Marco Visentini Scarzanella

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The recovery of 3D tissue structure and morphology during robotic assisted surgery is an important step towards accurate deployment of surgical guidance and control techniques in minimally invasive therapies. In this article, we present a novel stereo reconstruction algorithm that propagates disparity information around a set of candidate feature matches.(More)
Despite a wide range of feature detectors developed in the computer vision community over the years, direct application of these techniques to surgical navigation has shown significant difficulties due to the paucity of reliable salient features coupled with free--form tissue deformation and changing visual appearance of surgical scenes. The aim of this(More)
In minimally invasive surgery, dense 3D surface reconstruction is important for surgical navigation and integrating pre- and intra-operative data. Despite recent developments in 3D tissue deformation techniques, their general applicability is limited by specific constraints and underlying assumptions. The need for accurate and robust tissue deformation(More)
The use of physically-based models combined with image constraints for intraoperative guidance is important for surgical procedures that involve large-scale tissue deformation. A biomechanical model of tissue deformation is described in which surface positional constraints and internally generated forces are derived from endoscopic images and preoperative(More)
With increasing demand on intra-operative navigation and motion compensation during robotic assisted minimally invasive surgery, real-time 3D deformation recovery remains a central problem. Currently the majority of existing methods rely on salient features, where the inherent paucity of distinctive landmarks implies either a semi-dense reconstruction or(More)
Despite recent advances in modeling the Shape-from-Shading (SFS) problem and its numerical solution, practical applications have been limited. This is primarily due to the lack of perspective SFS models without the assumption of a light source at the camera centre and the non-metric spatial localisation of the reconstructed shape. In this work, we propose a(More)
Reliable feature tracking is important for accurate tissue deformation recovery, 3D anatomical registration and navigation in computer assisted minimally invasive surgical procedures. Despite a wide range of feature detectors developed in the computer vision community, direct application of these approaches to surgical navigation has shown significant(More)
This paper presents a novel cue for automatic recapture detection of videos. The problem of recapture detection is important to the field of digital forensics as recapture is often an indicator of prior tampering activity. In this paper, we tackle the problem by considering the deformation underwent by geometric primitives, such as straight lines, when(More)
In bronchoschopy, computer vision systems for navigation assistance are an attractive low-cost solution to guide the endoscopist to target peripheral lesions for biopsy and histological analysis. We propose a decoupled deep learning architecture that projects input frames onto the domain of CT renderings, thus allowing offline training from patient-specific(More)
Minimally invasive surgery has been established as an important way forward in surgery for reducing patient trauma and hospitalization with improved prognosis. The introduction of robotic assistance enhances the manual dexterity and accuracy of instrument manipulation. Further development of the field in using pre- and intra-operative imaging guidance(More)