Improving texture optimization with application to visualizing meat products

When inspecting food quality, CT Scanning is among the primary tools used to gain insight. It provides valuable volumetric data using a process, which leaves the product unspoiled and untouched. However, volumetric data is merely a measure of density and therefore contains no appearance information (such as color, translucency, reflective properties). One way of reintroducing this lost information back to the volume data is to synthesize an appropriate texture and apply this to the volume data. A recent method within the field of texture synthesis is called Texture Optimization presented by Kopf et al. in 2007. This method accepts a number of 2D input exemplars, from which it generates a solid texture volume. The volume is iteratively improved via an expectation maximization algorithm. The bottleneck of Texture Optimization occurs during a nearest neighbor search, between texture patches from the 2D input exemplars and the generated texture volume. We examine the current procedures for minimizing the bottleneck and present a novel approach which increases the speed of the synthesis algorithm while minimizing loss of quality. The nearest neighbor search is performed in a high dimensional space. Applying a principal component analysis on the texture patches originating from the synthesized solid accelerates the process. These patches are then reduced in dimensionality until ”only” 95% of their original variance remains. This usually results in a dimension reduction from 192 to about 60-80. The reduction in dimensionality speeds up the convergence of the Texture Optimization method considerably. We examine the impacts of reducing the dimensionality further by tweaking the parameters as well as introducing an alternative method to reducing the dimensionality. Additionally, we study the possibility of selecting only a subsample of the neighborhoods available from the input exemplar without significantly impacting the overall synthesis quality.