A Robust Object Shape Prediction Algorithm in the Presence of White Gaussian Noise

摘要

This paper presents a shape prediction algorithm in a noisy video sequence based on pixel representation in the undecimated wavelet domain. In our algorithm for tracking of user-defined shapes in a noisy sequence of images, the amplitude of coefficients in the best basis tree expansion of the undecimated wavelet packet transform are used as feature vectors (FVs). FVs robustness against noise has been achieved through inherent denoising and edge component separation in the best basis selection algorithm. The algorithm uses these FVs to track the pixels of small square blocks located at the vicinity of the object boundary. Searching for the best-matched block has been performed using conventional block matching algorithm in the wavelet domain. Our experimental results show that the algorithm is robust to noise in case of object's shape translation, rotation and/or scaling and can be used to track both rigid and non-rigid shapes in image sequences.