Computer vision is a complex field that uses multimedia information received from computers to analyze images and videos using AI-based tools.
Internet users are producing a large amount of data every single day like posting images on Instagram and uploading videos on YouTube and so on.
These huge images and video data can be difficult to index and maintain, which can easily handle by computer algorithms.
Computer vision and CV tools are very important to organize huge and complex unstructured data easily which needs only meta descriptions.
It is a very complex field that includes several computers concerning information related to images or videos.
Computer vision is a subset or part of artificial intelligence and machine learning that prepares and investigates several images or videos to gain valuable data using efficient tools.
The fascinating learning algorithms for facial recognition, object identification, image restoration, scene reconstruction are included in Computer Vision.
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Popular Top Computer Vision Tools:
There are multiple computer vision tools are established to deal with images and videos but In the following cv tools are the top tools and we are going to discuss them in detail.
1. OpenCV
It is an open-source or free-to-use computer vision tool and library in deep learning that holds numerous distinct functions and image processing attributes.
OpenCV has designed and delivered in the Year 2000 by Intel and later on makes it a free-to-use community.
It has many different algorithms related to computer vision that can play efficiently and operate on real-time applications.
Facial detection and recognition, object identification, tracking eye movements and camera movements, obtaining 3D models, moving objects monitoring, building an augmented reality, image recognition, etc all the tasks can do using OpenCV.
OpenCV offers an interface to programming like Python, MATLAB, Java, and C++ and it supports multiple operating systems like Linux, Windows, Mac, and Android, etc.
It covers all the fundamental approaches and techniques of computer vision to work out images and video processing tasks, using programming languages and tools.
It is the most well-known and highly valuable library in python so OpenCV-Python was made as an official project for reliable hardware usability and multi-platform operation.
2. Tensorflow
TensorFlow is an open-source platform that accommodates a wide variety of tasks, and other backend support for AI and Machine Learning including Computer Vision.
It is a good model building and easy-to-code python library used as backed for various python libraries like Keras and others for deep learning operations.
It utilizes for training a deep learning model associated with computer vision that includes object identification, face recognition, and so on.
Tensorflow is the most well-known symbolic math library massively used for large data and used for deep learning applications like neural networks.
TensorFlow 2.0 supports different predictive models that are used for picture and speech recognition, object detection, reinforced learning, recommendations of products, etc.
Similar kinds of predictive models permit you to utilize your own best practices and build up your individual unique solutions.
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3. MATLAB
MATLAB is a mathematical computing environment developed by MathWorks in 1984 for advanced mathematical and scientific operations.
It comprises the CV Toolbox for executing numerous algorithms and functions like 3D reconstruction,3-D camera calibration, object tracking, etc.
The machine learning algorithms like ACF, Faster R-CNN, the benefit to create and train custom object detector and recognition applications in MATLAB.
The Matlab algorithms run on GPUs and multicore processors for the fastest execution and they support code generation in programming like C and C++.
MATLAB is a prominent tool for research and that makes it a better image processing application for fast research prototyping.
Other programming benefits can be It generates different suggestions of methods for easy code optimization to manage the errors at the time of program execution.
4. CUDA
NVIDIA’s foundation designed CUDA for parallel processing that can be easy for programming and fast for Computer Vision.
CUDA uses the GPU (Graphics processing unit) to achieve maximum performance and it introduces the NVIDIA execution library that holds a collection of functions for images, signal, and video processing.
This library is used by engineers and developers for general-purpose execution with help of a CUDA-enabled graphics processing engine.
Several other libraries including GPU4Vision, OpenVIDIA, and MinGPU are the modern Computer Vision tools that use CUDA.
5. SimpleCV
SimpleCV is a simple, open-source, and modern computer vision library similar to OpenCV that does not require in-depth learning of all the computer vision tools concepts.
It is a cv framework used for buffer management, eigenvalues, bit depths, matrix storage, file formating, bitmap storage, color spaces, and many others.
It enables users to investigate computer vision by viewing images and video streaming with help of mobiles, computer webcams, FireWire, and others.
On the other hand, it is the best tool to accomplish unusual fast prototyping and simplistic model building from multimedia data.
6. Keras
Keras is the easy-to-use python deep learning library that consistently utilizes quick, simple, or high-level development for deep neural networks.
This library can run on top of different other libraries and it uses themes as backends like TensorFlow, Theano, Microsoft Cognitive Toolkit, or PlaidML, etc.
Keras was used for training convolutional neural networks (CNN) to perform Image Classification and Image Similarity in python.
Building a deep convolutional generative network using Keras supports knowing the technology and the logic behind formulating the fake and edited images and videos.
Related Article: What is an Artificial Neural Network (ANN)?
7. BoofCV
BoofCV is also another similar library like OpenCV which means both tools are open-source and built for real-time computer vision implementation.
It is maintained and delivered by Apache under a 2.0 license that denotes free usage for academic, research, and commercial use.
BoofCV can do extra advanced computer vision-related operations including camera calibration, tracking, low-level image processing, feature detection, and so on.
Similarly, It can perform other functionality like recognition, streamlined low-level image processing routines, structure-from-motion, etc.
8. YOLO
Yolo is a new computer vision tool that is accurate and the latest cutting-edge real-time object detection system.
It is a much faster and reliable cv platform as a comparison to other object detection and computer vision tools but it has less community support.
Yolo uses the neural network the deep learning model for image classification that works on full images to classify the objects.
It applies a single convolutional network for the intact image and operates object detection as a regression problem
YOLO is advanced real-time object detection and comes in computer vision tools that use a neural network to a complete picture and it partitions the image into regions and predicts probabilities for all-region.
9. FastCV
FastCV is an open-source library for computer vision, machine learning, and image processing.
FastCV contains lots of state-of-the-art computer vision algorithms with examples and demos.
FastCV is a pure Java library without any third-party dependencies, The API of FastCV should be very simple to learn.
Thus it is ideal for students or beginners who want to implement computer vision in their projects and prototypes quickly.
We implemented FastCV on Android in order to add computer vision capabilities to our mobile apps and games easily.
10. Scikit-image
Scikit-image, is an open-source software library and comes under the top computer vision tools for image processing in Python.
You can use scikit-image to convert between different color spaces and perform basic operations like thresholding and edge detection.
It’s not a program you’ll use every day, but it comes in handy for a number of practical applications.
For example, you could use scikit-image on your webcam (with some minor setup) to take a photo through infrared light or detect watermarks on photos.
These are just a couple of examples of what you can do with scikit-image, If all else fails, there’s always image manipulation.
Conclusion
A huge amount of data is generated from various platforms like social media every day which is highly overloaded with texts, images, and videos.
These computer vision tools present an ecosystem for combining with many other software and technologies in helping with computer vision operation.
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