Scientific Computing relies on executing computer algorithms coded in different programming languages. One such interdisciplinary scientific field is the study of Computer Vision, often abbreviated as CV. Computer Vision is used to develop techniques that can automate tasks like acquiring, processing, analyzing and understanding digital images. It is also utilized for extracting high-dimensional data from the real world to produce symbolic information. In simple words, Computer Vision gives computers the ability to see, understand and process images and videos like humans.
The vast advances in hardware, machine learning tools, and frameworks have resulted in the implementation of Computer Vision in various fields like IoT, manufacturing, healthcare, security, etc. Major tech firms like Amazon, Google, Microsoft, and Facebook are investing immensely in the research and development of this field.
Out of the many tools and libraries available for Computer Vision nowadays, there are two major tools OpenCV and Matlab that stand out in terms of their speed and efficiency. In this article, we will have a detailed look at both of them.
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OpenCV: An open-source multiplatform solution tailored for Computer Vision
OpenCV, developed by Intel and now supported by Willow Garage, is released under the BSD 3-Clause license and is free for commercial use. It is one of the most popular computer vision tools aimed at providing a well-optimized, well tested, and open-source (C++)-based implementation for computer vision algorithms. The open-source library has interfaces for multiple languages like C++, Python, and Java and supports Linux, macOS, Windows, iOS, and Android. Many of its functions are implemented on GPU.
The first stable release of OpenCV version 1.0 was in the year 2006. The OpenCV community has grown rapidly ever since and with its latest release, OpenCV version 4.1.1, it also brings improvements in the dnn (Deep Neural Networks) module, which is a popular module in the library that implements forward pass (inferencing) with deep networks, which are pre-trained using popular deep learning frameworks.
Some of the features offered by OpenCV include:
- imread function to read the images in the BGR (Blue-Green-Red) format by default.
- Easy up and downscaling for resizing an image.
- Supports various interpolation and downsampling methods like INTER_NEAREST to represent the nearest neighbor interpolation.
- Supports multiple variations of thresholding like adaptive thresholding, bitwise operations, edge detection, image filtering, image contours, and more.
- Enables image segmentation (Watershed Algorithm) to classify each pixel in an image to a particular class of background and foreground.
- Enables multiple feature-matching algorithms, like brute force matching, knn feature matching, among others.
With its active community and regular updates for Machine Learning, OpenCV is only going to grow by leaps and bounds in the field of Computer Vision projects.
MATLAB: A licensed quick prototyping tool with OpenCV integration
One disadvantage of OpenCV, which makes novice computer vision users tilt towards Matlab is the former’s complex nature. OpenCV is comparatively harder to learn due to lack of documentation and error handling codes. Matlab, developed by MathWorks is a proprietary programming language with a multi-paradigm numerical computing environment. It has over 3 million users worldwide and is considered one of the easiest and most productive software for engineers and scientists. It has a very powerful and swift matrix library.
Matlab also works in integration with OpenCV. This enables MATLAB users to explore, analyze, and debug designs that incorporate OpenCV algorithms. The support package of MATLAB includes the data type conversions necessary for MATLAB and OpenCV.
MathWorks provided Computer Vision Toolbox renders algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. It also allows detection, tracking, feature extraction, and matching of objects. Matlab can also train custom object detectors using deep learning and machine learning algorithms such as YOLO v2, Faster R-CNN, and ACF. Most of the toolbox algorithms in Matlab support C/C++ code generation for integrating with existing code, desktop prototyping, and embedded vision system deployment.
However, Matlab does not contain as many functions for computer vision as OpenCV, which has more of its functions implemented on GPU. Another issue with Matlab is that it’s not open-source, it’s license is costly and the programs are not portable.
Another important factor which matters a lot in computer vision is the performance of a code, especially when working on real-time video processing.
Which has a faster execution time? OpenCV or Matlab?
Along with Computer Vision, other fields also require faster execution while choosing a programming language or library for implementing any function. This factor is analyzed in detail in a paper titled “Matlab vs. OpenCV: A Comparative Study of Different Machine Learning Algorithms”.
The paper provides a very practical comparative study between Matlab and OpenCV using 20 different real datasets. The differentiation is based on the execution time for various machine learning algorithms like Classification and Regression Trees (CART), Naive Bayes, Boosting, Random Forest and K-Nearest Neighbor (KNN). The experiments were run on an Intel core 2 duo P7450 machine, with 3GB RAM, and Ubuntu 11.04 32-bit operating system on Matlab version 188.8.131.525 (R2011a), and OpenCV C++ version 2.1.
The paper states, “To compare the speed of Matlab and OpenCV for a particular machine learning algorithm, we run the algorithm 1000 times and take the average of the execution times. Averaging over 1000 experiments is more than necessary since convergence is reached after a few hundred.”
The outcome of all the experiments revealed that though Matlab is a successful scientific computing environment, it is outrun by OpenCV for almost all the experiments when their execution time is considered. The paper points out that this could be due to a combination of a number of dimensionalities, sample size, and the use of training sets. One of the listed machine learning algorithms KNN produced a log time ratio of 0.8 and 0.9 on datasets D16 and D17 respectively.
Clearly, Matlab is great for exploring and fiddling with computer vision concepts as researchers and students at universities that can afford the software. However, when it comes to building production-ready real-world computer vision projects, OpenCV beats Matlab hand down.
You can learn about building more Computer Vision projects like human pose estimation using TensorFlow from our book ‘Computer Vision Projects with OpenCV and Python 3’.