3D Surface Reconstruction Using Infrared (IR) Signal

Three-Dimension (3D) surface reconstruction from object using an infrared sensor is becoming one of the widely used in research today. In this project data collected from infrared sensor will be used to reconstruct a 3D surface of an object. The sensor value data need to be converted into distance between infrared sensor to the surface of an object. One-Dimensional (1D) to two-dimensional (2D) conversion is carried out by using the angle between collection of each data and the distance between sensor and an object. The method to be used to reconstruct a 2D image by using conversion between Polar (angle-distance-axis) to Cartesian coordinate (x-yaxis) plane. The 2D images that were obtained will be used to reconstruct threedimensional (3D) surface using a mesh grid algorithm and by acquiring the z-axis of the object

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Denoising infrared structured light DIBR signals using 3D morphological operators

The advent of low cost infrared structured light cameras (such as MS Xbox Kinect sensors) has brought the need for effective denoising algorithms that improves the quality and the accuracy of the reconstructed 3D scene. The overall noise depends on diverse factors and leads to significant alterations on object borders in the depth signal. It is possible to exploit a color signal acquired by a standard RGB camera to correct these alterations and interpolate the missing values. The paper presents a denoising and interpolation strategy that adopts 3D morphological operators to smooth and regularize volumes generated from the structured light cameras. Experimental results show that both the number of valid points and the quality of the warped views improve with respect to other regularizing approaches. © 2012 IEEE