MATLAB vs scikit-image

Overview

MATLAB

Stacks1.1K

Followers702

Votes37

scikit-image

Stacks311

Followers129

Votes12

GitHub Stars6.4K

Forks2.3K

MATLAB vs scikit-image: What are the differences?

Introduction:

Both MATLAB and scikit-image are widely used tools in the field of image processing and analysis. While MATLAB is a proprietary software developed by MathWorks, scikit-image is an open-source Python library. Although they are used for similar purposes, there are several key differences between the two.

Programming language support: The most significant difference between MATLAB and scikit-image is the programming language they use. MATLAB employs its own proprietary language, whereas scikit-image utilizes Python. This distinction can have implications for code readability, availability of libraries, and overall flexibility.
Cost: MATLAB is a commercial software that requires a license for full usage, while scikit-image is an open-source library that is freely available to everyone. The cost difference can be of utmost importance for researchers or individuals with a limited budget.
Community and documentation: Due to its open-source nature, scikit-image has a large and active community of developers and users. This results in extensive online documentation, forums, and tutorials for users to refer to. MATLAB also has a significant user base and documentation, but the open-source community of scikit-image often provides more up-to-date resources.
Functionality and toolboxes: MATLAB's Image Processing Toolbox is a comprehensive collection of functions for image processing tasks, including filtering, segmentation, morphological operations, and more. On the other hand, scikit-image is a more lightweight library and may not have the same breadth of functionality. However, scikit-image can be supplemented with other Python libraries such as NumPy, SciPy, and OpenCV to achieve similar results.
Integration with other tools: MATLAB is often used as an all-in-one solution for scientific computing and data analysis, with built-in functionality for matrix computations, simulations, and visualization. This tight integration can be advantageous for users who require a unified environment. In contrast, scikit-image integrates well with the wider Python ecosystem, allowing for seamless integration with other libraries and tools commonly used in scientific computing.
Platform compatibility: MATLAB is available on multiple platforms, including Windows, macOS, and Linux. scikit-image, being a Python library, can be used on any platform that supports Python. This flexibility in platform compatibility makes scikit-image suitable for a wider range of operating systems and environments.

In summary, the key differences between MATLAB and scikit-image lie in the programming language, cost, community support, functionality, integration options, and platform compatibility.

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Detailed Comparison

MATLAB	scikit-image
Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.	scikit-image is a collection of algorithms for image processing.
-	Provides I/O, filtering, morphology, transformations, measurement, annotation, color conversions, test data sets, etc.;Written in Python with a well-commented source code;Has had 5,709 commits made by 116 contributors representing 29,953 lines of code;Released under BSD-3-Clause license
Statistics
GitHub Stars -	GitHub Stars 6.4K
GitHub Forks -	GitHub Forks 2.3K
Stacks 1.1K	Stacks 311
Followers 702	Followers 129
Votes 37	Votes 12
Pros & Cons
Pros 20 Simulink 5 Functions, statements, plots, directory navigation easy 5 Model based software development 3 S-Functions 2 REPL Cons 2 Does not support named function arguments 2 Doesn't allow unpacking tuples/arguments lists with * 2 Parameter-value pairs syntax to pass arguments clunky 1 Costs a lot	Pros 6 More powerful 4 Anaconda compatibility 2 Great documentation

What are some alternatives to MATLAB, scikit-image?

JavaScript

JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.

Python

Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.

PHP

Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.

Ruby

Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.

Java

Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!

Golang

Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.

HTML5

HTML5 is a core technology markup language of the Internet used for structuring and presenting content for the World Wide Web. As of October 2014 this is the final and complete fifth revision of the HTML standard of the World Wide Web Consortium (W3C). The previous version, HTML 4, was standardised in 1997.

C#

C# (pronounced "See Sharp") is a simple, modern, object-oriented, and type-safe programming language. C# has its roots in the C family of languages and will be immediately familiar to C, C++, Java, and JavaScript programmers.

Scala

Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.

Elixir

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

Related Comparisons

MATLAB vs scikit-image: What are the differences?

Introduction:

Programming language support: The most significant difference between MATLAB and scikit-image is the programming language they use. MATLAB employs its own proprietary language, whereas scikit-image utilizes Python. This distinction can have implications for code readability, availability of libraries, and overall flexibility.
Cost: MATLAB is a commercial software that requires a license for full usage, while scikit-image is an open-source library that is freely available to everyone. The cost difference can be of utmost importance for researchers or individuals with a limited budget.
Community and documentation: Due to its open-source nature, scikit-image has a large and active community of developers and users. This results in extensive online documentation, forums, and tutorials for users to refer to. MATLAB also has a significant user base and documentation, but the open-source community of scikit-image often provides more up-to-date resources.
Functionality and toolboxes: MATLAB's Image Processing Toolbox is a comprehensive collection of functions for image processing tasks, including filtering, segmentation, morphological operations, and more. On the other hand, scikit-image is a more lightweight library and may not have the same breadth of functionality. However, scikit-image can be supplemented with other Python libraries such as NumPy, SciPy, and OpenCV to achieve similar results.
Integration with other tools: MATLAB is often used as an all-in-one solution for scientific computing and data analysis, with built-in functionality for matrix computations, simulations, and visualization. This tight integration can be advantageous for users who require a unified environment. In contrast, scikit-image integrates well with the wider Python ecosystem, allowing for seamless integration with other libraries and tools commonly used in scientific computing.
Platform compatibility: MATLAB is available on multiple platforms, including Windows, macOS, and Linux. scikit-image, being a Python library, can be used on any platform that supports Python. This flexibility in platform compatibility makes scikit-image suitable for a wider range of operating systems and environments.

In summary, the key differences between MATLAB and scikit-image lie in the programming language, cost, community support, functionality, integration options, and platform compatibility.

MATLAB vs scikit-image

Overview