Terrell N. Mundhenk

Manifold-based fingerprinting for target identification

In this paper, we propose a fingerprint analysis algorithm based on using product manifolds to create robust signatures for individual targets in motion imagery. The purpose of target fingerprinting is to reidentify a target after it disappears and then reappears due to occlusions or out of camera view and to track targets persistently under camera handoff situations. The proposed method is statistics-based and has the benefit of being compact and invariant to viewpoint, rotation, and scaling. Moreover, it is a general framework and does not assume a particular type of objects to be identified. For improved robustness, we also propose a method to detect outliers of a statistical manifold formed from the training data of individual targets. Our experiments show that the proposed framework is more accurate in target reidentification than single-instance signatures and patch-based methods.

High Precision Object Segmentation and Tracking for use in Super Resolution Video Reconstruction

Super resolution image reconstruction [1] is a potentially useful tool. It should allow recovery of fine image detail at higher resolutions for frames in a video. In general, what this means is that we can take images in a video and recover a higher resolution set of images than the original movie was filmed in. This is possible because we can recover extra information about a frame of video by extrapolating hidden values from other frames in the video.