Contrast-agnostic groupwise registration by robust PCA for quantitative cardiac MRI

Quantitative cardiac MRI is an increasingly important diagnostic tool for cardiovascular diseases. Yet, it is essential to have correct image registration for good accuracy and precision of quantitative mapping. Registering all baseline images from a quantitative cardiac MRI sequence, however, is nontrivial because the patient is moving, leading...

Investigation of pointnet for semantic segmentation of large-scale outdoor point clouds

Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have credibility for capturing geometry of objects including shape, size, and orientation. Deep learning (DL) has been recognized as the most successful approach for image semantic segmentation. Applied to point clouds, performance of the many DL...

Model-to-Image Registration via Deep Learning towards Image-Guided Endovascular Interventions

Cardiologists highlight the need for an intra-operative 3D visualization to assist interventions. The intra-operative 2D X-ray/Digital Subtraction Angiography (DSA) images in the standard clinical workflow limit cardiologists’ views significantly. Compared with image-to-image registration, model-to-image registration is an essential approach...

SCIPoC: Semantic Classification of Indoor Point Cloud: A study into the possibilities of classifying indoor point cloud using a Deep Learning approach

With the constantly evolving range of applications for technology the quality and amount of data constantly increases as well. In this growing data environment, there is a constant search to provide more value to all data that is available for as little effort as possible. Our research tries to add such additional value by diving into the...

Practical Approaches towards Complete Real-time Gaze Tracking

Visual context plays a key role in many computer vision tasks, and performance of eye/gaze-tracking methods also benefit from it. However, the size of contextual information (e.g. full face image) is very large w.r.t the primary input i.e. cropped image of the eye. This adds large computational costs to the algorithm and makes it inefficient,...