Full spherical views provide advantages in many applications that use visual information. Dual back-to-back fisheye cameras are receiving much attention to obtain this type of view. However, obtaining a high-quality full spherical view is very challenging. In this paper, we propose a correction step that models the relation between the pixels of the pair of fisheye images in polar coordinates. This correction is implemented during the mapping from the unit sphere to the fisheye image using the equidistant fisheye projection. The objective is that the projections of the same point in the pair of images have the same position on the unit sphere after the correction. In this way, they will also have the same position on the equirectangular coordinate system. Consequently, the discontinuity between the spherical views for blending is minimized. Throughout the manuscript, we show that the angular polar coordinates of the same scene point in the fisheye images are related by a sine function and the radial distance coordinates by a linear function. Also, we propose employing a polynomial as a geometric transformation between the pair of spherical views during the image alignment since the relationship between the matching points of pairs of spherical views is not linear, especially in the top/bottom regions. Quantitative evaluations demonstrate that using the correction step improves the quality of the full spherical view, i.e. IQ MS-SSIM, up to 7%. Similarly, using a polynomial improves the IQ MS-SSIM up to 6.29% with respect to using an affine matrix.

In this work, we present a simulation software that permits designing and testing several types of controllers based on both classical and modern control theory. It has been created using Easy JavaScript Simulations, since this software permits implementing interactive simulations of physical systems in a quick and intuitive way. This laboratory contains a SISO (Single-Input and Single-Output) and a MIMO (Multiple-Input and Multiple-Output) plant, which are hydraulic and nonlinear, thus the linear model (linearized equations) and the original model (nonlinerized equations) have been implemented. The user can choose any of these physical systems and they have the options to control them using either continuous-time or discrete-time controllers. All parameters of the plant are fully configurable by the user. After that, the controller can be designed and tested. This simulation software offers several configurations: (a) PID (Proportional, Integral and Derivative controller); (b) state feedback; (c) observer and state feedback; and (d) integral controller, observer and state feedback control. The evolution of the controlled system is visualized using an animation of the virtual plant and a graphical representation of the evolution of the most important variables. In this paper, the steps for the implementation of this simulation software are detailed.