A fast approach to deformable surface 3D tracking

Deformable surface 3D tracking is a severely under-constrained problem and great efforts have been made to solve it. A recent state-of-the-art approach solves this problem by formulating it as a second order cone programming (SOCP) problem. However, one drawback of this approach is that it is time-consuming. In this paper, we propose an effective method for 3D deformable surface tracking. First, we formulate the deformable surface tracking problem as a linear programming (LP) problem. Then, we solve the LP problem with an algorithm which converges superlinearly rather than bisection algorithm whose convergence speed is linear. Our experimental studies on synthetic and real data have demonstrated the proposed method can not only reliably recover 3D structures of surfaces but also run faster than the state-of-the-art method.     

Fully deformable 3D digital partition model with topological control

We propose a purely discrete deformable partition model for segmenting 3D images. Its main ability is to maintain the topology of the partition during the minimization process. To do so, our main contribution is a new definition of multi-label simple points (ML simple point) that is easily computable. An ML simple point can be relabeled without modifying the overall topology of the partition. The definition is based on intervoxel properties, and uses the notion of collapse on cubical complexes. This work is an extension of a former restricted definition (Dupas et al., 2009) that prohibits the move of intersections of boundary surfaces. A deformation process is carried out with a greedy energy minimization algorithm. A discrete area estimator is used to approach at best standard regularizers classically used in continuous energy minimizing methods. We illustrate the potential of our approach with the segmentation of 3D medical images with known expected topology.     

Graph-based deformable matching of 3D line with application in protein fitting

We present an algorithm for matching two sets of line segments in 3D that have undergone non-rigid deformations. This problem is motivated by a biology application that seeks a correspondence between the alpha-helices from two proteins, so that matching helices have similar lengths and these can be aligned by some low-distortion deformation. While matching between two feature sets have been extensively studied, particularly for point features, matching line segments has received little attention so far. As typical in point-matching methods, we formulate a graph matching problem and solve it using continuous relaxation. We make two technical contributions. First, we propose a graph construction for undirected line segments such that the optimal matching between two graphs represents an as-rigid-as-possible deformation between the two sets of segments. Second, we propose a novel heuristic for discretizing the continuous solution in graph matching. Our heuristic can be applied to matching problems (such as ours) that are not amenable to certain heuristics, and it produces better solutions than those applicable heuristics. Our method is compared with a state-of-art method motivated by the same biological application and demonstrates improved accuracy.     

Using photo-consistency to register 2D optical images of the humanface to a 3D surface model

The authors propose a novel method to register two or more optical images to a 3D surface model. The potential applications of such a registration method could be in medicine for example, in image guided interventions, surveillance and identification, industrial inspection, or telemanipulation in remote or hostile environments. Registration is performed by optimizing a similarity measure with respect to the transformation parameters. We propose a novel similarity measure based on "photo-consistency." For each surface point, the similarity measure computes how consistent the corresponding optical image information in each view is with a lighting model. The relative pose of the optical images must be known. We validate the system using data from an optical-based surface reconstruction system and surfaces derived from magnetic resonance (MR) images of the human face. We test the accuracy and robustness of the system with respect to the number of video images, video image noise, errors in surface location and area, and complexity of the matched surfaces. We demonstrate the algorithm working on 10 further optical-based reconstructions of the human head and skin surfaces derived from MR images of the heads of five volunteers. Matching four optical images to a surface model produced a 3D error of between 1.45 and 1.59 mm, at a success rate of 100 percent, where the initial misregistration was up to 16 mm or degrees from the registration position     

Fast deformable matching of 3D images over multiscale nested subspaces. Application to atlas-based MRI segmentation

This paper presents a fast method to perform dense deformable matching of 3D images, applied to the registration of inter-subject brain MR images. To recover the complex morphological variations in neuroanatomy, the registration method uses a hierarchy of 3D deformations fields that are estimated, by minimizing a global energy function over a sequence of nested subspaces. The resulting deformable matching method shows low sensitivity to local minima and is able to track large non-linear deformations, with moderate computational load. The performances of the approach are assessed both on simulated 3D transformations and on a real data base of 3D brain MR images from different individuals. An application of the deformable image matching method to 3D atlas-based image segmentation is presented. This atlas-based segmentation is used at Strasbourg Hospital, in daily clinical applications, in order to extract regions of interest from 3D MR images of patients suffering from epilepsy.     

