Robust change detection by global-illumination-change compensation and noise-adaptive thresholding

Robustness to noise and/or illumination variations has been a major issue with change detection. We present a novel change detection scheme that is robust to both noise and illumination changes. The major difference of our method compared to the existing ones is that our algorithm globally estimates and compensates for the illumination changes between two input images, whereas the previous approaches rely on some local change metrics for illumination-independent detection. Specifically, a piecewise quadratic model for describing global illumination changes (GICs) is formulated, and an efficient estimation algorithm for the model parameters is proposed. In this way, we can successfully detect any types of local changes, whereas the previous approaches show either limited performances in detecting object interiors or severe performance degradations when global illumination changes occur. We also present a noise-adaptive thresholding method for the GIC-compensated intensity differences. The noise variance is estimated accurately based on symmetricity and decrease of zero-mean noise distributions, and then the decision threshold is selected adaptively to the estimated variance. Experimental results show that the proposed method is very robust to both noise and illumination changes, and outperforms the previous algorithms in various aspects. (C) 2004 Society of Photo-Optical Instrumentation Engineers.