N.A. Borghese

Different brain correlates for watching real and virtual hand actions.

We investigated whether observation of actions reproduced in three-dimensional virtual reality would engage perceptual and visuomotor brain processes different from those induced by the observation of real hand actions. Participants were asked to passively observe grasping actions of geometrical objects made by a real hand or by hand reconstructions of different quality in 3D virtual reality as well as on a 2D TV screen. We found that only real actions in natural environment activated a visuospatial network including the right posterior parietal cortex. Observation of virtual-reality hand actions engaged prevalent visual perceptual processes within lateral and mesial occipital regions. Thus, only perception of actions in reality maps onto existing action representations, whereas virtual-reality conditions do not access the full motor knowledge available to the central nervous system.

An algorithm for 3-D automatic movement detection by means of standard TV cameras

An algorithm for the computation of 3-D coordinates (space intersection) of marked points on a moving subject surveyed by a pair of TV cameras is presented. It has been designed to meet the requirements of routine analysis in biomechanics laboratories. 3-D geometrical arrangement of the TV cameras (space resection) is obtained by means of a method based on an iterative least-squares estimation, and requires little time for calibration operations; 3-D coordinates are computed by means of a fast geometrical intersection algorithm. The whole algorithm has been extensively used in different laboratories, and its reliability and accuracy are reported.<<ETX>>

Autoscan: a flexible and portable 3D scanner

Quantifying physical abnormalities, guiding corrective and plastic surgery, manufacturing clothing, three-dimensional CAD, and other related fields all benefit from the increasing use of 3D scanners. These scanning systems reconstruct a 3D surface as a large set of polygonal meshes. Although Cyberware scanning systems have become a commercial standard, they have two main drawbacks. First, for large objects they require a mechanical structure that cannot be installed or moved easily. Second, they only allow the scanning of objects within limited size ranges. The paper considers a portable 3D scanning system called Autoscan which provides flexibility, reliability and accuracy for scanning 3D surfaces. Autoscan consists of a pair of video cameras, a real-time image processor and a computer host.

Complete calibration of a stereo photogrammetric system through control points of unknown coordinates

This paper presents a new method for calibrating a video 3D stereo-photogrammetric system. The external parameters and the focal lengths of the cameras are determined from the epipolar constraint and the principal points are computed through the minimisation of a cost function carried out through an evolutionary optimisation. The method has been made more robust with a deterministic annealing procedure of the search region amplitude. Calibration is carried out by moving a rigid bar, carrying two markers on its extremities, inside the working volume. The distance between the two markers is the only measure required. Tests on real data are reported which show that the obtained accuracy is comparable to the one achieved calibrating with control points of known 3D coordinates.

A Hierarchical RBF Online Learning Algorithm for Real-Time 3-D Scanner

In this paper, a novel real-time online network model is presented. It is derived from the hierarchical radial basis function (HRBF) model and it grows by automatically adding units at smaller scales, where the surface details are located, while data points are being collected. Real-time operation is achieved by exploiting the quasi-local nature of the Gaussian units: through the definition of a quad-tree structure to support their receptive field local network reconfiguration can be obtained. The model has been applied to 3-D scanning, where an updated real-time display of the manifold to the operator is fundamental to drive the acquisition procedure itself. Quantitative results are reported, which show that the accuracy achieved is comparable to that of two batch approaches: batch HRBF and support vector machines (SVMs). However, these two approaches are not suitable to real-time online learning. Moreover, proof of convergence is also given.

Hierarchical RBF networks and local parameters estimate

The method presented here is aimed to a direct fast setting of the parameters of a RBF network for function approximation. It is based on a hierarchical gridding of the input space; additional layers of Gaussians at lower scales are added where the residual error is higher. The number of the Gaussians of each layer and their variance are computed from considerations grounded in the linear filtering theory. The weight of each Gaussian is estimated through a maximum a posteriori estimate carried out locally on a sub-set of the data points. The method shows a high accuracy in the reconstruction, it can deal with non-evenly spaced data points and can be fully parallelizable. Results on the reconstruction of both synthetic and real data are presented and discussed. ( 1998 Elsevier Science B.V. All rights reserved.

A portable modular system for automatic acquisition of 3D objects

A modular system which is able to reconstruct the 3D surface of an object is presented here, if has a three level architecture. The first level is devoted to the acquisition of a set of 3D points over the surface (digitisation), the second level constructs the 3D surface in the form of a mesh, filtering the measurement noise. In the third level a bitmap of the surface, obtained from a snapshot, is projected over the 3D mesh to obtain a highly realistic 3D model. This instrument improves the commercial available scanners in two main aspects. The digitiser proves highly flexible and it can easily accommodate objects of different dimension. The construction of the mesh and the filtering of the digitisation noise is carried in a single step through an algorithm which can be parallelised to work in real time. When the spot detection will be transferred to a standard graphic board and the mesh construction over a dedicated (FPGA) board, this instrument shall be seen as a standard input device of next generation graphical workstations.

Enhancing digital cephalic radiography with mixture models and local gamma correction

We present a new algorithm, called the soft-tissue filter, that can make both soft and bone tissue clearly visible in digital cephalic radiographies under a wide range of exposures. It uses a mixture model made up of two Gaussian distributions and one inverted lognormal distribution to analyze the image histogram. The image is clustered in three parts: background, soft tissue, and bone using this model. Improvement in the visibility of both structures is achieved through a local transformation based on gamma correction, stretching, and saturation, which is applied using different parameters for bone and soft-tissue pixels. A processing time of 1 s for 5 Mpixel images allows the filter to operate in real time. Although the default value of the filter parameters is adequate for most images, real-time operation allows adjustment to recover under- and overexposed images or to obtain the best quality subjectively. The filter was extensively clinically tested: quantitative and qualitative results are reported here.

Automatic multiscale meshing through HRBF networks

A procedure for the real-time construction of three-dimensional (3-D) multiscale meshes from not evenly sampled 3-D points is described and discussed in this paper. The process is based on the connectionist model named hierarchical radial basis functions network (HRBF), which has been proved effective in the reconstruction of smooth surfaces from sparse noisy data points. The network goal is to achieve a uniform reconstruction error, equal to measurement error, by stacking noncomplete grids of Gaussians at decreasing scales. It is shown here how the HRBF properties can be used to develop a configuration algorithm, which produces a continuous surface in real time. In addition, the model is extended to automatically convert the continuous surface into a 3-D mesh according to an adequate error measure.

Hierarchical Approach for Multiscale Support Vector Regression

Support vector regression (SVR) is based on a linear combination of displaced replicas of the same function, called a kernel. When the function to be approximated is nonstationary, the single kernel approach may be ineffective, as it is not able to follow the variations in the frequency content in the different regions of the input space. The hierarchical support vector regression (HSVR) model presented here aims to provide a good solution also in these cases. HSVR consists of a set of hierarchical layers, each containing a standard SVR with Gaussian kernel at a given scale. Decreasing the scale layer by layer, details are incorporated inside the regression function. HSVR has been widely applied to noisy synthetic and real datasets and it has shown the ability in denoising the original data, obtaining an effective multiscale reconstruction of better quality than that obtained by standard SVR. Results also compare favorably with multikernel approaches. Furthermore, tuning the SVR configuration parameters is strongly simplified in the HSVR model.