Multi-view stereo reconstruction of scenes from satellite images is traditionally performed with a pair-wise stereovision approach: (1) multiple views are grouped into pairs, (2) each pair is processed by two-view stereo methods producing an elevation model or point cloud, lastly (3) the pairwise reconstructions are integrated and filtered to obtain a final result. These steps are organized in a pipeline and the end-to-end performance of reconstructions depends on the behavior of these steps. This work introduces two changes that increase the performance of the reconstructions: a new pair selection approach and a new integration method are presented. The new pair selection replaces commonly used heuristics with a principled criterion that predicts the completeness of a pair based on offline simulations. The presented integration method is based on an iterated bilateral filter. Experiments show that these changes yield a systematic improvement on the performance of the pipeline.

Different methods can be applied to satellite images to derive an altitude map from a set of images. In this article we evaluate a set of representative methods from different approaches. We consider true multi-view stereo methods as well as pair-wise ones, classic methods and deep learning based ones, methods already in use on satellite images and others that were originally devised for close range imaging and are adapted to satellite imagery. While deep learning (DL) methods have taken over multi-view stereo reconstruction in the last years, this tendency has not fully reached satellite stereo pipelines that still largely rely on pair-wise classic algorithms. For the comparison, we set-up a framework that allows to interface a DL-based stereo method taken from the computer vision literature with a satellite stereo pipeline. For multi-view stereo algorithms we build on a recently proposed framework originally devised to apply Colmap method to satellite images. Methods are compared on several datasets that include sets of images taken within a few days and sets of images taken months apart. Results show that DL methods have, in general, a good generalization power. In particular, the use of the GANet DL method as the matching step in a pair-wise stereo pipeline is promising as it already performs better than the classic counterpart, even without a specific training.

Production verification of analog circuit specifications is a challenging task requiring expensive test equipment and time consuming procedures. This paper presents a method for low cost on-chip parameter verification based on the analysis of a digital signature. A 65 nm CMOS on-chip monitor is proposed and validated in practice. The monitor composes two signals (x(t), y(t)) and divides the X-Y plane with nonlinear boundaries in order to generate a digital code for every analog (x, y) location. A digital signature is obtained using the digital code and its time duration. A metric defining a discrepancy factor is used to verify circuit parameters. The method is applied to detect possible deviations in the natural frequency of a Biquad filter. Simulated and experimental results show the possibilities of the proposal.

Automatic ultrasound (US) image segmentation is a difficult task due to the quantity of noise present in the images and the lack of information in several zones produced by the acquisition conditions. In this paper, we propose a method that combines shape priors and image information to achieve this task. In particular, we introduce knowledge about the rib-eye shape using a set of images manually segmented by experts. A method is proposed for the automatic segmentation of new samples in which a closed curve is fitted taking into account both the US image information and the geodesic distance between the evolving curve and the estimated mean rib-eye shape in a shape space. This method can be used to solve similar problems that arise when dealing with US images in other fields. The method was successfully tested over a database composed of 610 US images, for which we have the manual segmentations of two experts.