Must try computer vision tools of 2020
India: Tue, 23 Jun 2020 22:03, by: Staff Reporters

Using this technology allows machines to recognise and characterise objects through digital images and video recordings.

Computer vision is a type of artificial intelligence that helps devices like computer in deciphering and comprehending the visual world. In simpler words, using this technology allows machines to recognise and characterise objects through digital images and video recordings.

Thus, computer vision is very popular in countless fields like security, health care and manufacturing etc.

Here are the best Computer Vision tools to best match your needs--

1) Tensorflow

It is an open source library of Artificial intelligence. The effortlessness of its API made the quality of Tensorflow expand immediately and beat the other computer vision tools. It provides users with an interface and one can create neural network architecture. Therefore, it is a very notable tool used widely for computer vision.

2) OpenCV

OpenCV is a set of libraries that enables users to write codes for computer vision in a very effective manner. The first reason behind its popularity is that OpenCV is free of cost. Another advantage is that its RAM usage is very less with hardly 60-70mb of usage. It is arguable the best for performing C++ & Python and image processing tasks.

3) Matlab

When it comes to picture processing, Matlab is considered to be one of the finest. It allows matrix manipulation, algorithms implementation and application development which incorporates GUI building.

In contrast to C++, Matlab is very simple which makes the entire process fast


Standing for Compute Unified Device Architecture was created by NVIDIA for programming using GPU. It works like a software layer where developers utilize the power of GPU and can perform tasks like image and videos processing.

5) Theano

Created by the LISA group at the University of Montreal in Canada, Theano is a python library that can run on both CPU and GPU. It is used for developing deep learning models. It enables users to tweak even the smallest detail of the framework.


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