## Materials and Methods
### Data Description: the SURIVSHNO 2019 Challenge Video and Its Critical Issues  
As already introduced, Computer Vision deals with the understanding and interpreting of visual representations such as digital images and videos, as well as other representations which will not be considered in this paper. A video is the electronic medium for recording and displaying a moving visual media, namely a chronographic sequence of photographic shots which forms a representation of the visual world and can capture motion. The representation of the visual characteristics of an object is converted by image sensors into digital signals that can be processed by a computer and made output through a screen as a visible-light image. The 2D digital image is spatially discretized in a number of addressable elements (i.e., the pixels-px) organized in rows and columns to cover the entire image space. The standard full-HD High-Definition Television (HDTV) system uses a resolution of \(1920\times 1080\) px with 16:9 aspect ratio, so that each of the 2,073,600 pixels stores three-channels color information. Trichromacy, in fact, mimics the animal vision which uses three different types of cone cells in the eye to perceive not only light intensity but also its spectral composition (i.e., color). A very common set of primary colors is that defined by the RGB color model, an additive model in which red, green, and blue light are added together to reproduce a broad range of colors. In particular, graphics file formats usually store RGB pictures as 24-bit images (i.e., RGB24 format), where RGB components are 8 bits each, so that each color intensity can be rendered at 256 levels (normalized to unity between 0 and 1 or, more commonly, with integers between 0 and 255), leading to a potential of 16,777,216 (about 16 million) colors. Therefore, in digital imaging systems (e.g., digital cameras, mobile phones, etc.) the acquisition corresponds to an interrogation of each pixel photo-sensor so that the full image is recorded; to produce a video this image recording is repeated in time at a given sampling frequency (commonly 30 fps). Nevertheless, the pixel sensors can be either interrogated simultaneously (i.e., global shutter) or, more frequently in mobile phones, one after the other in a predetermined sequence (i.e., rolling shutter). In the particular case, the SURIVSHNO 2019 video is a sequence of 1298 RGB24 images acquired at 30 fps for a duration of 43.3 s. All the frames are recorded at a full-HD resolution of \(1920\times 1080\) px with 16:9 aspect ratio. The RGB color image depicts a front view of a fan composed by 10 blades, coupled to a spindle by a hexagonal shaft and an 8.8 screw. The first frame of the video is reported in Figure 1.  
The IAS of the fan is unknown, nevertheless, it can be recognized as strongly non-stationary by watching the video. Some typical issues of non-stationary cases, in fact, arise. In particular, spatial aliasing due to the rolling shutter effect of the camera can be easily noticed. As the fan is spinning counterclockwise at an increasing rotational speed, the blades on the left side appear to get thicker while the blades on the right side appear to become thinner as the video progresses in time. This is visualized in Figure 2, in contrast to Figure 1.  
Figure 1: The first frame of the video, digitalized as a \(1080\times 1920\times 3\) matrix: full-HD resolution of \(1920\times 1080\) (16:9 aspect ratio), where each pixel color is represented as a triplet of intensities for the three-color components namely red, green, and blue (RGB additive trichromacy color model). Aliasing is a typical issue of digital signals. A digitally reconstructed image, in fact, will differ from the original image (i.e., analog) because of the spatial discretization (i.e., the sampling) so that visible patterns or deformations can compromise the quality of the reconstruction. Temporal aliasing, determined by the sampling frequency or, in case of videos, by the frame rate of the camera, is a major concern of Digital Signal Processing.