MATLAB is the best software toolbox to use in this course, since it is mature software product and the course material has extensive examples how to use MATLAB for various purposes. Also, the MATLAB scripting language provides high level coding efficient ways to complete tasks. On the flip side, MATLAB is proprietary and expensive package to have. There are only 5 online licenses of it in the computer science department up to my knowledge. Secondly, coding practices might seem cumbersome and restricting. For more details see the homepage
Scilab is a freeware analog of MATLAB. The main advantage is that it is free. Up to my knowledge the powers of MATLAB and Scilab are comparable in most cases. It also has a image processing toolkit. However, it is in orphaned state. A video processing toolbox on the other hand seems to be live and kicking. Both toolboxes seems to covder basic functionality needed for the course. For the details see: Scilab homepage, Scilab Image Processing Toolbox, Scilab Image and Video Processing Toolbox
Gnu R is an environment for statistical and numerical computation. Although the main emphasis of GNU R is in statistics it also provides image processing functions. Similarly to Scilab, the GNU R software is freeware . However, there are no explicit advantages over MATLAB and Scilab. Except, those who have programmed in R do not have to learn an new language. For further details consult the GNU R homepage, EBImage, biOps package, rimage, dcemri.
Standard C++ library for image processing that covers all necessary operations discussed in the course. The resulting code is efficient and compact. However, you loose in development time, as C++ is quite verbose. Of course, you can use bindings to more novice friendly languages. For further details. consult the OpenCV homepage.