Baseline vulnerable road user injury risk in multiple U.S. dense urban driving environments.

Objective: Understanding and modeling baseline driving safety risk in dense urban areas represents a crucial starting point for automated driving system (ADS) safety impact analysis. The purpose of this study was to leverage naturalistic vulnerable road user (VRU) collision data to quantify collision rates, crash severity, and injury risk distributions in the absence of objective injury outcome data.

Methods: From over 500 million vehicle miles traveled, a total of 335 collision events involving VRUs were video verified and reconstructed (126 pedestrians, 144 cyclists, and 65 motorcyclists).

Local self similar descriptors: comparison and application to gastroenterology images.

Local descriptors coupled with robust methods for learning visual dictionaries have been a pivotal tool in computer vision. Although the identification of similar patterns is commonly conducted on some stage of the bag-of-words framework, a prior assessment of spatial local similarities can be indicative of specific objects, and thus improved recognition rates. In this work we delve a function of similarity for enhancing the discriminative power of local constrained SIFT descriptors.

Impact of ensemble learning in the assessment of skeletal maturity.

The assessment of the bone age, or skeletal maturity, is an important task in pediatrics that measures the degree of maturation of children's bones. Nowadays, there is no standard clinical procedure for assessing bone age and the most widely used approaches are the Greulich and Pyle and the Tanner and Whitehouse methods. Computer methods have been proposed to automatize the process; however, there is a lack of exploration about how to combine the features of the different parts of the hand, and how to take advantage of ensemble techniques for this purpose.

Real-scale 3D models of the scoliotic spine from biplanar radiography without calibration objects.

This paper presents a new method for modelling the spines of subjects and making accurate 3D measurements using standard radiologic systems without requiring calibration objects. The method makes use of the focal distance and statistical models for estimating the geometrical parameters of the system. A dataset of 32 subjects was used to assess this method.

An evaluation of image descriptors combined with clinical data for breast cancer diagnosis.

Purpose: Breast cancer computer-aided diagnosis (CADx) may utilize image descriptors, demographics, clinical observations, or a combination. CADx performance was compared for several image features, clinical descriptors (e.g.

Interactive 3D reconstruction of the spine from radiographs using a statistical shape model and second-order cone programming.

Three-dimensional models of the spine are commonly used to diagnose, to treat, and to study spinal deformities. Creating these models is however time-consuming and, therefore, expensive. We propose in this paper a reconstruction method that finds the most likely 3D reconstruction given a maximal error bound on a limited set of landmark locations supplied by the user.

Fast 3D reconstruction of the spine from biplanar radiographs using a deformable articulated model.

This paper proposes a novel method for fast 3D reconstructions of the scoliotic spine from two planar radiographs. The method uses a statistical model of the shape of the spine for computing the 3D reconstruction that best matches the user input (about 7 control points per radiograph). In addition, the spine was modelled as an articulated structure to take advantage of the dependencies between adjacent vertebrae in terms of location, orientation and shape.