10 Computer Vision Tools You Need To Have In Your Pocket
Just like human beings, computers can also see an image and describe what it sees correctly. The computer achieves this by utilizing computer vision software, which involves integrating Artificial Intelligence (AI) and machine learning (ML). This has furthered the world’s dependence on AI and machine learning as computer vision cuts across all fields. It has uses in healthcare, manufacturing, fashion, security e.t.c. Investing in computer vision is highly profitable, and you no longer have to worry about the best computer vision tool available. In this post, we have curated the best computer vision software from which you can easily choose.
What is Computer vision?
Before we go on, we owe it to you to deepen your understanding of Computer vision. Computer vision is a subdivision of AI that enables computers to aggregate, understand, and interpret images and other visual information. In essence, the software allows the computer to ‘see’, much like human beings do. It is a fusion of AI and ML. Thus, It involves a lot of data analysis and ML algorithms to ‘see’, describe, analyze and process visual information.
The 3 essential parts of computer vision software are:
- Acquisition of Images
- Processing of Images
- Analysis of Image and Image comprehension
Computer Vision Tools
Computer vision software is a complex field and requires a competent computer vision engineer to navigate it and the hardware part of computer vision. Thus, there are computer vision tools that serve as computer vision libraries.
Image Processing Tools
Image processing is not the same as computer vision, contrary to what you might think. Admittedly, both are similar, but some nuances differentiate them. Image processing is the transformation or the enhancement of a picture. Computer vision, on the other hand, is the ability of a computer to see, analyze and understand an image. Computer vision uses image processing tools and algorithms in performing some of its functions.
Image processing and image processing tools are used in image enhancement, while computer vision tools are used in simulating human sight.
What is the Best Computer Vision Tool?
OpenCV: is an open computer vision software, which makes it easily accessible. It is a cross-platform library that allows users of different operating systems to use it seamlessly. This nifty tool uses its efficient computer vision system toolbox to provide about 2,500 algorithms to its users.OpenCV is a go-to tool for both established and startup companies all over the world.
- It is easy to use.
- It is an excellent image processing tool.
- There is no restriction when using the library.
- It has a large community of interactive users.
- It is very efficient.
- Some algorithms are slow for ARM devices (smartphones, tablets, etc.)
- It is free.
- It is user-friendly and is functional over various algorithms.
- Its large community of users is helpful.
Matlab (Matrix laboratory)
Matlab (Matrix laboratory): Created in 1984 by MathWorks, Matlab has a library that combines data programming and design processes. Its computer vision toolbox contains and allows efficient execution of algorithms. It has an image processing tool that promises high quality when it is used to manipulate and enhance images after obtaining them from imaging hardware.
It is useful in financial institutions and research institutions as it helps create trustworthy and efficient test models through its quick prototyping.
- It makes use of different languages.
- It makes programming easy.
- It is not beginner-friendly.
- It is quite technical, as it requires prior know-how.
- $29 for students.
- $95 for home use.
- $275 for research purposes.
- $940 for regular use. This involves operating, installing and administering by oneself.
- It is easy to use after you master it.
- It has a simple user interface.
- It has a free forum where users rub minds together.
TensorFlow: This is an easy open-source which has a range of tools, programming languages, libraries and resources. It is one of the best tools which incorporates artificial intelligence and machine learning. It has many benefits, which include efficient web applications, which makes it popular with Google. It has extensive community support and a broad ecosystem that possesses useful tools in its computer vision system toolbox. It has consistent updates, which helps it maintain the precise execution of algorithms.
- It has a precise execution of algorithms.
- It has consistent updates.
- It supports many libraries and programming languages.
- It has the best computer vision library for python-support.
- Its error messages are hard to debug and decipher.
- It has difficult concepts that may be hard for newbies to grasp.
- It has a low computation speed.
- It is free.
- It has good customer support.
- It is useful in many fields.
SimpleCV: This is a simple open-source structure that is perfect for creating different computer vision applications. It is an excellent machine vision software for python. SimpleCV delivers a precise and legible interface for image manipulation and feature extraction. SimpleCV makes it simple to obtain prototypes easily.
- Information on image features can be gotten without hassle.
- Manipulations are quick to do.
- New algorithms are continuously made.
- The API is easy for beginners to understand.
- Some API does not work as efficiently with Python 2, as it does with python.
- It doesn’t support other programming languages, like C++ or Java, as it only supports python.
- It is free.
- It has an interactive and helpful community that helps whenever one has issues.
- It supports many complex algorithms.
CUDA (Compute Unified Device Architecture): This is a computing software platform and programming simulation for computing processes on graphical processing units (GPUs). It is a type of API used by developers to program these GPUs for general purpose processing. They use this software to speed up computing applications. CUDA is devised to function with programming languages, such as C++ and Fortran. This makes it easier for gurus in programming to use GPU properties.
- Quicker downloads to and quicker feedback from the GPU.
- Codes can be accessed from random addresses easily.
- Reduced efficiency due to activities between host and device memory.
- One-way traffic between programming language and CUDA.
- It is applicable in a wide range of disciplines.
KNIME (Konstanz Information Miner): Are open-source data analytics and combination software that enables the merging of various data sources, for data analysis and programming. It is useful in many disciplines, like in the industries and research.
- Enables processing of vast volumes of data.
- Combines other open-source algorithms.
- It randomly stops working.
- Its delivery for other programming languages is sluggish.
- It is easy for beginners to acclimatize to.
Weka: This is an aggregation of machine learning software for data extraction. It is an open-source machine vision software that can be acquired through a graphical user interface. It has tools that are useful in data classification, processing, clustering and visualization.
- It is easy to use.
- It has an extensive group of data simulation styles.
- It can work on most modern computing structures.
- It only works with small datasets.
- It is efficient.
- It is fast and easy to use.
Raster Vision: This is an open-source structure for python developers who create computer vision models on aerial and satellite imagery. It is software that enables engineers to formulate channels for machine vision learning, i.e., training models, making predictions, analyzing training data, and packaging the model files for timely dissemination.
Boof CV: This is an open-source real-time computer vision tool created from scratch. It is functional in a wide range of interests. It is useful for in-camera calibration, the low-level processing of images, feature detection, geometric computer vision, and structure-from-motion. It has a set of codes that can be easily read and understood.
AForge.NET: This is a computer vision tool created for developers, researchers, and users in the world of Artificial Intelligence. It was initially released in 2006 by Andrew Kirillov.
Computer vision has a potential of use that cuts across all fields such as health, agriculture, manufacturing, automobile industry, the fashion industry, and so on. Computer Vision is the present and the future. Its implementation will make the world a better place to live in. Instead of that, everyone should take advantage of computer vision through any of the tools we have listed above. The world is heading to a digital utopia and harnessing the power of Computer vision can give you the edge you need to stay on top of things.
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