A hierarchical dense deformable model for 3D face reconstruction from skull

3D face reconstruction from skull has been investigated deeply by computer scientists in the past two decades because it is important for identification. The dominant methods construct 3D face from the soft tissue thickness measured at a set of landmarks on skull. The quantity and position of the landmarks are very vital for 3D face reconstruction, but there is no uniform standard for the selection of the landmarks. Additionally, the acquirement of the landmarks on skull is difficult without manual assistance. In this paper, an automatic 3D face reconstruction method based on a hierarchical dense deformable model is proposed. To construct the model, the skull and face samples are acquired by CT scanner and represented as dense triangle mesh. Then a non-rigid dense mesh registration algorithm is presented to align all the samples in point-to-point correspondence. Based on the aligned samples, a global deformable model is constructed, and three local models are constructed from the segmented patches of the eye, nose and mouth. For a given skull, the globe and local deformable models are iteratively matched with it, and the reconstructed facial surface is obtained by fusing the globe and local reconstruction results. To validate the presented method, a measurement in the coefficient domain of a face deformable model is defined. The experimental results indicate that the proposed method has good performance for 3D face reconstruction from skull.     

3D noisy discrete objects: Segmentation and application to smoothing

We propose in this paper a segmentation process that can deal with noisy discrete objects. A flexible approach considering arithmetic discrete planes with a variable width is used to avoid the over-segmentation that might happen when classical segmentation algorithms based on regular discrete planes are used to decompose the surface of the object. A method to choose a seed and different segmentation strategies according to the shape of the surface is also proposed, as well as an application to smooth the border of convex noisy discrete objects.     

A technique to match highly similar 3D objects with an application to biomedical security

Multimedia Tools and Applications - Biometric technologies such as the face, fingerprint, and iris recognition have important utility in biomedical and healthcare applications. The use of...     

An improved region-based model with local statistical features for image segmentation

In this paper, we propose a new region-based active contour model (ACM) for image segmentation. In particular, this model utilizes an improved region fitting term to partition the regions of interests in images depending on the local statistics regarding the intensity and the magnitude of gradient in the neighborhood of a contour. By this improved region fitting term, images with noise, intensity non-uniformity, and low-contrast boundaries can be well segmented. Integrated with the duality theory and the anisotropic diffusion process based on structure tensor, a new regularization term is defined through the duality formulation to penalize the length of active contour. By this new regularization term, the structural information of images is utilized to improve the ability of capturing the geometric features such as corners and cusps. From a numerical point of view, we minimize the energy function of our model by an efficient dual algorithm, which avoids the instability and the non-differentiability of traditional numerical solutions, e.g. the gradient descent method. Experiments on medical and natural images demonstrate the advantages of the proposed model over other segmentation models in terms of both efficiency and accuracy.     

Intraclass retrieval of nonrigid 3D objects: application to face recognition

As the size of the available collections of 3D objects grows, database transactions become essential for their management with the key operation being retrieval (query). Large collections are also precategorized into classes so that a single class contains objects of the same type (e.g., human faces, cars, four-legged animals). It is shown that general object retrieval methods are inadequate for intraclass retrieval tasks. We advocate that such intraclass problems require a specialized method that can exploit the basic class characteristics in order to achieve higher accuracy. A novel 3D object retrieval method is presented which uses a parameterized annotated model of the shape of the class objects, incorporating its main characteristics. The annotated subdivision-based model is fitted onto objects of the class using a deformable model framework, converted to a geometry image and transformed into the wavelet domain. Object retrieval takes place in the wavelet domain. The method does not require user interaction, achieves high accuracy, is efficient for use with large databases, and is suitable for nonrigid object classes. We apply our method to the face recognition domain, one of the most challenging intraclass retrieval tasks. We used the Face Recognition Grand Challenge v2 database, yielding an average verification rate of 95.2 percent at 10-3 false accept rate. The latest results of our work can be found at http://www.cbl.uh.edu/UR8D/.     

Topological Active Volume 3D segmentation model optimized with genetic approaches

The Topological Active Volumes is an active model focused on 3D segmentation tasks. It is based on the 2D Topological Active Nets model and provides information about the surfaces and the inside of the detected objects in the scene. This paper proposes new optimization approaches based on Genetic Algorithms that improve the results of the 3D segmentations and overcome some drawbacks of the model related to parameter tuning or noise conditions. The hybridization of the genetic algorithm with a greedy local search allows the treatment of topological changes in the model, with the possibility of an automatic subdivision of the Topological Active Volume. This combination integrates the advantages of the global and local search procedures in the segmentation process.     

