Rotation Angle Estimation Algorithms for Textures and Their Implementations on Real Time Systems

In this chapter, rotation angle estimation algorithms for textures and their real time implementations on a custom smart camera called FU-SmartCam is introduced (Ulas et al., 2007) and improved. In the textile industry, weft-straightening is a fundamental problem which is closely related to the rotation angle estimation. Earlier weft-straightening machines used simple sensors and hardware; however, with the increased complexity of fabric types and demand for faster and more accurate machines, the whole industry started to switch to smart camera systems. Three basic methods, which are based on FGT constellation, polar transformation, and statistical features, are proposed and their performances are evaluated. As an improvement to statistical based method, we introduce a neural network based approach to choose optimum weights for the statistical features. Moreover, a comparison between FU-SmartCam and a commercial one called Tattile Smart Camera is given. Experimental results show that the introduced algorithms provide satisfactory performance, and can be used in real time systems.