Spatial Filtering Introduction with An Example | Digital Image Processing

00:03:26
https://www.youtube.com/watch?v=hDsicd5HuRE

Resumo

TLDRIn this tutorial, viewers learn about intensity transformation and spatial filtering in digital image processing. The spatial domain is discussed, explaining pixel layout and grayscale intensity values. The tutorial elaborates on spatial filtering, where pixel intensities are altered by considering neighboring pixels using mathematical operators. An example illustrates neighborhood processing by averaging the intensities of surrounding pixels to adjust a specific pixel’s intensity. The need for various transformation functions indicates their different applications, which will be discussed in future videos.

Conclusões

  • 🖼️ Spatial Domain: The pixel arrangement in an image is called the spatial domain.
  • 🌈 Grayscale Values: Pixel values range from 0 (dark) to 255 (bright).
  • 🔄 Intensity Transformation: It's the process of changing pixel intensities in an image.
  • 🏘️ Spatial Filtering: Alters pixel intensity based on neighboring pixels.
  • 📊 Neighborhood Processing: Computes new pixel values using surrounding pixels' intensities.
  • 📉 Point Processing: Techniques relying only on single pixel intensity.

Linha do tempo

  • 00:00:00 - 00:03:26

    In this tutorial, the concept of intensity transformation in digital images is introduced alongside spatial filtering techniques essential for image processing. The spatial domain representation is explained, highlighting how pixels are organized horizontally and vertically, with their brightness indicated by values ranging from 0 (darkest) to 255 (brightest). The tutorial further explains spatial filtering as a method to adjust a pixel's intensity based on neighboring pixels. An example illustrates calculating the average of a pixel's neighborhood to change its intensity, demonstrating the neighborhood processing technique. Finally, the distinction between neighborhood and point processing techniques is made, with a mention of upcoming videos to cover different transformation functions.

Mapa mental

Vídeo de perguntas e respostas

  • What is intensity transformation in digital images?

    Intensity transformation refers to the process of changing the pixel intensities in an image based on certain operations or transformations.

  • What is the spatial domain in image processing?

    The spatial domain refers to the arrangement of pixels in an image, where each pixel represents a value based on brightness.

  • How are pixel values interpreted in grayscale images?

    In grayscale images, pixel values range from 0 (darkest) to 255 (brightest), indicating different shades of gray.

  • What is spatial filtering?

    Spatial filtering is an image processing technique that changes pixel intensities based on the intensities of neighboring pixels.

  • What technique is used to calculate the new intensity of a pixel?

    The neighborhood processing technique is used to calculate a new intensity by averaging or using other statistical measures of the surrounding pixels.

  • What is the difference between neighborhood processing and point processing?

    Neighborhood processing depends on the intensities of surrounding pixels, while point processing relies solely on the intensity of an individual pixel.

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Legendas
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Rolagem automática:
  • 00:00:00
    hello and welcome to 5-minute tutorials
  • 00:00:03
    in this short episode we will learn what
  • 00:00:06
    is meant by intensity transformation of
  • 00:00:09
    digital images as well as learn the
  • 00:00:11
    concepts of spatial filtering which are
  • 00:00:14
    widely used in digital image processing
  • 00:00:22
    before moving to our topic
  • 00:00:24
    let us first understand what is meant by
  • 00:00:26
    spatial domain representation of an
  • 00:00:28
    image the pixel representation of an
  • 00:00:31
    image is called spatial domain where a
  • 00:00:33
    pixel is the basic logical unit in
  • 00:00:35
    digital graphics in spatial domain the
  • 00:00:39
    pixels are arranged horizontally and
  • 00:00:41
    vertically the top-left pixel in the
  • 00:00:44
    images at position 0 comma 0 the
  • 00:00:47
    positions of other pixels are found
  • 00:00:50
    accordingly
  • 00:00:50
    for example the dark pixel is at the
  • 00:00:53
    position 1 comma 2 for grayscale images
  • 00:00:57
    the value of each pixel is usually in
  • 00:00:59
    the range 0 to 255 the value of a pixel
  • 00:01:04
    shows how bright or dark it appears in
  • 00:01:06
    an image usually the value 0 is the
  • 00:01:10
    darkest while a value of 255 is the
  • 00:01:13
    brightest pixel in the image values in
  • 00:01:16
    between make up the different shades of
  • 00:01:18
    gray
  • 00:01:19
    next let's learn another definition
  • 00:01:21
    spatial filtering it is an image
  • 00:01:24
    processing technique for changing the
  • 00:01:26
    intensities of a pixel according to the
  • 00:01:28
    intensities of the neighboring pixels
  • 00:01:30
    the intensity transformation process can
  • 00:01:34
    be expressed as a mathematical equation
  • 00:01:35
    as follow here f of X comma Y is an
  • 00:01:39
    input image G of X comma Y is the output
  • 00:01:42
    image and T is an operator on F defined
  • 00:01:45
    over a neighborhood of point X comma Y
  • 00:01:47
    the operator T may be like compute the
  • 00:01:50
    average intensity of the pixels in the
  • 00:01:52
    neighborhood or calculate the median of
  • 00:01:54
    the pixel intensities in the
  • 00:01:56
    neighborhood let's understand it with an
  • 00:01:58
    example suppose we have a square image
  • 00:02:01
    with width and height equal to 7 pixels
  • 00:02:04
    a pixel at position 1 comma 2 has a
  • 00:02:07
    value 0 so it is the darkest pixel in
  • 00:02:10
    the image this pixel is surrounded from
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    all sides by 8 other pixels the current
  • 00:02:17
    pixel as well as 8 surrounding pixels
  • 00:02:19
    form a 3 by 3 neighborhood let's
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    calculate the average of all these
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    pixels we sum up all the pixel values in
  • 00:02:27
    the neighborhood and then divide by 9 we
  • 00:02:30
    get the value 227 finally we replace the
  • 00:02:35
    value 0 of the pixel 1 comma 2 by the
  • 00:02:38
    answer we've just calculated that's it
  • 00:02:41
    we've changed the intensity of a pixel
  • 00:02:44
    this similar process is applied on all
  • 00:02:47
    49 pixels of the given image and as a
  • 00:02:50
    result we get an entirely different
  • 00:02:52
    image the technique we've just applied
  • 00:02:55
    is called neighborhood processing
  • 00:02:57
    technique the approaches whose results
  • 00:03:00
    depend only on the intensity of a point
  • 00:03:02
    are called point processing techniques
  • 00:03:04
    for different purposes we have various
  • 00:03:08
    transformation functions for each
  • 00:03:10
    technique we will discuss them in next
  • 00:03:12
    few videos
  • 00:03:15
    I hope you've enjoyed this tutorial
  • 00:03:17
    don't forget to subscribe this channel
  • 00:03:20
    take care and bye
Etiquetas
  • intensity transformation
  • spatial filtering
  • digital image processing
  • spatial domain
  • pixel representation
  • neighborhood processing
  • gray scale
  • image processing techniques
  • mathematical operators
  • pixel values