Abstract:

Computer Vision is a trending field nowadays, as evidenced by its application in cutting-edge technologies like the autonomous navigation of vehicles or robots, the modelling and/or identification of objects and environments, or the registration of images, to name a few. In fact, computer vision techniques are present in our daily life each time that we take a panoramic picture, or we apply funny effects to our face through a video application running in our smartphones. Moreover, our faces are also the main characters when these techniques seek for suspicious people in crowded places like shopping centres or airports. The interest on this field can be also noticed in the number of courses addressing it in online platforms like Coursera [1], or in Bachelors and Masters studies.

A software tool for visually illustrating the principles behind typical computer vision techniques would be a resource of significant interest for lecturers teaching those courses. Usually, computer vision algorithms consist of a pipeline of techniques applied to an image in order to retrieve the desired information. Although there exist many tools implementing these techniques, most of them focus on providing a computational efficient solution, like OpenCV [2], hence being necessary a certain level of understanding of low-level details and therefore not optimal for teaching purposes. On the other hand, there are also tools focused on illustrating the results of those techniques, for example showing the edges detected in an image (e.g. [3]), but they lack in providing intermediate information that illustrates what it is underneath. The ideal tool would be a trade-off between these two common options.

In this paper we present the mVision toolkit, a set of Graphical User Interfaces (GUIs) implemented in MATLAB to show the performance and relevant details behind most common computer vision techniques, including: segmentation, edge detection, object recognition, etc. In this way, each GUI offers the possibility of executing and configuring a number of algorithms pursuing the same goal, e.g. detecting edges in an image, also showing illustrative intermediate information that assists the lecturer in his/her explanations and helps the student to understand the details at a glance. The development of the toolkit started in 2009, and it is constantly updated according to the experience acquired in our courses. Recently we have made mVision public under the GNUv3 license for the lecturers’ community at https://github.com/jotaraul/mVision, and we also welcome any contribution from it.

References:
[1] Cousera webpage. https://www.coursera.org/ [Accessed online Nov’2017]
[2] Itseez. Open Source Computer Vision Library. At https://github.com/itseez/opencv [Accessed online Nov’2017].
[3] Pinetools edges detection webpage: http://pinetools.com/image-edge-detection [Accessed online Nov’2017].

Keywords:

mVision, MATLAB toolbox, computer vision.