A New Adaptive Lifting Scheme Transform for Robust Object Detection

This paper presents a new adaptive lifting scheme transform for detecting user-selected objects in a sequence of images. In our algorithm, we first select a set of object features in the wavelet transform domain and then build an adaptive transform by using the selected features. The adaptive transform is constructed based on adaptive prediction in a lifting scheme procedure. Adaptive prediction is performed such that, the large coefficients in the high-pass component of the non-adaptive transform vanishes in the high-pass component of the adaptive transform. Finally, both the non-adaptive and adaptive transforms are applied to a given test image and the transform domain coefficients are compared for detecting the object of interest. It is shown that the presented algorithm is robust to the noisy environments with reasonable signal-to-noise ratio. We have verified our claims with experimental results on noisy 1-D signals and images