This work discusses an improvement to the boundary tracking algorithm introduced by Chen et al 2011. This method samples points in an image locally and utilizes the CUSUM algorithm to reduce tracking problems due to noise or texture. However, when tracking problems do arise, the local nature of the algorithm does not give any mechanism in which to recover. This work introduces a second CUSUM algorithm to detect off-boundary movement, compensating for such movement by backtracking. Boundary tracking results comparing the two algorithms are presented, including both image data and a numerical comparison of the effectiveness of the algorithms.
In this work, we survey image reconstruction methods for hyperspectral imagery. First, a review of image interpolation methods, both linear and nonlinear, is given. Second, image inpainting methods, especially from the variational perspective, are analyzed with respect to their suitability for hyperspectral inpainting. The ability to connect edges through occlusions and the structure of the space in which the hyperspectral data lies are especially considered when propagating data into unknown regions. Finally, a general method for adapting image reconstruction methods to the hyperspectral case is presented.
In this work, we introduce a method to segment hyperspectral images using a Chan-Vese framework. We utilize a modified l2 distance especially well-suited for hyperspectral classification problems. This distance considers spectral signal shape rather than illumination for the classification of objects. The practicality of multiple phase segmentation in this application is also demonstrated. We then use a high spatial resolution grayscale or color image and a high spectral, but low spatial resolution hyperspectral image to produce a fused segmentation result that is more accurate than segmentation on either image alone. Lastly, we show that the algorithm also gives a natural method for end member selection and apply this result to anomaly detection.