Using local binary patterns in speckle image analysis
Using local binary patterns in speckle image analysis, Proc IEEE International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO, Opatija, Croatia, Vol. , pp. 314 - 317, May, 2018.
Digital Object Identifier: 10.23919/MIPRO.2018.8400033
Firstly described by Newton in the 17th century, speckle is an optical phenomenon which can be translated into image patterns produced by wave interferences of diffused reflections. In fact, the speckle pattern is generated by the multiple interference phenomena that occur when a rough surface is illuminated with a coherent source of light, producing randomly distributed reflected waves of the same frequency but different phases and amplitudes. Although it has been known for a long time, capturing video sequences of speckle patterns was dependent on recent technological developments, in particular, related to laser technology and microsensors. The speckle acquisition setup comprises a light source, usually a laser, an optical beam expander and a CCD camera. The generated interference patterns are captured in series of video sequences, to further be processed. In previous works, several image processing algorithms have been applied to analyze video frames of speckle, aimed to capture the evolution patterns in dynamic processes. However, due to the typical high frequencies of the changing patterns, classical texture algorithms mostly fail this goal. In this work, speckle dynamics are evaluated using Local Binary Patterns (LBP) jointly with some of its main variants and a newly proposed algorithm, in a reactive hyperemia controlled test. The proposed methodology goes beyond the traditional implementations of LBPs, by considering an additional Gaussian filtering, a methodology thus coined as LBPg. The results, on the one hand, confirm that the classical formulations of LBP are not sensitive to changes in the simulated patterns but, on the other hand, demonstrate that the newly proposed LBP-adapted algorithm successfully identify the dynamics of the processes under study.