Real-time deep hair matting on mobile devices

Augmented reality is an emerging technology in many application domains. Among them is the beauty industry, where live virtual try-on of beauty products is of great importance. In this paper, we address the problem of live hair color augmentation. To achieve this goal, hair needs to be segmented quickly and accurately. We show how a modified MobileNet CNN architecture can be used to segment the hair in real-time. Instead of training this network using large amounts of accurate segmentation data, which is difficult to obtain, we use crowd sourced hair segmentation data. While such data is much simpler to obtain, the segmentations there are noisy and coarse. Despite this, we show how our system can produce accurate and fine-detailed hair mattes, while running at over 30 fps on an iPad Pro tablet.Comment: 7 pages, 7 figures, submitted to CRV 201

Quantitative longitudial analysis of spatially heterogeneous brain atrophy in multiple sclerosis

The importance of axonal loss and tissue atrophy in multiple sclerosis (MS) has become progressively more appreciated. Axonal injury and loss have been recognized both by pathology and magnetic resonance imaging (MRI). It has been shown that tissue injury in MS, including axonal loss, starts at the onset of the disease, even in the absence of any clinical signs. Many lines of evidence link axonal loss and atrophy to chronic disability, and some researchers consider brain atrophy the best correlate of clinical progression. Although axonal loss has been observed both in lesions and in normal appearing brain tissue (NABT), the mechanisms of axonal loss and atrophy have not been yet fully understood. Two main hypotheses are: (1) the primary pathology is related to lesions and the changes in NABT are secondary; or (2) MS is a diffuse, primary degenerative disorder. To address these, a method to detect, quantify, and characterize local brain atrophy in patients with MS has been developed. The method is applied to longitudinal series of brain MRIs that were spatially normalized by means of both linear and elastic non-linear image registration. Non-linear deformation fields were analyzed to evaluate local volume change, per voxel, in overall NABT. Novel distance metrics have been introduced to relate spatially local volume change to white matter (WM) lesions. Regional atrophy in WM fiber tracts were analyzed and related to corresponding regional and global lesion loads. A number of image processing algorithms employed in this thesis was improved, adapted or combined in such a way to comply with specific requirements of applications to MS. The studies performed within this thesis demonstrated that there was a component to atrophy that was related to WM lesions, and possibly resulted secondary to tissue injuries inside lesions, but that it did not explain atrophy in the whole. Evidence of a component to atrophy, not directly linked to WM lesions, was also offered. Together, the results suggest that the whole brain is affected globally, and that atrophy can additionally propagate from WM lesions