able to suppress clutter and noise.
► The proposed model can be used for small object detection in a cluttered and noisy image.