Building 3D surface networks from 2D curve networks with application to anatomical modeling

Constructing 3D surfaces that interpolate 2D curves defined on parallel planes is a fundamental problem in computer graphics with wide applications including modeling anatomical structures. Typically the problem is simplified so that the 2D curves partition each plane into only two materials (e.g., air versus tissue). Here we consider the general problem where each plane is partitioned by a curve network into multiple materials (e.g., air, cortex, cerebellum, etc.). We present a novel method that automatically constructs a surface network from curve networks with arbitrary topology and partitions an arbitrary number of materials. The surface network exactly interpolates the curve network on each plane and is guaranteed to be free of gaps or self-intersections. In addition, our method provides a flexible framework for user interaction so that the surface topology can be modified conveniently when necessary. As an application, we applied the method to build a high-resolution 3D model of the mouse brain from 2D anatomical boundaries defined on 350 tissue sections. The surface network accurately models the partitioning of the brain into 17 abutting anatomical regions with complex topology.     

Multi-rate coupling of physical simulations for haptic interaction with deformable objects

Real-time simulation of deformable objects involves many computational challenges to be solved, particularly in the context of haptic applications, where high update rates are necessary for obtaining a satisfying experience. The required performance can generally be achieved by introducing an intermediate layer responsible for the simulation of the small part of the surface being in contact with the fingers. In this paper, we present an algorithm controlling the run-time of the concurrent simulation threads. It uses information from previous simulation steps to estimate the time spent in the simulation operation considering also changes in the geometry of the intermediate layer. The introduction of such a local contact simulation introduces damping to the overall system. Its effect on the dynamics of the simulation system is experimentally analysed with an interaction test.     

Self-calibrated visual servoing with respect to axial-symmetric 3D objects

A self-calibrated approach to visual servoing with respect to non-planar targets modeled through a pair of coaxial circles plus one point is discussed. Full calibration data (fixed internal parameters) are obtained from two views, and used to recover the Euclidean structure of an auxiliary virtual plane associated to the target, together with the relative pose of the camera. Pose disambiguation is achieved without requiring any real third view of the target. The approach benefits of an off-line planning strategy by which the camera follows a 3D helicoidal path around an arbitrarily chosen axis. A convenient choice for the helicoidal axis is found to be that of the target axis itself. Simulation results demonstrate that the approach is robust with respect to noise both in the off-line and on-line control phases.     

Free-form image registration regularized by a statistical shape model: application to organ segmentation in cervical MR

Deformable registration is prone to errors when it involves large and complex deformations, since the procedure can easily end up in a local minimum. To reduce the number of local minima, and thus the risk of misalignment, regularization terms based on prior knowledge can be incorporated in registration. We propose a regularization term that is based on statistical knowledge of the deformations that are to be expected. A statistical model, trained on the shapes of a set of segmentations, is integrated as a penalty term in a free-form registration framework. For the evaluation of our approach, we perform inter-patient registration of MR images, which were acquired for planning of radiation therapy of cervical cancer. The manual delineations of structures such as the bladder and the clinical target volume are available. For both structures, leave-one-patient-out registration experiments were performed. The propagated atlas segmentations were compared to the manual target segmentations by Dice similarity and Hausdorff distance. Compared with registration without the use of statistical knowledge, the segmentations were significantly improved, by 0.1 in Dice similarity and by 8 mm Hausdorff distance on average for both structures.     

A computational model of perceptual saliency for 3D objects in virtual environments

When giving directions to the location of an object, people typically use other attractive objects as reference, that is, reference objects. With the aim to select proper reference objects, useful for locating a target object within a virtual environment (VE), a computational model to identify perceptual saliency is presented. Based on the object’s features with the major stimulus for the human visual system, three basic features of a 3D object (i.e., color, size, and shape) are individually evaluated and then combined to get a degree of saliency for each 3D object in a virtual scenario. An experiment was conducted to evaluate the extent to which the proposed measure of saliency matches with the people’s subjective perception of saliency; the results showed a good performance of this computational model.     

Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method

A method based on 3D videos is proposed for multi-target segmentation and tracking with a moving viewing system. A spatiotemporal energy functional is built up to perform motion segmentation and estimation simultaneously. To overcome the limitation of the local minimum problem with the level set method, a convex relaxation method is applied to the 3D spatiotemporal segmentation model. The relaxed convex model is independent of the initial condition. A primal-dual algorithm is used to improve computational efficiency. Several indoor experiments show the validity of the proposed method.