SlideShare une entreprise Scribd logo
1  sur  47
Télécharger pour lire hors ligne
Image Processing
Date:
INSTRUCTOR
DR. MOHSIN SIDDIQUE
ASSIST. PROFESSOR
DEPARTMENT OF CIVIL & ENV ENG.
 Image processing actions can be grouped into three sub-areas (Prats-
Montalban et al. 2011):
 Image compression, which reduces the memory requirements by removing
the redundancy present in the image, that is, the image information which is
not perceptible to the human eye.
 Image preprocessing, which consists of improving the visual quality of the
image by reducing noise, pixel calibration and standardization, enhancing
the edge detection, and making the image analysis step more reliable based
on objective and well established criteria. The term image preprocessing, in
general, is referred to all manipulations on an image, each of which produces
a new image.
 Image analysis, which usually returns numeric values and/or graphical
information about the image characteristics that are suited for classification,
defect detection, or prediction of some of the quality properties of the
imaged object. The term image analysis is used when the output is a number
or decision, not an image.
Image processing
2
 Image: A reproduction or imitation of the form of a person or thing.
 A 2D matrix of intensity (grey or color) values.
 Images typically generated by illuminating a scene and absorbing
energy reflected by scene objects
Image & Pixel
3
Pixel: A pixel is generally thought of as the smallest single component of a digital image. In above
image, 1 small square represents an image pixel
 A common type of solid-state detector
in current use is the charge coupled
device (CCD).
 At a specific pixel location, the CCD
element is exposed to incident light
energy and it builds up an electric
charge proportional to the intensity of
the incident light.
 The electric charge is subsequently
amplified and converted from analog
to digital form.
Image creation: Digital Imaging Device
4
An example of the process for creation of a digital image (B. Park, R. Lu , 2015)
Image creation
5
Digital Format of Image
Pixels are the smallest units of an image
6
 Common image formats include:
1 values per point/pixel (B&W or Grayscale)
3 values per point/pixel (Red, Green, and Blue)
4 values per point/pixel (Red, Green, Blue, + “Alpha” or Opacity)
Digital Image Format
What is light?
8
The visible portion of the electromagnetic (EM) spectrum. It occurs
between wavelengths of approximately 400 and 700 nanometers.
Electromagnetic (EM) Spectrum
Electromagnetic (EM) Spectrum
9
The Visible Spectrum
The color of an object is defined by the color of the light that it
reflects . Thus a “blue” object is “blue” because it reflects blue
light.
Intermediate colors are formed when an object reflects two or
more of the additive primaries.
10
 Radar imaging (radio waves)
 Magnetic Resonance Imaging (MRI) (Radio waves)
 Microwave imaging
 Infrared imaging
 Photographs
 Ultraviolet imaging telescopes
 X‐rays and Computed tomography
 Positron emission tomography (gamma rays)
 Ultrasound (not EM waves)
Images from EM spectrum
11
Basic Image Measurements
12
 Spatial resolution-pixel per unit dimension
ppi, dpi, & lpi etc
 Pixel bit depth or color depth or Intensity level resolution- number of
shades that can actually be represented by the amount of information saved
for each pixel
 Saturation and noise
 Spatial resolution is defined as the rate, or number of times, at which an
image is sampled during the acquisition or imaging process. More specifically,
it is the frequency of pixels used to capture sample shades in the space of the
object being digitized.
 Generally, more pixels per unit dimension means a higher resolution, but the
overall image quality cannot be determined by spatial resolution alone.
 Typical array sizes in pixels (or pixel resolution) in many imaging sensors vary
from 640x480 to 2,048x1,536 pixels or even higher.
 For reference human vision is >100 million pixels.
 Quantitatively, spatial resolution can be described as:(Gonzales and Woods
2008). ppi (pixels per inch), dpi (dots per inch), and lpi (line pairs per inch)
 ppi is commonly used for images while dpi and lpi are considered printing
terms.
Resolution (spatial resolution)
13
Resolution (spatial resolution)
Images taken from Gonzalez & Woods, Digital Image Processing (2002)
14
Resolution
15
 Tone can be defined as each distinguishable variation from white to
black.
 Color may be defined as each distinguishable variation on an image
produced by a multitude of combinations of hue (color), value
(lightness-intensity) and chroma (saturation-highest intensity).
 If there is not sufficient contrast between an object and it's background
to permit, at least, detection there can be no identification.
 While a human interpreter may only be able to distinguish between
ten and twenty shades of grey; interpreters can distinguish at least
100 times more variations of color on color photography than shades
of grey on black and white photography.
Tone/Color
16
 Saturation: Highest intensity (shade) value above which color is washed out
 Noise: grainy texture pattern
Saturation and noise
17
Images taken from Gonzalez & Woods, Digital Image Processing (2002)
Pixel bit depth or color depth or Intensity level resolution
18
 It is the number of intensity levels (shades) used to represent the image
 The more intensity levels used, the finer the level of detail discernable in an
image.
 Intensity level resolution usually given in terms of number of bits used to
store each intensity level
Images taken from Gonzalez & Woods, Digital Image Processing (2002)
256 grey levels,
(8bit per pixel)
Pixel bit depth or color depth or Intensity level resolution
19
 Computers work on a binary system; each bit of data is either 1 or 0.
 Each pixel in a raster image is represented by a string of binary digits and
the number of digits is known as the bit depth. Hence, a one-bit image can
assign only one of two values to a single pixel: 0 or 1 (black or white).
 An 8-bit (28) gray scale image can assign one of 256 colors to a single
pixel. A 24-bit (2(3x8) RGB image (8-bits each for red, green and blue color
channels) can assign one of 16.8 million colors to a single pixel.
 The following formula can be used to calculate the shades using bit
depth
Number of Shades = 2x, where x = the bit depth
 To achieve a desired bit depth without any data loss, it is necessary to
digitize a photograph at a higher bit depth and then scale down to the
desired bit depth after any image processing has occurred
 Several different systems are used to represent color images. The most
common are:
RGB (additive color system),
CMYK (subtractive color system),
HSV and the
CIELAB color space.
 The terms color space and color profile can often be used interchangeably,
since a profile is a description or numerical model of a specific color space.
 There are two types of profiles: matrix-based and table-based.
Matrix-based profiles use mathematical formulas to describe the 3D color
spaces
Table-based profiles, as the name implies, use a large table of sample
points called a Look-Up Table or LUT to define the 3D color space
Color representation
20
 RGB is a color model that uses the three primary (red, green, blue) additive
colors, which can be mixed to make all other colors
 CMYK (CMY) is a color model based on subtracting light. The cyan (C),
magenta (M), yellow (Y) and key or black (k) are the basic colors for a
subtractive model, and represent the complements of the three primary colors
Color representation
21
Color representation
22
(B. Park, R. Lu , 2015)
 File types are used to encode digital images, allowing for compression and
storage.
 There are hundreds of image file formats. For examples
Tagged Image File Format (TIFF)
Graphics Interchange Format (GIF)
Portable Network Graphics (PNG)
JPEG, BMP, Portable Bitmap Format (PBM), etc
 Image pixel values can be:
Grayscale: 0 – 255 range
Binary: 0 or 1
Color: RGB colors in 0‐255 range (or other color model)
File types
23
 Binary Images: In a binary image,
each pixel assumes one of only two
discrete values: 1 or 0.
 Indexed Images: An indexed image
consists of an array and a colormap
matrix.
 Grayscale Images: A grayscale
image (also called gray-scale, gray
scale, or gray-level) is a data matrix
whose values represent intensities
within some range.
 Truecolor Images: A truecolor image
is an image in which each pixel is
specified by three values — one each
for the red, blue, and green
components of the pixel's color.
Type of digital images
24
