In this paper we present a novel image-based 3D surface reconstruction technique that incorporates reflectance, polarisation, and defocus information into a variational framework. Our technique is especially suited for the difficult task of 3D reconstruction of rough metallic surfaces. An error functional composed of several error terms related to the measured reflectance and polarisation prope...

Camera calibration is an important and sensitive step in 3D environment reconstruction by stereovision. Small errors in the estimation of the camera parameters could rise to high errors in the 3D measurements, as the working distance increases. Therefore a method for analyzing the influence of each camera’s parameter error in the accuracy of the 3D measurement is compulsory in order to minimize...

A new method for the unsupervised learning of sparse representations using autoencoders is proposed and implemented by ordering the output of the hidden units by their activation value and progressively reconstructing the input in this order. This can be done efficiently in parallel with the use of cumulative sums and sorting only slightly increasing the computational costs. Minimizing the diff...

Compressive sensing theory has demonstrated that sparse signals can be recovered from a small number of random linear measurements. However, for practical purposes, like storage, transmission, or processing with modern digital equipment, continuous-valued compressive sensing measurements need to be quantized. In this thesis we examine the topic of optimal quantization of compressive sensing mea...

Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Compressed sensing initially adopted [Formula: see text] minimization for signal reconstruction which is computationally expensive. Several greedy recovery algorithms have been recently proposed for signal reconstruction at a lower computational complexity compared to the optimal [Fo...