Matlab is a popular high-level tool used in technical computing language and interactive environment including here computer vision and image processing. Even it has a free and powerful alternative like OpenCV, it comes with a set of features that allow users to quickly develop and debugging applications. A series of tutorials from where you can learn how to use Matlab in computer vision and image processing as well as the advantages of Matlab makes the subject of this article.
OpenCV is a free alternative for Matlab and has high performances compared with this. Built for use in C/C++ programming language, OpenCV is highly customizable and is designed for fast processing of a large number of images in short time.
Why to use MATLAB?
1. Fast development
- Fast and good programming with fewer bugs compared with OpenCV since a wide range of functions are available and has support for displaying and manipulate data. Fast coding is a positive side of Matlab that allows you to develop quickly vision applications, but it is slower at execution time, which is a disadvantage point.
2. Fast debugging
- Matlab doesn’t have specific programming problems like memory allocation and it can stop automatically the script when encountered a problem. Also, it allows users to execute code using command lines even an error occurs and fix the error while the code is still in execution mode. Due to the fact that Matlab can execute code during debugging is an advantage compared with other IDE tools.
3. Clear code
- Matlab has a concise code that makes easier to write code, understand, and for debugging.
- Matlab has a comprehensive documentation with a lot of examples and explanations.
Below can be found a series of guides, tutorials, and examples from where you can teach different methods to detect and track objects using Matlab as well as a series of practical example where Matlab automatically is used for real-time detection and tracking.
- Image Processing with MATLAB 1 – simple example that shows you how objects from an image can be detected using particular shapes;
- Tracking in Practice: Student Guide – a comprehensive guide that teaches you how to install the tracking framework and create a practical example for detection and tracking an object;
- Introduction to Implementing Matlab in Computer Vision – in this file are available a series of examples from where you can learn how Matlab can be used for computer vision in different areas;
- Motion-Based Multiple Object Tracking – advanced example how Matlab is used or automatic detection and tracking moving objects from video images;
- Tracking Objects: Acquiring And Analyzing Image Sequences In MATLAB – another example where for object tracking technique is used the Image Processing Toolbox;
- Motion Tracking in Image Sequences – in this guide are available two examples how to detect and track objects by identifying objects at different points in time;
- Object Tracking – comprehensive introduction that teaches you how the Kalman Filter algorithm is applied in Matlab to track objects;
- Object tracking using a Kalman filter (MATLAB) – another tutorial that teaches you how to use the Kalman Filter algorithm in order to track a face in video images;
- Object Detection and Tracking – in this example is presented in detail how to detect a particular object from an image by finding a reference to a target image;
- Moving Object detection and tracking using image subtraction – simple guide where can be found an algorithm implemented for detection and tracking objects using image subtraction process;
- Stereo Vision – comprehensive guides how Matlab can be used for detecting and tracking different objects captured with stereo cameras;
1. Matlab vs Aforge vs OpenCV, stackoverflow.com;
2. MATLAB and Octave Functions for Computer Vision and Image Processing, csse.uwa.edu.au;
3. Using MATLAB with OpenCV, mathworks.ch;
4. Why is Matlab so popular in the computer vision community even with OpenCV being so complete?, stackoverflow.com