 If a color image has to be converted into an intensity or grayscale image, the
following equations can be used. One alternative is the simple average of the
R, G, B color channels:
 Another equation, which takes into account the luminance perception of the
human eye, is
Type of digital images
25
Image processing (or pre-processing)
 Algorithms that alter an input image to create new image
 It involves a series of image operations to enhance the quality of a digital
image so as to remove defects such as geometric distortion, improper focus,
repetitive noise, non-uniform lighting and camera motion.
26
Image processing ( or pre-processing)
An overview of the operational steps for a image processing (machine vision system)
(B. Park, R. Lu , 2015)
27
 The operations that can be performed on digital images include:
point, local or neighborhood, and global operations.
 Point operations transform pixels without regard to neighboring pixels. The
gray value of the output image at a particular pixel depends only on the
gray value of the same pixel in the input image.
 A local or neighborhood operation or mask operation generates an output
pixel whose value depends on the pixel values in a neighborhood of the
corresponding input point.
 An operation is a global operation if the output value at specific coordinate is
dependent on all the values in the input images.
Image processing ( or pre-processing)
28
 Image enhancement is the process of improving the visual appearance of
digital images.
 It is usually done by contrast enhancement or histogram manipulation.
 Contrast adjustment remaps image intensity values to the full display range
of the data type. An image with good contrast has sharp differences between
black and white.
Image enhancement
29
To illustrate, the image on the left has poor contrast, with intensity values limited to
the middle portion of the range. The image on the right has higher contrast, with
intensity values that fill the entire intensity range [0, 255]. In the high contrast image,
highlights look brighter and shadows look darker (ref. https://www.mathworks.com/)
Image enhancement
30
 Histogram manipulation
 Concept of an image histogram.
 Mathematically, histogram is a graphical
presentation of the frequency count of the
occurrence of each intensity (brightness value)
in an image.
 The brightness values (i.e. 0-255) are displayed
along the x-axis of the graph.
 The frequency of occurrence of each of these
values in the image is shown on the y-axis
Image enhancement
31
There is no single enhancement procedure which is best
Best one is that which best displays the features of interest to the Analyst
 Linear stretch: stretch using minimum
and maximum values.
 Histogram equalization: stretch using a
nonlinear function derived from
distribution of intensities.
 Density slicing (pseud coloring):
(Introducing color to a single-band
Image)
divide the range of values in a
single band by assigning each
interval into a color.
Image enhancement
Density slicing (pseud coloring)
Linear stretch
Histogram equalization
32
Image enhancement
33
 All arithmetic operations performed on matrices may be performed on
images. Arithmetic operations between images are array operations carried
out between corresponding pixel pairs. Hence, the images normally have to
be of the same size. These operators are frequently used for reducing noise
and image enhancement.
 The four arithmetic operations are as follows:
Addition
Subtraction
Multiplication
Division
Arithmetic operations
34
 Filtering is a process that selectively enhances or suppresses particular
frequency (wavelengths) within an image.
 Two approaches to digitally filtering data:
Filtering in the spatial domain
 Convolution, correlation etc
Filtering in the frequency domain
 Edge detection and Enhancement
 Fourier transform
 Wavelet transform
Filtering Techniques
35
 Low pass filters: e.g., Average (mean), median, and Gaussian filters
Removes high frequency. Low pass filtering causes blurring
 High pass filters: e.g., Gradient and Laplacian kernels
Opposite of low pass filtering: eliminate low frequency values. High pass
filtering causes image sharpening
Filtering Techniques
36
Original Low pass High pass
 With spatial image filtering technique, a window of finite size and shape is
scanned across the entire image, transforming the local intensities in the output
image. The window with its weights is called the convolution kernel or filter
mask.
Filtering Techniques: Spatial filtering
37
https://towardsdatascience.com/convolu
tion-vs-correlation-af868b6b4fb5
 Two main linear spatial filtering methods are correlation and convolution.
 Correlation is the process of moving a filter mask over the image and
computing the sum of products at each location
 The mechanisms of convolution are the same, except that the filter is first
rotated by 180o
 Correlation and convolution yield the same result when the filter mask is
symmetric. However, basic image processing techniques are mainly based on
convolution.
Filtering Techniques: Spatial filtering
38
 All smoothing filters build a weighted average of the surrounding pixels, and
some of them also use the center pixel itself.
 Averaging and Gaussian filters are linear filters often used for noise
reduction with their operation causing a smoothing in the image but having the
effect of blurring edges.
Filtering Techniques: Image smoothing and blurring
Examples of averaging filter using masks [3x3] and [9x9] (B. Park, R. Lu , 2015)
Examples of Gaussian filter using masks [3x3] and [9x9] (B. Park, R. Lu , 2015)
39
 Sometimes, non-linear operations on neighborhoods yield better results. An
example is the use of a median filter to remove noise. Median filtering
replaces each pixel by the median in a neighborhood around the pixel.
Filtering Techniques: Image smoothing and blurring
Example of median filter using a kernel [33]: the input image (left) contains
Gaussian noise, and the noise is removed in the resultant image (right) after 33
median filtering (B. Park, R. Lu , 2015)
40
 An edge is an area of an image characterized by sharp changes in gray-level
or brightness.
 The process of edge detection attenuates high fluctuations in color, i.e., dramatic
change in intensity. In the frequency domain, this process refers to the
attenuation of high frequencies.
Filtering Techniques: Edge Detection and Enhancement
41
https://web.cs.wpi.edu/~emmanuel/courses/cs545/S14/
 Among the families of edge detection filters are: Gradient filters, Laplacian,
and wavelet transform (Klinger 2003).
 Both gradient and Laplacian kernels are of the high-pass filter, which operates
by differencing adjacent pixels, because the sharp edges can be described
by high frequencies.
Filtering Techniques: Edge Detection and Enhancement
Edge detection
42
 Image segmentation is one of the most important steps in the entire image
processing technique, as subsequent extracted data are highly dependent on
the accuracy of this operation.
 Its main aim is to divide an image into regions that have a strong correlation
with objects or area of interest (i.e., ROIs).
 Segmentation can be achieved by three different techniques (Sonka et al.
1999; Sun, 2000):
Thresholding,
Edge-based segmentation, and
Region-based segmentation
Image segmentation
43
 Threshold can be applied to an image to isolate a feature represented by
specific range of digital numbers (DN).
Image segmentation: Thresholding
 To calculate the area of lakes, DNs not
representing water are a distraction
 Highest DN for water is 35 and is used
as the threshold
 All DNs > 35 are assigned 255
(saturated to white);
 DNs<=35 are assigned zero
(black)
 The lakes are much more prominent in
the image after
44
Image segmentation
Image segmentation process for bananas (B. Park, R. Lu , 2015)
45
 Digital image classification is the process of assigning pixels to classes.
 Since, measured reflection values in an image depend on the local
characteristics of the object; in other words there is a relationship between
objects and measured reflection values.
 Therefore, by comparing pixels each other, it is possible to assemble groups
of similar pixels into classes and pixels within the same class are spectrally
similar each other, however, in practice, they have some variability within
classes.
Digital image classification
46
Comments….
Questions….
Suggestions….
References:
B. Park, R. Lu , (2015), Chapter 2: DOI: 10.1007/978-1-4939-2836-1_2
Gonzalez & Woods, Digital Image Processing (2002)
https://web.cs.wpi.edu/~emmanuel/courses/cs545/S14/
47
Thank you !

