# A Comprehensive Guide to the MATLAB ‘does not equal’ Operator

This article aims to provide an in-depth understanding of the concept of “does not equal” in MATLAB, its usage within the context of programming and computer vision tasks. We will discuss the importan …

Updated November 12, 2023

*Hey! If you love Computer Vision and OpenCV as much as I do let's connect on Twitter or LinkedIn. I talk about this stuff all the time and build cool projects.*This article aims to provide an in-depth understanding of the concept of “does not equal” in MATLAB, its usage within the context of programming and computer vision tasks. We will discuss the importance of this operator along with examples that illustrate how it can be employed in both MATLAB and Python scripts for various applications such as image processing, classification, and pattern recognition. Additionally, we’ll explain the differences between ‘does not equal’ and other comparison operators commonly used in programming languages like Python and MATLAB.

## Introduction to MATLAB and Computer Vision

MATLAB is a high-level language specifically designed for technical computing, which allows engineers, scientists, and researchers to analyze and visualize data using vectorization capabilities and matrix operations. With its ability to quickly process large amounts of data, it is widely used in various fields like engineering, math, statistics, image processing, and computer vision.

Computer Vision refers to the field of artificial intelligence that deals with extracting information from digital images or videos. It involves tasks such as object detection, classification, feature extraction, and pattern recognition. Computer scientists leverage programming languages like Python and MATLAB for creating models and algorithms that can process image data efficiently.

## The ‘does not equal’ Operator in MATLAB

In MATLAB, the ‘does not equal’ operator is denoted by the double tilde (~). It evaluates whether a given expression or condition does not satisfy a specific criterion. To better understand this concept, let us first examine some of the basic comparison operators available in programming languages like Python and MATLAB:

- Equal to (==): This operator checks if two values are equal. For example, x == 2 compares if variable ‘x’ is assigned with the value 2.
- Not equal to (!= or !=): The not equal to operators check if two values are different from each other. It acts as the opposite of the equality operator (==).

Now let us examine how the MATLAB ‘does not equal’ operator (~) works:

The ~ operator does not work exactly like the Python ‘not in’, which checks whether a certain value is present in a collection. Instead, the double tilde (~) in MATLAB applies to each element of a vector or matrix, checking if an expression or condition holds true for that particular element.

## Usage and Examples with Code Samples

Here are some practical examples of using ‘does not equal’ operator in MATLAB and its translation into Python:

### Example 1: Checking If All Elements Are Different from a Given Value

In this example, we want to determine if each element in the vector x is distinctly different from the value 3. If at least one element equals 3, then it won’t be considered as ‘not equal’. The code will look like:

MATLAB:
`v = [1, 4, 2, 5]; ~(v == 3)`

Python:
`my_list = [1, 4, 2, 5]`

`if not any(x==3 for x in my_list):`

`print('All elements are not equal to 3')`

### Example 2: Identifying Unique Elements in a Vector or Matrix

In this case, we aim to filter out duplicate values from the vector v and create a new vector containing only unique elements. We can use the ‘does not equal’ operator to achieve this goal by iterating through each element of the original vector and checking if any other element matches it:

MATLAB:
`u = v(~v == v);`

Python:
`unique_list = []`

`for x in v:`

`if not len([y for y in v if y==x]>1):`

`unique_list.append(x)`

In conclusion, the ‘does not equal’ operator (~) is a versatile tool in MATLAB and other programming languages like Python, which can be leveraged to perform various tasks such as analyzing data sets and optimizing computer vision algorithms for image processing. Understanding its usage within the context of these languages enables researchers and engineers to develop efficient solutions that cater to their specific requirements.