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→Talk Abstracts Spring 2013
We present an approach to detecting and analyzing the 3D configuration of objects in real-world images with heavy occlusion and clutter. We focus on the application of finding and analyzing cars. We do so with a two-stage framework; the first stage reasons about 2D shape and appearance variation due to within-class variation (station wagons look different than sedans) and changes in viewpoint. Rather than using a view-based model, we describe a compositional representation that models a large number of effective views and shapes using a small number of local view-based templates. We use this model to propose candidate detections and 2D estimates of shape. These estimates are then refined by our second stage, using an explicit 3D model of shape and viewpoint. We use a morphable model to capture 3D within-class variation, and use a weak-perspective camera model to capture viewpoint. We learn all model parameters from 2D annotations. We demonstrate state-of-the-art accuracy for detection, viewpoint estimation, and 3D shape reconstruction on challenging images from the PASCAL VOC 2011 dataset.
We present an approach to detecting and analyzing the 3D configuration of objects in real-world images with heavy occlusion and clutter. We focus on the application of finding and analyzing cars. We do so with a two-stage framework; the first stage reasons about 2D shape and appearance variation due to within-class variation (station wagons look different than sedans) and changes in viewpoint. Rather than using a view-based model, we describe a compositional representation that models a large number of effective views and shapes using a small number of local view-based templates. We use this model to propose candidate detections and 2D estimates of shape. These estimates are then refined by our second stage, using an explicit 3D model of shape and viewpoint. We use a morphable model to capture 3D within-class variation, and use a weak-perspective camera model to capture viewpoint. We learn all model parameters from 2D annotations. We demonstrate state-of-the-art accuracy for detection, viewpoint estimation, and 3D shape reconstruction on challenging images from the PASCAL VOC 2011 dataset.
===Prediction of tissue properties from PET/SPECT cardiac images===
Speaker: [http://www.umiacs.umd.edu/~gibert/ Xavier Gibert Serra] : May 2, 2013
Implantable cardioverter defibrillators (ICDs) deliver shocks in response to left ventricular tachycardia (VT), usually caused by anomalous electrical conduction pathways within scar tissue. About 17,000 patients per year in the U.S. with hemodynamically significant and recurrent VT require radiofrequency ablation. In patients with structural heart disease, electrophysiological (EP) voltage mapping of the endocardial/epicardial surface with a catheter-based system identifies scar areas immediately prior to ablation, which isolates slow conducting channel regions. About half of patients during 6-month follow-up have recurrent
incessant or intermittent VT, thereby indicating a need to improve ablation effectiveness.
Nuclear medicine imaging techniques have shown some success in predicting electrophysiology(EP)-derived tissue properties (scar, border zone, normal) from PET/SPECT cardiac images to aid EP ablation procedures for left ventricular tachycardia (VT). However, current procedures are based on subjective evaluations by human analysts and are prone to error. Existing medical image processing techniques are insufficient to provide reliable predictions due to limitations in image resolution, presence of outliers, and lack of an closed-form relation betwen PET and EP. In collaboration with the University of Maryland Medical Center in Baltimore, we are addressing the following problems:
1) Integrated visualization of 3-D PET/SPECT cardiac images and discretely sampled EP voltage measurements.
2) Automated registration of cardiac PET/SPECT images with EP voltage data.
3) Robust sparse regression methods with outlier rejection.
==Past Semesters==
==Past Semesters==