Contenu connexe

Tendances

Digital image processing techniques
Digital image processing techniquesDigital image processing techniques
Digital image processing techniquesShab Bi
 
Image Acquisition and Representation
Image Acquisition and RepresentationImage Acquisition and Representation
Image Acquisition and RepresentationAmnaakhaan
 
Digital Image Processing_ ch1 introduction-2003
Digital Image Processing_ ch1 introduction-2003Digital Image Processing_ ch1 introduction-2003
Digital Image Processing_ ch1 introduction-2003Malik obeisat
 
Texture in image processing
Texture in image processing Texture in image processing
Texture in image processing Anna Aquarian
 
Digital Image Processing - Image Enhancement
Digital Image Processing  - Image EnhancementDigital Image Processing  - Image Enhancement
Digital Image Processing - Image EnhancementMathankumar S
 
Lecture 4 Relationship between pixels
Lecture 4 Relationship between pixelsLecture 4 Relationship between pixels
Lecture 4 Relationship between pixelsVARUN KUMAR
 
Image enhancement techniques a review
Image enhancement techniques   a reviewImage enhancement techniques   a review
Image enhancement techniques a revieweSAT Journals
 
Introduction to Digital Image Processing
Introduction to Digital Image ProcessingIntroduction to Digital Image Processing
Introduction to Digital Image ProcessingNagashree Bn
 
Image classification
Image classificationImage classification
Image classificationAnchor Charan
 
Image classification
Image classificationImage classification
Image classificationAli A Jalil
 
Data input and transformation
Data input and transformationData input and transformation
Data input and transformationMohsin Siddique
 
Change detection techniques
Change detection techniquesChange detection techniques
Change detection techniquesFemi Opaleye
 
Feature detection and matching
Feature detection and matchingFeature detection and matching
Feature detection and matchingKuppusamy P
 
Image processing fundamentals
Image processing fundamentalsImage processing fundamentals
Image processing fundamentalsA B Shinde
 
Digital image classification
Digital image classificationDigital image classification
Digital image classificationAleemuddin Abbasi
 
Image enhancement techniques
Image enhancement techniquesImage enhancement techniques
Image enhancement techniquesSaideep
 
Image enhancement
Image enhancementImage enhancement
Image enhancementAyaelshiwi
 

Tendances (20)

Digital image processing techniques
Digital image processing techniquesDigital image processing techniques
Digital image processing techniques
 
Image Acquisition and Representation
Image Acquisition and RepresentationImage Acquisition and Representation
Image Acquisition and Representation
 
Digital Image Processing_ ch1 introduction-2003
Digital Image Processing_ ch1 introduction-2003Digital Image Processing_ ch1 introduction-2003
Digital Image Processing_ ch1 introduction-2003
 
Texture in image processing
Texture in image processing Texture in image processing
Texture in image processing
 
