Putting things into context: segmenting photographs based on hand-drawn lines

Abstract: This report presents a method for finding areas of interest in an image, based on lines drawn in that image. The method is designed to work with photographic images of whiteboards, where the information on the whiteboard can be categorized based on the structure of what is drawn on it. Structurally, the method is divided into two main phases. The first phase processes a bitmap image and outputs a set of vectorized features representing strokes of a pen. The second phase filters and categorizes these features and matches them against pre-defined contextual models. The output from the second phase is a set of matching contextual models, each containing a set of area outlines representing contextually important areas of the image. The method proves robust both to variations in the quality of input – such as lighting, angles and signal-to-noise ratio - as well as to the choice of parameters used by the algorithms internally.