Deformation-based Augmented Reality for Hepatic Surgery

International audienceIn this paper we introduce a method for augmenting the laparoscopic view during hepatic tumor resection. Using augmented reality techniques, vessels, tumors and cutting planes computed from pre-operative data can be overlaid onto the laparoscopic video. Compared to current techniques, which are limited to a rigid registration of the pre-operative liver anatomy with the intra-operative image, we propose a real-time, physics-based, non-rigid registration. The main strength of our approach is that the deformable model can also be used to regularize the data extracted from the computer vision algorithms. We show preliminary results on a video sequence which clearly highlights the interest of using physics-based model for elastic registration

Image-guided Simulation of Heterogeneous Tissue Deformation For Augmented Reality during Hepatic Surgery

International audienceThis paper presents a method for real-time augmentation of vas- cular network and tumors during minimally invasive liver surgery. Internal structures computed from pre-operative CT scans can be overlaid onto the laparoscopic view for surgery guidance. Com- pared to state-of-the-art methods, our method uses a real-time biomechanical model to compute a volumetric displacement field from partial three-dimensional liver surface motion. This permits to properly handle the motion of internal structures even in the case of anisotropic or heterogeneous tissues, as it is the case for the liver and many anatomical structures. Real-time augmentation results are presented on in vivo and ex vivo data and illustrate the benefits of such an approach for minimally invasive surgery

Markerless deformation capture of hoverfly wings using multiple calibrated cameras

This thesis introduces an algorithm for the automated deformation capture of hoverfly wings from multiple camera image sequences. The algorithm is capable of extracting dense surface measurements, without the aid of fiducial markers, over an arbitrary number of wingbeats of hovering flight and requires limited manual initialisation. A novel motion prediction method, called the ‘normalised stroke model’, makes use of the similarity of adjacent wing strokes to predict wing keypoint locations, which are then iteratively refined in a stereo image registration procedure. Outlier removal, wing fitting and further refinement using independently reconstructed boundary points complete the algorithm. It was tested on two hovering data sets, as well as a challenging flight manoeuvre. By comparing the 3-d positions of keypoints extracted from these surfaces with those resulting from manual identification, the accuracy of the algorithm is shown to approach that of a fully manual approach. In particular, half of the algorithm-extracted keypoints were within 0.17mm of manually identified keypoints, approximately equal to the error of the manual identification process. This algorithm is unique among purely image based flapping flight studies in the level of automation it achieves, and its generality would make it applicable to wing tracking of other insects

Rõivaste tekstureerimine kasutades Kinect V2.0

This thesis describes three new garment retexturing methods for FitsMe virtual fitting room applications using data from Microsoft Kinect II RGB-D camera. The first method, which is introduced, is an automatic technique for garment retexturing using a single RGB-D image and infrared information obtained from Kinect II. First, the garment is segmented out from the image using GrabCut or depth segmentation. Then texture domain coordinates are computed for each pixel belonging to the garment using normalized 3D information. Afterwards, shading is applied to the new colors from the texture image. The second method proposed in this work is about 2D to 3D garment retexturing where a segmented garment of a manikin or person is matched to a new source garment and retextured, resulting in augmented images in which the new source garment is transferred to the manikin or person. The problem is divided into garment boundary matching based on point set registration which uses Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. The final contribution of this thesis is by introducing another novel method which is used for increasing the texture quality of a 3D model of a garment, by using the same Kinect frame sequence which was used in the model creation. Firstly, a structured mesh must be created from the 3D model, therefore the 3D model is wrapped to a base model with defined seams and texture map. Afterwards frames are matched to the newly created model and by process of ray casting the color values of the Kinect frames are mapped to the UV map of the 3D model

Dense non-rigid visual tracking with a robust similarity function

International audienceThis paper deals with dense non-rigid visual tracking robust towards global illumination perturbations of the observed scene. The similarity function is based on the sum of condi- tional variance (SCV). With respect to most approaches that minimize the sum of squared differences, which is poorly robust towards illumination variations in the scene, the choice of SCV as our registration function allows the approach to be naturally robust towards global perturbations. Moreover, a thin-plate spline warping function is considered in order to take into account deformations of the observed template. The proposed approach, after being detailed, is tested in nominal conditions and on scenes where light perturbations occur in order to assess the robustness of the approach

Impact of Soft Tissue Heterogeneity on Augmented Reality for Liver Surgery

International audienceThis paper presents a method for real-time augmented reality of internal liver structures during minimally invasive hepatic surgery. Vessels and tumors computed from pre-operative CT scans can be overlaid onto the laparoscopic view for surgery guidance. Compared to current methods, our method is able to locate the in-depth positions of the tumors based on partial three-dimensional liver tissue motion using a real-time biomechanical model. This model permits to properly handle the motion of internal structures even in the case of anisotropic or heterogeneous tissues, as it is the case for the liver and many anatomical structures. Experimentations conducted on phantom liver permits to measure the accuracy of the augmentation while real-time augmentation on in vivo human liver during real surgery shows the benefits of such an approach for minimally invasive surgery

Dense image registration and deformable surface reconstruction in presence of occlusions and minimal texture

Deformable surface tracking from monocular images is well-known to be under-constrained. Occlusions often make the task even more challenging, and can result in failure if the surface is not sufficiently textured. In this work, we explicitly address the problem of 3D reconstruction of poorly textured, occluded surfaces, proposing a framework based on a template-matching approach that scales dense robust features by a relevancy score. Our approach is extensively compared to current methods employing both local feature matching and dense template alignment. We test on standard datasets as well as on a new dataset (that will be made publicly available) of a sparsely textured, occluded surface. Our framework achieves state-of-the-art results for both well and poorly textured, occluded surfaces

Line-constrained camera location estimation in multi-image stereomatching

Stereomatching is an effective way of acquiring dense depth information from a scene when active measurements are not possible. So-called lightfield methods take a snapshot from many camera locations along a defined trajectory (usually uniformly linear or on a regular grid—we will assume a linear trajectory) and use this information to compute accurate depth estimates. However, they require the locations for each of the snapshots to be known: the disparity of an object between images is related to both the distance of the camera to the object and the distance between the camera positions for both images. Existing solutions use sparse feature matching for camera location estimation. In this paper, we propose a novel method that uses dense correspondences to do the same, leveraging an existing depth estimation framework to also yield the camera locations along the line. We illustrate the effectiveness of the proposed technique for camera location estimation both visually for the rectification of epipolar plane images and quantitatively with its effect on the resulting depth estimation. Our proposed approach yields a valid alternative for sparse techniques, while still being executed in a reasonable time on a graphics card due to its highly parallelizable nature