Digital Image Processing - Image Enhancement
Digital Image Processing  - Image EnhancementDigital Image Processing  - Image Enhancement
Digital Image Processing - Image Enhancement
 
Lecture 4 Relationship between pixels
Lecture 4 Relationship between pixelsLecture 4 Relationship between pixels
Lecture 4 Relationship between pixels
 
Image enhancement techniques a review
Image enhancement techniques   a reviewImage enhancement techniques   a review
Image enhancement techniques a review
 
Introduction to Digital Image Processing
Introduction to Digital Image ProcessingIntroduction to Digital Image Processing
Introduction to Digital Image Processing
 
Image classification
Image classificationImage classification
Image classification
 
Image classification
Image classificationImage classification
Image classification
 
Data input and transformation
Data input and transformationData input and transformation
Data input and transformation
 
Raster data and Vector data
Raster data and Vector dataRaster data and Vector data
Raster data and Vector data
 
Change detection techniques
Change detection techniquesChange detection techniques
Change detection techniques
 
Feature detection and matching
Feature detection and matchingFeature detection and matching
Feature detection and matching
 
TIN IN GIS
TIN IN GISTIN IN GIS
TIN IN GIS
 
Image processing fundamentals
Image processing fundamentalsImage processing fundamentals
Image processing fundamentals
 
Map algebra
Map algebraMap algebra
Map algebra
 
Digital image classification
Digital image classificationDigital image classification
Digital image classification
 
Image enhancement techniques
Image enhancement techniquesImage enhancement techniques
Image enhancement techniques
 
Image enhancement
Image enhancementImage enhancement
Image enhancement
 

Similaire à Basics of image processing & analysis

2015.basicsof imageanalysischapter2 (1)
2015.basicsof imageanalysischapter2 (1)2015.basicsof imageanalysischapter2 (1)
2015.basicsof imageanalysischapter2 (1)moemi1
 
Sign Language Recognition Using Image Processing For Mute People
Sign Language Recognition Using Image Processing For Mute PeopleSign Language Recognition Using Image Processing For Mute People
Sign Language Recognition Using Image Processing For Mute Peoplepaperpublications3
 
Digital imaging in dentistry / orthodontics courses
Digital imaging in dentistry / orthodontics courses Digital imaging in dentistry / orthodontics courses
Digital imaging in dentistry / orthodontics courses Indian dental academy
 
Noise recognition in digital image
Noise recognition in digital imageNoise recognition in digital image
Noise recognition in digital imageReyad Hossain
 
Noise recognition in digital image
Noise recognition in digital imageNoise recognition in digital image
Noise recognition in digital imageReyad Hossain
 
Multimedia digital images
 Multimedia  digital images Multimedia  digital images
Multimedia digital imagesMohammad Dwikat
 
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)IJERD Editor
 
Intensity Enhancement in Gray Level Images using HSV Color Coding Technique
Intensity Enhancement in Gray Level Images using HSV Color Coding TechniqueIntensity Enhancement in Gray Level Images using HSV Color Coding Technique
Intensity Enhancement in Gray Level Images using HSV Color Coding TechniqueIRJET Journal
 
Recent advances digital imaging /certified fixed orthodontic courses by India...
Recent advances digital imaging /certified fixed orthodontic courses by India...Recent advances digital imaging /certified fixed orthodontic courses by India...
Recent advances digital imaging /certified fixed orthodontic courses by India...Indian dental academy
 
Recent advances digital imaging /certified fixed orthodontic courses by India...
Recent advances digital imaging /certified fixed orthodontic courses by India...Recent advances digital imaging /certified fixed orthodontic courses by India...
Recent advances digital imaging /certified fixed orthodontic courses by India...Indian dental academy
 

Similaire à Basics of image processing & analysis (20)

2015.basicsof imageanalysischapter2 (1)
2015.basicsof imageanalysischapter2 (1)2015.basicsof imageanalysischapter2 (1)
2015.basicsof imageanalysischapter2 (1)
 
Chapter-1.pptx
Chapter-1.pptxChapter-1.pptx
Chapter-1.pptx
 
Digital imaging
Digital imagingDigital imaging
Digital imaging
 
Sign Language Recognition Using Image Processing For Mute People
Sign Language Recognition Using Image Processing For Mute PeopleSign Language Recognition Using Image Processing For Mute People
Sign Language Recognition Using Image Processing For Mute People
 
Recent advances digital imaging
Recent advances digital imaging Recent advances digital imaging
Recent advances digital imaging
 
Digital imaging (2)
Digital imaging (2)Digital imaging (2)
Digital imaging (2)
 
Digital imaging
Digital imagingDigital imaging
Digital imaging
 
Digital imaging in dentistry / orthodontics courses
Digital imaging in dentistry / orthodontics courses Digital imaging in dentistry / orthodontics courses
Digital imaging in dentistry / orthodontics courses
 
MultimediaLecture5.pptx
MultimediaLecture5.pptxMultimediaLecture5.pptx
MultimediaLecture5.pptx
 
Noise recognition in digital image
Noise recognition in digital imageNoise recognition in digital image
Noise recognition in digital image
 
Noise recognition in digital image
Noise recognition in digital imageNoise recognition in digital image
Noise recognition in digital image
 
Multimedia digital images
 Multimedia  digital images Multimedia  digital images
Multimedia digital images
 
h.pdf
h.pdfh.pdf
h.pdf
 
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
 
ch1ip.ppt
ch1ip.pptch1ip.ppt
ch1ip.ppt
 
Intensity Enhancement in Gray Level Images using HSV Color Coding Technique
Intensity Enhancement in Gray Level Images using HSV Color Coding TechniqueIntensity Enhancement in Gray Level Images using HSV Color Coding Technique
Intensity Enhancement in Gray Level Images using HSV Color Coding Technique
 
Unit ii
Unit iiUnit ii
Unit ii
 
[IJET-V1I6P10] Authors: Mr.B.V.Sathish Kumar, M.Tech Scholar G.Sumalatha
[IJET-V1I6P10] Authors: Mr.B.V.Sathish Kumar, M.Tech Scholar G.Sumalatha [IJET-V1I6P10] Authors: Mr.B.V.Sathish Kumar, M.Tech Scholar G.Sumalatha
[IJET-V1I6P10] Authors: Mr.B.V.Sathish Kumar, M.Tech Scholar G.Sumalatha
 
Recent advances digital imaging /certified fixed orthodontic courses by India...
Recent advances digital imaging /certified fixed orthodontic courses by India...Recent advances digital imaging /certified fixed orthodontic courses by India...
Recent advances digital imaging /certified fixed orthodontic courses by India...
 
