A Local Feature Descriptor Invariant to Complex Illumination Changes
Abstract: In this paper, we
propose a novel and robust local image descriptor to resolve the problem of complex
illumination variations on low-level feature matching. Many classic local
feature descriptors areinvariant to linear illumination or monotonous intensity
shift, but cannot handle more complex nonlinear illumination changes, which
often occur due to the different time of exposure, the viewing direction, different
types of light-surface interactions and other varying brightness changes. The
presented descriptor that extracts the histograms of oriented gradients and
Uniform Symmetric-Local Binary Pattern (US-LBP) features for each feature point
neighborhood in the scale space. Extensive experiments show that the proposed
descriptor outperforms many state-of-the-art descriptors such as SIFT, ORB,
SURF and FREAK under the problem of local image matching, especially
demonstrating the effectiveness of our method under complex illumination
changes.
Keywords: Harris corner
detector; Illumination invariance; Histograms of oriented gradients; Uniform symmetric-Local
Binary Pattern
Author: Luo Yong, Chen Yuanzhi
Journal Code: jptkomputergg160088
