OpenCV Vs MATLAB: Which Is Best For Successful Computer Vision Project?

A variety of languages exist to code for specific or generic tasks when it comes to AI or computer vision. Each of them serves different, sometimes the same but more efficient functions. The question arises here – Which is the best to use? Which one among all serves a better purpose?

To analyze what suits your program better, you must understand, co-relate and compare them to filter the best for you.

As computer vision has become one of the significant fields across the technology industry, one can expect tons of jobs to come up in the next couple of years. In an effort to get ready for such an opportunity you must know the advantages and exclusiveness of significant languages used for computer vision.
OpenCV and MATLAB are amongst the most common and most utilized languages. They stand out in terms of speed and efficiency.

Let's explore further.

Features of OpenCV

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library which was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.

•  The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms.

•  Such algorithms can be utilized to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high-resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc.

•  The library has more than 47 thousand people of the user community and the estimated number of downloads exceeding 18 million.

•  OpenCV is extensively used in companies, research groups, and governmental bodies.

•  Well-established companies like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota employ this library.

•  Moreover, significant startups like Applied Minds, VideoSurf, and Zeitera make extensive use of OpenCV.

•  OpenCV has C++, Python, Java and MATLAB interfaces and supports Windows, Linux, Android, and Mac OS.

•  It leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available.

•  OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.

Features of MATLAB

MATLAB is a programming language that combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook.

•  MATLAB helps you gain insight into your image and video data, develop algorithms, and explore implementation tradeoffs.

•  User can design vision solutions with a comprehensive set of reference-standard algorithms for image processing, computer vision, and deep learning.

•  One can collaborate with teams using OpenCV, Python, and C/C++ using interoperable APIs and integration tools.

•  It helps you accelerate algorithms on NVIDIA GPUs, cloud, and data center resources without specialized programming or IT knowledge.

•  MATLAB apps can be used to explore your data interactively and automatically generate MATLAB code. This means you don't have to code from scratch.

•  It can be integrated directly with open source. You can reuse legacy code written in another programming language, create MATLAB powered responsive web sites, or program hardware using error-free embedded C-code generated directly from MATLAB.

•  Using MATLAB, the user is allowed to work with C/C++ and HDL code and run image processing algorithms on PC hardware, FPGAs, and ASICs, and develop imaging systems.

•  Users can use the generated CUDA within MATLAB to accelerate computationally intensive portions of his MATLAB code.

Comparison: OpenCV Vs MATLAB

Well, MATLAB is more convenient in developing and data presentation, however, OpenCV is much faster in execution. In the case of OpenCV, the speed ratio reaches more than 80 in some cases.

However, OpenCV is comparatively harder to learn due to a lack of documentation and error handling codes. This disadvantage is what makes novice computer vision users lean towards MATLAB more often. But once gained expertise with OpenCV, some professionals suggest sticking with it as it is the most comprehensive open-source library for computer vision and has a large user community.

Some professionals also suggest MATLAB as it is useful for rapid prototyping and its code is very easy to debug. Moreover, it has good documentation and support.

The disadvantage which surrounds MATLAB is it is not open source and the license is pretty pricey, and its programs are not portable. However, MATLAB is a full scientific suite that consists of a massive IDE with its own language.

Therefore, it is quite clear that MATLAB is suitable for exploring computer vision concepts as researchers and students at universities that can afford the software but OpenCV comes handier while building production-ready real-world computer vision projects.