Recent advances digital imaging /certified fixed orthodontic courses by India...
Recent advances digital imaging /certified fixed orthodontic courses by India...Recent advances digital imaging /certified fixed orthodontic courses by India...
Recent advances digital imaging /certified fixed orthodontic courses by India...
 

Plus de Mohsin Siddique

Engineering Economics: Solved exam problems [ch1-ch4]
Engineering Economics: Solved exam problems [ch1-ch4]Engineering Economics: Solved exam problems [ch1-ch4]
Engineering Economics: Solved exam problems [ch1-ch4]Mohsin Siddique
 
Basics of image processing using MATLAB
Basics of image processing using MATLABBasics of image processing using MATLAB
Basics of image processing using MATLABMohsin Siddique
 
Particle image velocimetry
Particle image velocimetryParticle image velocimetry
Particle image velocimetryMohsin Siddique
 
Chapter 5 coastal structures
Chapter  5 coastal structuresChapter  5 coastal structures
Chapter 5 coastal structuresMohsin Siddique
 
Chapter 4 Introduction to beach processes and management strategies
Chapter  4 Introduction to beach processes and management strategiesChapter  4 Introduction to beach processes and management strategies
Chapter 4 Introduction to beach processes and management strategiesMohsin Siddique
 
Chapter 3 linear wave theory and wave propagation
Chapter  3 linear wave theory and wave propagationChapter  3 linear wave theory and wave propagation
Chapter 3 linear wave theory and wave propagationMohsin Siddique
 
Chapter 2 wave and tides with examples
Chapter  2 wave and tides with examplesChapter  2 wave and tides with examples
Chapter 2 wave and tides with examplesMohsin Siddique
 
Chapter 1 introduction to coastal engineering and management strategies
Chapter  1 introduction to coastal engineering and management strategiesChapter  1 introduction to coastal engineering and management strategies
Chapter 1 introduction to coastal engineering and management strategiesMohsin Siddique
 
Chapter 6 concrete dam engineering with examples
Chapter 6 concrete dam engineering with examplesChapter 6 concrete dam engineering with examples
Chapter 6 concrete dam engineering with examplesMohsin Siddique
 
Chapter 5 drop sturcutures
Chapter 5 drop sturcuturesChapter 5 drop sturcutures
Chapter 5 drop sturcuturesMohsin Siddique
 
Chapter 4 seepage theories
Chapter 4 seepage theoriesChapter 4 seepage theories
Chapter 4 seepage theoriesMohsin Siddique
 
Chapter 3 channel design
Chapter 3  channel designChapter 3  channel design
Chapter 3 channel designMohsin Siddique
 
Chapter 2 open channel hydraulics
Chapter 2 open channel hydraulicsChapter 2 open channel hydraulics
Chapter 2 open channel hydraulicsMohsin Siddique
 
Chapter 7 spillway and energy dissipators
Chapter 7 spillway and energy dissipatorsChapter 7 spillway and energy dissipators
Chapter 7 spillway and energy dissipatorsMohsin Siddique
 
Chapter 1 introduction to hydraulics structures history...
Chapter  1 introduction to hydraulics structures history...Chapter  1 introduction to hydraulics structures history...
Chapter 1 introduction to hydraulics structures history...Mohsin Siddique
 
Fluid MechanicsVortex flow and impulse momentum
Fluid MechanicsVortex flow and impulse momentumFluid MechanicsVortex flow and impulse momentum
Fluid MechanicsVortex flow and impulse momentumMohsin Siddique
 
Dymanics of open channel flow
Dymanics of open channel flowDymanics of open channel flow
Dymanics of open channel flowMohsin Siddique
 
Fluid MechanicsLosses in pipes dynamics of viscous flows
Fluid MechanicsLosses in pipes dynamics of viscous flowsFluid MechanicsLosses in pipes dynamics of viscous flows
Fluid MechanicsLosses in pipes dynamics of viscous flowsMohsin Siddique
 

Plus de Mohsin Siddique (20)

Engineering Economics: Solved exam problems [ch1-ch4]
Engineering Economics: Solved exam problems [ch1-ch4]Engineering Economics: Solved exam problems [ch1-ch4]
Engineering Economics: Solved exam problems [ch1-ch4]
 
Basics of image processing using MATLAB
Basics of image processing using MATLABBasics of image processing using MATLAB
Basics of image processing using MATLAB
 
Particle image velocimetry
Particle image velocimetryParticle image velocimetry
Particle image velocimetry
 
Chapter 5 coastal structures
Chapter  5 coastal structuresChapter  5 coastal structures
Chapter 5 coastal structures
 
Chapter 4 Introduction to beach processes and management strategies
Chapter  4 Introduction to beach processes and management strategiesChapter  4 Introduction to beach processes and management strategies
Chapter 4 Introduction to beach processes and management strategies
 
Chapter 3 linear wave theory and wave propagation
Chapter  3 linear wave theory and wave propagationChapter  3 linear wave theory and wave propagation
Chapter 3 linear wave theory and wave propagation
 
Chapter 2 wave and tides with examples
Chapter  2 wave and tides with examplesChapter  2 wave and tides with examples
Chapter 2 wave and tides with examples
 
Chapter 1 introduction to coastal engineering and management strategies
Chapter  1 introduction to coastal engineering and management strategiesChapter  1 introduction to coastal engineering and management strategies
Chapter 1 introduction to coastal engineering and management strategies
 
