In vision-based augmented reality systems, the relation between the real and virtual worlds needs to be estimated to perform the registration of virtual objects. This paper suggests a vision-based registration method for video see-through augmented reality systems using binocular cameras which increases the quality of the registration performed using three points of a known marker. The originality of this work is the use of both monocular vision-based and stereoscopic vision-based techniques in order to complete the registration. Also, a method that performs a correction of the 2D positions in the images of the marker points is proposed. The correction improves the registration stability and accuracy of the system. The stability of the registration obtained with the proposed registration method combined or not with the correction method is compared to the registration obtained with standard stereoscopic registration.
This paper presents a summary of recent research activities carried out at our laboratory in the field of Infrared Thermography for Nondestructive Evaluation (TNDE). First, we explore the latest developments in signal improvement. We describe three approaches: multiple pulse stimulation; the use of Synthetic Data for de-noising of the signal; and a new approach derived from the Fourier diffusion equation called the Differentiated Absolute Contrast method (DAC). Secondly, we examine the advances carried out in inverse solutions. We describe the use of the Wavelet Transform to manage pulsed thermographic data, and we present a summary on Neural Networks for TNDE. Finally, we look at the problem of complex geometry inspection. In this case, due to surface shape, heat variations might be incorrectly identified as flaws. We describe the Shape-from-Heating approach and we propose some potential research avenues to deal with this problem.
In this paper, two neural network approaches are compared for defect detection using thermal evolution, phase and amplitude data acquired in the pulsed thermography approach with pulsed phase thermography processing. The tested approaches are based on Perceptron and Kohonen neural networks. Examples of results are presented for each technique with the three types of available data, in the case of flat-bottom holes in aluminum. Results show that the Perceptron using phase data gives better results being less influenced by disturbances.