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Facial features vigorously change with illumination and direction, leading to increased false rejection as well as false acceptance and we can’t store images of the same person at a different angle and illumination. If we do this then we will be led to overloading of database [1].75% of the authentication of the facial recognition system fails due to the change of orientation of the test face image then from the stored image [1]. So, we need some feature that is invariant to the change in orientation.

In [2] they selected 9 feature points that have the property of angle invariance…


NOTE: It is a summary from the book Digital Image Processing, 4th Edition Rafael C. Gonzalez, University of Tennessee Richard E. Woods, MedData Interactive

Intensity Transformation Functions and spatial domain

So, in this article, we will discuss Intensity Transformation Functions that are implemented in Spatial Domain. In the spatial domain, we used to operate directly on the pixels of an image. Spatial domain techniques are more efficient computationally and require fewer processing resources to implement.

The spatial domain process is denoted by g(x,y)=T [f(x,y)] where f(x,y) is the input image, g(x,y) is the output image and T is the operator…


NOTE: It is a summary from the book Digital Image Processing, 4th Edition Rafael C. Gonzalez, University of Tennessee Richard E. Woods, MedData Interactive

The main objective of this article is to introduce the maths and the image enhancement techniques to the reader…..

Array versus Matrix Operation :

Images are viewed as the matrix. But in this series of DIP, we are using array operation. There is a difference is Matrix and Array Operation. In Array, the operation is carried out by Pixel by Pixel in Image.

Let these we two images:

Figure 1: Two images

Then the Matrix Operation is :


CODEX

NOTE: It is a summary from the book Digital Image Processing, 4th Edition Rafael C. Gonzalez, University of Tennessee Richard E. Woods, MedData Interactive

Before digging into the deep concept of DIP we should know how images are perceived by humans.

Structure of the Human Eye

Figure 1: Structure of the Human Eye Souce: https://www.news-medical.net/health/Anatomy-of-the-Human-Eye.aspx

Figure 1 shows the anatomy of the eye and we have learned about it in 10th class so moving on to the image formation by the human eye. As we know that the distance between the lens and the retina (imaging region) is fixed and the focal length needs to be adjusted to achieve…


NOTE: It is a summary from the book Digital Image Processing, 4th Edition Rafael C. Gonzalez, University of Tennessee Richard E. Woods, MedData Interactive

Digital image processing is the use of digital computers to process digital images through an algorithm.

Digital image processing focuses on two major tasks :

  1. Improvement of pictorial information for human interpretation
  2. Processing of image data for storage, transmission and representation for autonomous machine perception

Image is defined as 2-d function f(x,y) where x and y are spatial (plane) coordinates and amplitude of f at any pair of coordinates (x,y) is called intensity or grey level…


MLearning.ai collaborates with over 500 researchers from 6 continents

1. Hive Data Types

Hive data types are categorized into numeric types, string types, misc types, and complex types. A list of Hive data types is given below.

  • TINYINT (1-byte signed integer, from -128 to 127)
  • SMALLINT (2-byte signed integer, from -32,768 to 32,767)
  • INT (4-byte signed integer, from -2,147,483,648 to 2,147,483,647)
  • BIGINT (8-byte signed integer, from -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807)
  • FLOAT (4-byte single precision floating point number)
  • DOUBLE (8-byte double precision floating point number)
  • DECIMAL (Hive 0.13.0 introduced user definable precision and scale)
  • TIMESTAMP
  • DATE
  • STRING
  • VARCHAR
  • CHAR
  • BOOLEAN
  • BINARY
  • arrays:

It is a collection of similar types of values that are indexable using…


1. Why we need a hive?

Before digging dip into Hive, we should know why we require it. So, it all started in the early ’90s when Facebook started and slowly the number of users increased. With time they had 1 billion users and with that, there was an increase in the data they have to process like it was around 1000 terabytes of data and 1 lakh queries and 500 photographs uploaded every day. So, this is a huge amount of data and we needed to process it.
So, the first thing everybody thought to use was “RDBMS” but RDBMS can’t handle such a huge amount…


As a Data Scientists we are required to make good charts and are extremely good with numbers but convey the inferences from the data to the clients we don’t need colorful graph and use advance tools and decorate graphs like charismas tree .We can convey our findings from some numbers to and have an influence on the people. And as a Data Science aspirant we need to tell about our project and the work we have done and tell the inferences using data mining techniques to the decision makers so they can apply it on the business .But to convince…

Anshu

A Data Science Aspirant and Student at The Northcap University. Read my research paper: https://ieeexplore.ieee.org/document/9362914

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