Chapter 6 concrete dam engineering with examples
Chapter 6 concrete dam engineering with examplesChapter 6 concrete dam engineering with examples
Chapter 6 concrete dam engineering with examples
 
Chapter 5 drop sturcutures
Chapter 5 drop sturcuturesChapter 5 drop sturcutures
Chapter 5 drop sturcutures
 
Chapter 4 seepage theories
Chapter 4 seepage theoriesChapter 4 seepage theories
Chapter 4 seepage theories
 
Chapter 3 channel design
Chapter 3  channel designChapter 3  channel design
Chapter 3 channel design
 
Chapter 2 open channel hydraulics
Chapter 2 open channel hydraulicsChapter 2 open channel hydraulics
Chapter 2 open channel hydraulics
 
Chapter 7 spillway and energy dissipators
Chapter 7 spillway and energy dissipatorsChapter 7 spillway and energy dissipators
Chapter 7 spillway and energy dissipators
 
Chapter 1 introduction to hydraulics structures history...
Chapter  1 introduction to hydraulics structures history...Chapter  1 introduction to hydraulics structures history...
Chapter 1 introduction to hydraulics structures history...
 
Buoyancy and floatation
Buoyancy and floatationBuoyancy and floatation
Buoyancy and floatation
 
Fluid MechanicsVortex flow and impulse momentum
Fluid MechanicsVortex flow and impulse momentumFluid MechanicsVortex flow and impulse momentum
Fluid MechanicsVortex flow and impulse momentum
 
Dymanics of open channel flow
Dymanics of open channel flowDymanics of open channel flow
Dymanics of open channel flow
 
Fluid MechanicsLosses in pipes dynamics of viscous flows
Fluid MechanicsLosses in pipes dynamics of viscous flowsFluid MechanicsLosses in pipes dynamics of viscous flows
Fluid MechanicsLosses in pipes dynamics of viscous flows
 
Flow measurement
Flow measurementFlow measurement
Flow measurement
 

Dernier

Multicomponent Spiral Wound Membrane Separation Model.pdf
Multicomponent Spiral Wound Membrane Separation Model.pdfMulticomponent Spiral Wound Membrane Separation Model.pdf
Multicomponent Spiral Wound Membrane Separation Model.pdfGiovanaGhasary1
 
Design of Clutches and Brakes in Design of Machine Elements.pptx
Design of Clutches and Brakes in Design of Machine Elements.pptxDesign of Clutches and Brakes in Design of Machine Elements.pptx
Design of Clutches and Brakes in Design of Machine Elements.pptxYogeshKumarKJMIT
 
How to Write a Good Scientific Paper.pdf
How to Write a Good Scientific Paper.pdfHow to Write a Good Scientific Paper.pdf
How to Write a Good Scientific Paper.pdfRedhwan Qasem Shaddad
 
Test of Significance of Large Samples for Mean = µ.pptx
Test of Significance of Large Samples for Mean = µ.pptxTest of Significance of Large Samples for Mean = µ.pptx
Test of Significance of Large Samples for Mean = µ.pptxHome
 
UNIT4_ESD_wfffffggggggggggggith_ARM.pptx
UNIT4_ESD_wfffffggggggggggggith_ARM.pptxUNIT4_ESD_wfffffggggggggggggith_ARM.pptx
UNIT4_ESD_wfffffggggggggggggith_ARM.pptxrealme6igamerr
 
Best-NO1 Best Rohani Amil In Lahore Kala Ilam In Lahore Kala Jadu Amil In Lah...
Best-NO1 Best Rohani Amil In Lahore Kala Ilam In Lahore Kala Jadu Amil In Lah...Best-NO1 Best Rohani Amil In Lahore Kala Ilam In Lahore Kala Jadu Amil In Lah...
Best-NO1 Best Rohani Amil In Lahore Kala Ilam In Lahore Kala Jadu Amil In Lah...Amil baba
 
Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...
Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...
Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...amrabdallah9
 
A Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software SimulationA Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software SimulationMohsinKhanA
 
SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....
SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....
SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....santhyamuthu1
 
Clutches and brkesSelect any 3 position random motion out of real world and d...
Clutches and brkesSelect any 3 position random motion out of real world and d...Clutches and brkesSelect any 3 position random motion out of real world and d...
Clutches and brkesSelect any 3 position random motion out of real world and d...sahb78428
 
Transforming Process Safety Management: Challenges, Benefits, and Transition ...
Transforming Process Safety Management: Challenges, Benefits, and Transition ...Transforming Process Safety Management: Challenges, Benefits, and Transition ...
Transforming Process Safety Management: Challenges, Benefits, and Transition ...soginsider
 
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docxSUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docxNaveenVerma126
 
Engineering Mechanics Chapter 5 Equilibrium of a Rigid Body
Engineering Mechanics  Chapter 5  Equilibrium of a Rigid BodyEngineering Mechanics  Chapter 5  Equilibrium of a Rigid Body
Engineering Mechanics Chapter 5 Equilibrium of a Rigid BodyAhmadHajasad2
 
solar wireless electric vechicle charging system
solar wireless electric vechicle charging systemsolar wireless electric vechicle charging system
solar wireless electric vechicle charging systemgokuldongala
 
EPE3163_Hydro power stations_Unit2_Lect2.pptx
EPE3163_Hydro power stations_Unit2_Lect2.pptxEPE3163_Hydro power stations_Unit2_Lect2.pptx
EPE3163_Hydro power stations_Unit2_Lect2.pptxJoseeMusabyimana
 
cloud computing notes for anna university syllabus
cloud computing notes for anna university syllabuscloud computing notes for anna university syllabus
cloud computing notes for anna university syllabusViolet Violet
 
The relationship between iot and communication technology
The relationship between iot and communication technologyThe relationship between iot and communication technology
The relationship between iot and communication technologyabdulkadirmukarram03
 

Dernier (20)

Lecture 2 .pdf
Lecture 2                           .pdfLecture 2                           .pdf
Lecture 2 .pdf
 
