A reliable system for automated image understanding can have immense impacts on many applications including image search, video surveillance, autonomous vehicles, and robotics, to name a few. However, the progress of the technology has been slow compared to text understanding and speech recognition. The problem can be attributed to lacking ways of breaking an image into a set of meaningful components analogous to words in text and speech processing. This research will develop an algorithm to partition a digital image into such meaningful components efficiently and effectively. To reach the goal, the investigators focus on discontinuities in color and brightness often called "edges" and study algorithmic ways to group them and delineate objects found in the image. Undergraduate students will actively participate in interdisciplinary research involving computer science, mathematics, statistics, psychology and biology. The PI will also develop an interdisciplinary course that integrates cognitive, neuro, and computer sciences. The resulting source code, software tools, data, and visual materials will be made publicly available to promote STEM education.
More specifically, the study centers on two recent innovations developed by the investigators: semi-group smoothing with a matrix of linear filters, and successive partitioning of a graph with increasing complexity. The former is an affine commutative linear operator that is shown to be effective in extracting high-curvature points while robust against aliasing. It will be used to smoothen, partition, and characterize contour fragments. The latter generates a set of closed contours surrounding a focal point successively from a simple shape to more complex ones. Furthermore, the study investigates new context sensitive perceptual saliency metrics that quantify the importance of edges based on their surroundings.