I. Frosio

Autocalibration of MEMS Accelerometers

In this paper, we present a novel procedure for the on-the-field autocalibration of triaxial micro accelerometers, which requires neither any equipment nor a controlled environment and allows increasing the accuracy of this kind of microsensor. The procedure exploits the fact that, in static conditions, the modulus of the accelerometer output vector matches that of the gravity acceleration. The calibration model incorporates the bias and scale factor for each axis and the cross-axis symmetrical factors. The parameters are computed through nonlinear optimization, which is solved in a very short time. The calibration procedure was quantitatively tested by comparing the orientation produced by MEMS with that measured by a motion capture system. Results show that the MEMS output, after the calibration procedure, is far more accurate with respect to the output obtained using factory calibration data and almost one order of magnitude more accurate with respect to using traditional calibration models.

Real-time accurate circle fitting with occlusions

Accurate location of circles inside images is a common problem in many scientific fields. Traditional algorithms, based on fitting a parameterized model, cannot accurately determine the circle in presence of partial occlusions. A novel problem formulation, based on maximum likelihood, allows estimating circles in real-time with sub-pixel accuracy also when occlusions are present.

Autocalibration of Triaxial MEMS Accelerometers With Automatic Sensor Model Selection

Up to now, little attention has been posed on a principled derivation of the cost function used for autocalibration of MEMS tri-axial accelerometers. By formulating the calibration problem in the context of maximum likelihood estimate, we derive here a general formulation that can be reduced to the classical quadratic cost function under certain hypotheses. Moreover, we adopt the Akaike information criterion to automatically choose the most adequate linear sensor model for the given calibration data set. Experiments on simulated and real data show the effectiveness of the proposed approach.

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.

Statistical Based Impulsive Noise Removal in Digital Radiography

A new filter to restore radiographic images corrupted by impulsive noise is proposed. It is based on a switching scheme where all the pulses are first detected and then corrected through a median filter. The pulse detector is based on the hypothesis that the major contribution to image noise is given by the photon counting process, with some pixels corrupted by impulsive noise. Such statistics is described by an adequate mixture model. The filter is also able to reliably estimate the sensor gain. Its operation has been verified on both synthetic and real images; the experimental results demonstrate the superiority of the proposed approach in comparison with more traditional methods.

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.

Real time enhancement of cephalometric radiographies

We present here a real time filtering procedure, called S.Ti.F., which is able to make both soft and bone tissue clearly visible into a cephalometric radiography. Histogram based clustering allows separating the bone pixels from the other ones. Different gamma corrections are then applied to bone and soft tissue areas, in order to obtain a clear visualization of the local anatomical structures. Trials on a large dataset of images demonstrated the power of the proposed filter. The real time processing and the possibility to interactively modify the filter parameters make it a very useful tool for dentists and surgeons

Linear pose estimate from corresponding conics

We propose here a new method to recover the orientation and position of a plane by matching at least three projections of a conic lying on the plane itself. The procedure is based on rearranging the conic projection equations such that the non linear terms are eliminated. It works with any kind of conic and does not require that the shape of the conic is known a-priori. The method was extensively tested using ellipses, but it can also be used for hyperbolas and parabolas. It was further applied to pairs of lines, which can be viewed as a degenerate case of hyperbola, without requiring the correspondence problem to be solved first. Critical configurations and numerical stability have been analyzed through simulations. The accuracy of the proposed algorithm was compared to that of traditional algorithms and of a trinocular vision system using a set of landmarks.

Optimized acquisition geometry for X-ray inspection

X-ray inspection of luggage and cargo containers is nowadays fundamental to guarantee the security in the field of transportation. The simplest inspection systems currently in use are based on transmission radiography, but high absorption objects may hide to the inspector the presence of weapons or other dangerous materials. Dual-energy views are used to automatically recognize nuclear and explosive materials, but also in this case high absorption objects may make the recognition results unreliable. Computerized tomography (CT) partially solves these problems, but it generally represents a costly and slow inspection method. In this paper, we suggest to use a fast, adaptive multi-view system where the geometry of the X-ray projecting cones are set according to an empirical criterion aimed at facilitating the recognition of all the objects inside the scanned volume. Experimental results on simulated and real data are reported in the paper to demonstrate the reasonableness and efficacy of the proposed criterion.

A Neural Network Based Method for Optical Patient Set-up Registration in Breast Radiotherapy

Patient set-up optimization is required in breast-cancer radiotherapy to fill the accuracy gap between personalized treatment planning and uncertainties in the irradiation set-up. Opto-electronic systems allow implementing automatic procedures to minimize the positional mismatches of light-reflecting markers located on the patient surface with respect to a corresponding reference configuration. The same systems are used to detect the position of the irradiated body surface by means of laser spots; patient set-up is then corrected by matching the control points onto a CT based reference model through surface registration algorithms. In this paper, a non-deterministic approach based on Artificial Neural Networks is proposed for the automatic, real-time verification of geometrical set-up of breast irradiation. Unlike iterative surface registration methods, no passive fiducials are used and true real-time performance is obtained. Moreover, the non-deterministic modeling performed by the neural algorithm minimizes sensitivity to intra-fractional and inter-fractional non-rigid motion of the breast. The technique was validated through simulated activities by using reference CT data acquired on four subjects. Results show that the procedure is able to detect and reduce simulated set-up errors and revealed high reliability in patient position correction, even when the surface deformation is included in testing conditions.