Dorin Comaniciu

TL;DR: A machine learning based vesselness is proposed by exploiting the rich domain specific knowledge embedded in an expert-annotated dataset and outperforms the conventional Hessian vesselness in both speed and accuracy.

TL;DR: A system for video object generation and selective encoding with applications in surveillance, mobile videophones, and the automotive industry, which belongs to a new generation of intelligent vision sensors called smart cameras, which execute autonomous vision tasks and report events and data to a remote base-station.

TL;DR: This paper implements PBN using a graph-structured network that alternates the two tasks of foreground/background discrimination and pose estimation for rejecting negatives as quickly as possible, and gains accuracy in object localization and poses estimation while noticeably reducing the computation.

TL;DR: This paper presents a robust and effective learning-based method for the automatic detection of solid lymph nodes from Computed Tomography data based on Marginal Space Learning and presents an efficient MRF-based segmentation method for solid lymph node detection.

TL;DR: In this paper, a method and system for detecting and classifying coronary stenoses in 3D CT image data is disclosed, where centerlines of coronary vessels are extracted from the CT image and the cross-section area of the lumen is estimated based on the coronary vessel centerlines using a trained regression function.