Bogdan Matei

TL;DR: In this paper, a multi-sensor, multi-modal data collection, analysis, recognition, and visualization platform can be embodied in a navigation capable vehicle, which provides an automated tool that can integrate multidimensional sensor data including two-dimensional image data, three-dimensional images, and motion, location, or orientation data, and create a visual representation of the integrated sensor data.

TL;DR: A ground segmentation algorithm which can handle both rural regions, and heavily urbanized areas, where the ground is 20% or less of the data, and an algorithm for estimating the orientation of a boundary contour of a building, based on minimizing the number of vertices in a rectilinear approximation to the building outline, which can cope with significant quantization noise in the outline measurements.

TL;DR: It is shown that the HEIV estimator can provide an accurate solution to most 3D vision estimation tasks, and illustrate its performance through two case studies: calibration and the estimation of the fundamental matrix.

TL;DR: A new method for rapid 3D object indexing that combines feature-based methods with coarse alignment-based matching techniques is proposed, achieving a sublinear complexity on the number of models and maintaining at the same time a high degree of performance for real 3D sensed data that is acquired in largely uncontrolled settings.

TL;DR: In this paper, a computer implemented method for detecting the presence of one or more pedestrians in the vicinity of the vehicle is disclosed, where a depth map is derived from the imagery and a plurality of pedestrian candidate regions of interest (ROIs) is detected from the depth map by matching each of the plurality of ROIs with a 3D human shape model.