Lecture 4 .pdf
Lecture 4                              .pdfLecture 4                              .pdf
Lecture 4 .pdf
 
Multicomponent Spiral Wound Membrane Separation Model.pdf
Multicomponent Spiral Wound Membrane Separation Model.pdfMulticomponent Spiral Wound Membrane Separation Model.pdf
Multicomponent Spiral Wound Membrane Separation Model.pdf
 
Design of Clutches and Brakes in Design of Machine Elements.pptx
Design of Clutches and Brakes in Design of Machine Elements.pptxDesign of Clutches and Brakes in Design of Machine Elements.pptx
Design of Clutches and Brakes in Design of Machine Elements.pptx
 
How to Write a Good Scientific Paper.pdf
How to Write a Good Scientific Paper.pdfHow to Write a Good Scientific Paper.pdf
How to Write a Good Scientific Paper.pdf
 
Test of Significance of Large Samples for Mean = µ.pptx
Test of Significance of Large Samples for Mean = µ.pptxTest of Significance of Large Samples for Mean = µ.pptx
Test of Significance of Large Samples for Mean = µ.pptx
 
UNIT4_ESD_wfffffggggggggggggith_ARM.pptx
UNIT4_ESD_wfffffggggggggggggith_ARM.pptxUNIT4_ESD_wfffffggggggggggggith_ARM.pptx
UNIT4_ESD_wfffffggggggggggggith_ARM.pptx
 
Best-NO1 Best Rohani Amil In Lahore Kala Ilam In Lahore Kala Jadu Amil In Lah...
Best-NO1 Best Rohani Amil In Lahore Kala Ilam In Lahore Kala Jadu Amil In Lah...Best-NO1 Best Rohani Amil In Lahore Kala Ilam In Lahore Kala Jadu Amil In Lah...
Best-NO1 Best Rohani Amil In Lahore Kala Ilam In Lahore Kala Jadu Amil In Lah...
 
Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...
Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...
Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...
 
A Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software SimulationA Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software Simulation
 
Présentation IIRB 2024 Marine Cordonnier.pdf
Présentation IIRB 2024 Marine Cordonnier.pdfPrésentation IIRB 2024 Marine Cordonnier.pdf
Présentation IIRB 2024 Marine Cordonnier.pdf
 
SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....
SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....
SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....
 
Clutches and brkesSelect any 3 position random motion out of real world and d...
Clutches and brkesSelect any 3 position random motion out of real world and d...Clutches and brkesSelect any 3 position random motion out of real world and d...
Clutches and brkesSelect any 3 position random motion out of real world and d...
 
Transforming Process Safety Management: Challenges, Benefits, and Transition ...
Transforming Process Safety Management: Challenges, Benefits, and Transition ...Transforming Process Safety Management: Challenges, Benefits, and Transition ...
Transforming Process Safety Management: Challenges, Benefits, and Transition ...
 
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docxSUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
 
Engineering Mechanics Chapter 5 Equilibrium of a Rigid Body
Engineering Mechanics  Chapter 5  Equilibrium of a Rigid BodyEngineering Mechanics  Chapter 5  Equilibrium of a Rigid Body
Engineering Mechanics Chapter 5 Equilibrium of a Rigid Body
 
solar wireless electric vechicle charging system
solar wireless electric vechicle charging systemsolar wireless electric vechicle charging system
solar wireless electric vechicle charging system
 
EPE3163_Hydro power stations_Unit2_Lect2.pptx
EPE3163_Hydro power stations_Unit2_Lect2.pptxEPE3163_Hydro power stations_Unit2_Lect2.pptx
EPE3163_Hydro power stations_Unit2_Lect2.pptx
 
cloud computing notes for anna university syllabus
cloud computing notes for anna university syllabuscloud computing notes for anna university syllabus
cloud computing notes for anna university syllabus
 
The relationship between iot and communication technology
The relationship between iot and communication technologyThe relationship between iot and communication technology
The relationship between iot and communication technology
 

