Computer simulation of metal alloys is an emerging trend in materials development. Simulated replicas of
fabricated alloys are based on the segmentations of alloy micrographs. Therefore, accurate segmentation of visible
precipitates is paramount to simulation accuracy. Since the shape and size of precipitates are key indicators of
physical alloy properties, automated segmentation algorithms must account for abundant prior information of
precipitate shape. We present a new method for constructing a prior enforcing rectangular shape which can be
applied within a min-cut framework for maximum a-posteriori segmentation.
Pose estimation and tracking of articulated objects like humans is particularly difficult due to the complex
occlusions among the articulated parts. Without the benefit of multiple views, resolution of occlusions becomes
both increasingly valuable and challenging. We propose a method for articulated 3D pose estimation from
monocular video which uses nonparametric belief propagation and employs a novel and efficient approach to
occlusion reasoning. We present a human tracking application, and evaluate results using the HumanEva II data
We apply stabilized inverse diffusion equations (SIDEs) to segment microscopy images of materials to aid in
analysis of defects. We extend SIDE segmentation methods and demonstrate the effectiveness of our approaches
to two material analysis tasks. We first develop a method to successfully isolate the textured area of a solidification
defect to pixel accuracy. The second task involves utilizing multiple illuminations of the same structure of a
polycrystalline alloy. Our novel approach features the fusion of data extracted from each of these images to
create a composite segmentation which effectively represents all texture boundaries visible in any of the images.
These two methods both propose new techniques to incorporate multiple images to produce segmentations.