Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New
computational tools are needed to search and organize these data. We present NBLAST, a …

n this work we propose a novel Hough voting approach for the detection of free-form shapes
in a 3D space, to be used for object recognition tasks in 3D scenes with a significant degree …

In this letter, we present an approach for automatic liver segmentation from computed
tomography (CT) scans that is based on a statistical shape model (SSM) integrated with an …

3D models of indoor environments are increasingly gaining importance due to the wide
range of applications to which they can be subjected: from redesign and visualization to …

The work presented in this paper goals to develop a system for automatic translation of static
gestures of alphabets in American Sign Language. In doing so three feature extraction …

We present an algorithm for automatic segmentation of the human pelvic bones from CT
datasets that is based on the application of a statistical shape model. The proposed method …

A major challenge in the application of state-of-the-art deep learning methods to the
classification of mobile lidar data is the lack of sufficient training samples for different object …

In this paper, a fully automatic method is proposed to segment the kidney into multiple
components: renal cortex, renal column, renal medulla and renal pelvis, in clinical 3D CT …

The exact localization of the mandibular nerve with respect to the bone is important for
applications in dental implantology and maxillofacial surgery. Cone beam computed …

In this paper we propose a framework for fully automatic, robust and accurate segmentation
of the human pelvis and proximal femur in CT data. We propose a composite statistical …