Shadow Detection Using Double-Threshold Pulse Coupled Neural Networks

Abstract

A novel double-threshold pulse coupled neural networks (DTPCNN) is proposed and applied to shadow detection. It attempts to reduce the false detection of shadows in a single image where the hue and brightness of some non-shadow regions are similar to or even lower than those of shadows. Shadows whose intensity and hue fall in between those of the scene and objectives are often viewed as non-shadows by the single dynamic threshold of PCNN. Moreover, entities with similar or darker hue and intensity may be wrongly classified as shadows. To solve this problem, two different dynamic thresholds that iteratively alter are designed. The upper and lower limits of detecting shadows are determined respectively by a higher threshold that decreases iteratively and a lower one that increases iteratively. The detection result is obtained by a fusion of two detection components. Experimental results demonstrate that compared to other tested methods, the misclassifications are significantly reduced and the shadows are more accurately extracted.