Scale weight selection for feature extraction using complex wavelets: A framework

Abstract

Unmanned Air Vehicles (UAVs) have become an intelligent asset for surveillance, target tracking and reconnaissance in both urban and battlefield settings. This paper gives a framework for scale weight selection during feature extraction in aerial images from UAV. Dual-Tree Complex Waveform technique is used to extract rich feature descriptors of keypoints in images so that full phase and amplitude information can be retained at each scale. The scale weights are dependent on image characteristics such as the illumination and the contrast levels. The outcome of the framework shows promising results in terms of less redundancy of salient features from the images and hence improving the computational speed.