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Top 10 Computer Vision Tools and Computer Vision Use Cases

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Before we dive into different computer vision tools, we need to understand what exactly computer vision is. Computer vision is a form of AI that allows computers and different systems to draw or extract information from videos, visual inputs, or digital images. Furthermore, computer vision allows AI to think and draw up recommendations concerning the information after extracting information.

In other words, computer vision works pretty much as human vision does. Many upcoming companies and businesses use this feature. At present, the industry is valued to reach 48.6 billion by 2022.

With that said, if you’ve been looking for better applications for Computer Vision or Computer Vision tools, then here is a list of apps:

#1. OpenCV

OpenCV has been touted as one of the best applications to use computer vision. This comes from the fact that this application features impressive object detection, contributing to better computer vision. Additionally, OpenCV, presently, is one of the most used open-source libraries for computer vision and image data. Also, the application supported a wide variety of programming languages, like Java, C++, etc. The application can be used for landmark detection, face detection, object tracking, etc.

#2. Tensorflow

If you’re looking for an alternative that beats OpenCV, this is it. Firstly, TensorFlow is managed and run by Google, secure and easy. Furthermore, just like OpenCV, it is an open-source library used for deep learning applications. It also supports traditional machine language, which makes this app perfect. Another reason Tensorflow remains to be one of the most popular apps for computer vision is because of its training modules. Although OpenCV is a great option, Tensorflow has training modules that make it a little better than OpenCV.

#3. Matlab

The third in our list of best apps for computer vision is Matlab. MATLAB is a computer programming language that helps combine desktop environments and design processes for programming. This combination makes MATLAB a great option for creating scripts that combine codes. MATLAB is one of the best applications for computer vision because it helps you gain insight into video data and images. Simultaneously, it is easy to develop algorithms and explore information. Furthermore, it allows you to design vision solutions for some reference or standard procedures. This can help you reduce time and create an effective sequence.

#4. CUDA

Well, CUDA is an upcoming star on this list. One of the primary reasons it has made it to the cream of our list is that it provides faster training of neural networks and deep-learning algorithms. But that’s not all; CUDA paired with GPUs is almost unstoppable. There’s a lot of power in the said domain by unstoppable meaning, however, at present. NVIDIA is working on developing several options for hardware to work in combination with this application to lead to an accelerated computer vision application. What’s more, CUDA comes with many unmatched benefits. For example, it features scattered reads, which means code can be read quite easily from the arbitrary address in the memory.

#5. Theano

One of the reasons Theano is a great app for computer vision is that it can run on CPU and GPU. However, that’s not all. This library of scientific computing has been around since 2007 and has garnered a specific reputation in its own right. Although not as popular as the other members of our list, Theano is still faster than TensorFlow, which is a big deal. Additionally, the single GPU task runs are extremely fast. Additionally, Theano supports various operations and gives you full control over optimizers. Unlike TensorFlow, it comes with a deep learning library, native windows support, and High-Level Wrappers like Lasagne.

#6. SimpleCV

SimpleCV is an open-source framework that allows developers to create computer vision applications. The platform allows you to access several different high-powered computer vision libraries. Furthermore, it also helps you to access OpenCV as well. What sets this application apart from OpenCV is that it allows you to access the libraries right then and there. Unlike OpenCV, which requires you to learn about color spaces, bit depths, buffer management, matrix versus bitmap storage, eigenvalues, etc. Apart from OpenCV, you can access other libraries as well. SimpleCV uses Python for scripting.

#7. Keras

Keras is an API that is developed for humans and not machines. This is what sets it apart! However, there’s more. Keras follows best practices for reducing cognitive load, which helps in offering consistent loads. It also minimizes the number of actions a user will have to make for common use cases.

The application also offers clear and actionable feedback for user errors. Keras also supports CUDA support for GPU. Since Keras is written in Python, it is easy to debug, use, and code. Keras, in the industry, has one of the best documentation.

#8. YOLO

YOLO is fairly new in the game, but that does not deter it from being a good option! YOLO comes from “You Only Look Once,” a fun twist on the Gen-Z YOLO! Jokes apart, YOLO is an algorithm that makes use of neural networks. This allows the application to offer fast, real-time object detection. The algorithm comes loaded with fast R-CNN, Single-Shot MultiBox Detector (SSD), and Retina-Net. Additionally, it performs better than other algorithms, which cannot perform proper object detection in a single run.

#9. BoofCV

This open-source library was written from scratch for real-time computer vision. The functionality of the application is quite good. It can process a range of subjects, low-level image processes, structure from motion, recognition, fiducial detection, feature detection and tracking, etc.

#10. CAFFE

Caffe is a deep learning framework. The application can be made with speed, modularity, or speed. It can be used for academic research projects and to create start-up prototypes. However, that’s not all, and it can even be used to create large-scale, industrial applications with vision, multimedia, and speech. Yahoo! at present implements CAFFE.
Although many other applications are great for computer vision, the list above has all the best applications. Furthermore, it is worth noting computer vision and object detection are relatively common now, so many new applications are being designed and developed.
With the list above, it is clear that OpenCV is unmatched. Although some other applications come too close, nothing beats the application. However, TensorFlow is perfect for those who are still learning.
For those who learn how to use computer vision, you should watch other programs that can help you out.

Computer Vision Cases: Top 10

Now that we know what computer vision is and the applications that work with it, it is time to understand its implementation. You see, computer vision, in the right hands, can be a boon to society. If you’re wondering how and where it can be used, then here at the top ten cases:

1. Transportation,

2. Manufacturing,

3. Healthcare,

4. Image and video annotation.

5. Automated data annotation,

6. Self-driving cars,

7. Customer behavior tracking in retail,

8. Object Recognition and Classification in Traffic in the automobile industry,

9. Human Pose Estimation in fitness,

10. Detection of Wheat Rust in agriculture.

However, it must be known that computer vision’s application is wide. The above cases are mere examples. Logically speaking, computer vision can be used for the same tasks for anything we use our eyes.

Conclusion

Computer vision sounds extensively futuristic, and honestly, it is. It will help many industries and law enforcement in multitudes of ways. Sure enough, it is still developing, and the implementation may take a while, but the technology has the potential to make our lives extensively easy.

What’s more, in professional life, this technology can allow professionals to automate some repetitive processes, which allows for specialized processing of image material, inducing better work speeds. Automating some processes will allow the professionals to focus on other aspects of work and be productive. Furthermore, this can help provide help to skilled personnel and support their decisions.

Computer vision sounds like a product of the 21st century. However, the foundations of the process were laid down back in the 1970s. Computer vision is seeing rapid changes in today’s world with better tech. Furthermore, modern times have seen an increase in the application of technology, and that too outside of research. Computer vision may become an integral part of our lives in the coming years.

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