Robust Outlier-Object Detection in Image Pairs Based on Variable Threshold Using Empirical Correction Constant
Dong-Sik Kim · Journal of the Institute of Electronics Engineers of Korea · 2009
By calculating the differences between two images, which are captured with the same scene at different time, we can detect a set of outliers, such as occluding objects due to moving vehicles. To reduce the influence from the different intensity properties of the images, a simple technique that reruns the regression, which is based on the polynomial regression model, is employed. For a robust detection of outliers, the image difference is normalized by the noise variance. Hence, an accurate estimate of the noise variance is very important. In this paper, using an empirically obtained correction constant is proposed. Numerical analysis using both synthetic and real images are also shown in this paper to show the robust performance of the detection algorithm.