Michal Sofka

TL;DR: A new likelihood ratio test that combines matched-filter responses, confidence measures and vessel boundary measures is presented, embedded into a vessel tracing framework, resulting in an efficient and effective vessel centerline extraction algorithm.

TL;DR: An automated 2D-image-pair registration algorithm capable of aligning images taken of a wide variety of natural and man-made scenes as well as many medical images and substantially out-performs algorithms based on keypoint matching alone is proposed.

TL;DR: The proposed classification system was deployed on large corporate networks, where it detected 2,090 new and unseen variants of malware samples with 90% precision, which is a considerable improvement when compared to the current flow-based approaches or existing signature-based web security devices.

TL;DR: This paper modifications widely-used keypoint descriptors such as SIFT and shape contexts, attempting to capture the insight that some structural information is indeed preserved between images despite dramatic appearance changes, and shows that indexing based on modified descriptors produces more correct matches on difficult pairs than current techniques at the cost of a small decrease in performance on easier pairs.

TL;DR: A novel generic segmentation system for the fully automatic multi-organ segmentation from CT medical images that combines the advantages of learning-based approaches on point cloud-based shape representation with those of PDE-optimization-based level set approaches, such as high accuracy and the straightforward prevention of segment overlaps.