Basics of image processing & analysis

  • 1. Image Processing Date: INSTRUCTOR DR. MOHSIN SIDDIQUE ASSIST. PROFESSOR DEPARTMENT OF CIVIL & ENV ENG.
  • 2.  Image processing actions can be grouped into three sub-areas (Prats- Montalban et al. 2011):  Image compression, which reduces the memory requirements by removing the redundancy present in the image, that is, the image information which is not perceptible to the human eye.  Image preprocessing, which consists of improving the visual quality of the image by reducing noise, pixel calibration and standardization, enhancing the edge detection, and making the image analysis step more reliable based on objective and well established criteria. The term image preprocessing, in general, is referred to all manipulations on an image, each of which produces a new image.  Image analysis, which usually returns numeric values and/or graphical information about the image characteristics that are suited for classification, defect detection, or prediction of some of the quality properties of the imaged object. The term image analysis is used when the output is a number or decision, not an image. Image processing 2
  • 3.  Image: A reproduction or imitation of the form of a person or thing.  A 2D matrix of intensity (grey or color) values.  Images typically generated by illuminating a scene and absorbing energy reflected by scene objects Image & Pixel 3 Pixel: A pixel is generally thought of as the smallest single component of a digital image. In above image, 1 small square represents an image pixel
  • 4.  A common type of solid-state detector in current use is the charge coupled device (CCD).  At a specific pixel location, the CCD element is exposed to incident light energy and it builds up an electric charge proportional to the intensity of the incident light.  The electric charge is subsequently amplified and converted from analog to digital form. Image creation: Digital Imaging Device 4
  • 5. An example of the process for creation of a digital image (B. Park, R. Lu , 2015) Image creation 5
  • 6. Digital Format of Image Pixels are the smallest units of an image 6
  • 7.  Common image formats include: 1 values per point/pixel (B&W or Grayscale) 3 values per point/pixel (Red, Green, and Blue) 4 values per point/pixel (Red, Green, Blue, + “Alpha” or Opacity) Digital Image Format
  • 8. What is light? 8 The visible portion of the electromagnetic (EM) spectrum. It occurs between wavelengths of approximately 400 and 700 nanometers. Electromagnetic (EM) Spectrum
  • 10. The Visible Spectrum The color of an object is defined by the color of the light that it reflects . Thus a “blue” object is “blue” because it reflects blue light. Intermediate colors are formed when an object reflects two or more of the additive primaries. 10
  • 11.  Radar imaging (radio waves)  Magnetic Resonance Imaging (MRI) (Radio waves)  Microwave imaging  Infrared imaging  Photographs  Ultraviolet imaging telescopes  X‐rays and Computed tomography  Positron emission tomography (gamma rays)  Ultrasound (not EM waves) Images from EM spectrum 11
  • 12. Basic Image Measurements 12  Spatial resolution-pixel per unit dimension ppi, dpi, & lpi etc  Pixel bit depth or color depth or Intensity level resolution- number of shades that can actually be represented by the amount of information saved for each pixel  Saturation and noise
  • 13.  Spatial resolution is defined as the rate, or number of times, at which an image is sampled during the acquisition or imaging process. More specifically, it is the frequency of pixels used to capture sample shades in the space of the object being digitized.  Generally, more pixels per unit dimension means a higher resolution, but the overall image quality cannot be determined by spatial resolution alone.  Typical array sizes in pixels (or pixel resolution) in many imaging sensors vary from 640x480 to 2,048x1,536 pixels or even higher.  For reference human vision is >100 million pixels.  Quantitatively, spatial resolution can be described as:(Gonzales and Woods 2008). ppi (pixels per inch), dpi (dots per inch), and lpi (line pairs per inch)  ppi is commonly used for images while dpi and lpi are considered printing terms. Resolution (spatial resolution) 13
  • 14. Resolution (spatial resolution) Images taken from Gonzalez & Woods, Digital Image Processing (2002) 14
  • 16.  Tone can be defined as each distinguishable variation from white to black.  Color may be defined as each distinguishable variation on an image produced by a multitude of combinations of hue (color), value (lightness-intensity) and chroma (saturation-highest intensity).  If there is not sufficient contrast between an object and it's background to permit, at least, detection there can be no identification.  While a human interpreter may only be able to distinguish between ten and twenty shades of grey; interpreters can distinguish at least 100 times more variations of color on color photography than shades of grey on black and white photography. Tone/Color 16
  • 17.  Saturation: Highest intensity (shade) value above which color is washed out  Noise: grainy texture pattern Saturation and noise 17 Images taken from Gonzalez & Woods, Digital Image Processing (2002)
  • 18. Pixel bit depth or color depth or Intensity level resolution 18  It is the number of intensity levels (shades) used to represent the image  The more intensity levels used, the finer the level of detail discernable in an image.  Intensity level resolution usually given in terms of number of bits used to store each intensity level Images taken from Gonzalez & Woods, Digital Image Processing (2002) 256 grey levels, (8bit per pixel)
  • 19. Pixel bit depth or color depth or Intensity level resolution 19  Computers work on a binary system; each bit of data is either 1 or 0.  Each pixel in a raster image is represented by a string of binary digits and the number of digits is known as the bit depth. Hence, a one-bit image can assign only one of two values to a single pixel: 0 or 1 (black or white).  An 8-bit (28) gray scale image can assign one of 256 colors to a single pixel. A 24-bit (2(3x8) RGB image (8-bits each for red, green and blue color channels) can assign one of 16.8 million colors to a single pixel.  The following formula can be used to calculate the shades using bit depth Number of Shades = 2x, where x = the bit depth  To achieve a desired bit depth without any data loss, it is necessary to digitize a photograph at a higher bit depth and then scale down to the desired bit depth after any image processing has occurred
  • 20.  Several different systems are used to represent color images. The most common are: RGB (additive color system), CMYK (subtractive color system), HSV and the CIELAB color space.  The terms color space and color profile can often be used interchangeably, since a profile is a description or numerical model of a specific color space.  There are two types of profiles: matrix-based and table-based. Matrix-based profiles use mathematical formulas to describe the 3D color spaces Table-based profiles, as the name implies, use a large table of sample points called a Look-Up Table or LUT to define the 3D color space Color representation 20
  • 21.  RGB is a color model that uses the three primary (red, green, blue) additive colors, which can be mixed to make all other colors  CMYK (CMY) is a color model based on subtracting light. The cyan (C), magenta (M), yellow (Y) and key or black (k) are the basic colors for a subtractive model, and represent the complements of the three primary colors Color representation 21
  • 23.  File types are used to encode digital images, allowing for compression and storage.  There are hundreds of image file formats. For examples Tagged Image File Format (TIFF) Graphics Interchange Format (GIF) Portable Network Graphics (PNG) JPEG, BMP, Portable Bitmap Format (PBM), etc  Image pixel values can be: Grayscale: 0 – 255 range Binary: 0 or 1 Color: RGB colors in 0‐255 range (or other color model) File types 23
  • 24.  Binary Images: In a binary image, each pixel assumes one of only two discrete values: 1 or 0.  Indexed Images: An indexed image consists of an array and a colormap matrix.  Grayscale Images: A grayscale image (also called gray-scale, gray scale, or gray-level) is a data matrix whose values represent intensities within some range.  Truecolor Images: A truecolor image is an image in which each pixel is specified by three values — one each for the red, blue, and green components of the pixel's color. Type of digital images 24
  • 25.  If a color image has to be converted into an intensity or grayscale image, the following equations can be used. One alternative is the simple average of the R, G, B color channels:  Another equation, which takes into account the luminance perception of the human eye, is Type of digital images 25
  • 26. Image processing (or pre-processing)  Algorithms that alter an input image to create new image  It involves a series of image operations to enhance the quality of a digital image so as to remove defects such as geometric distortion, improper focus, repetitive noise, non-uniform lighting and camera motion. 26
  • 27. Image processing ( or pre-processing) An overview of the operational steps for a image processing (machine vision system) (B. Park, R. Lu , 2015) 27
  • 28.  The operations that can be performed on digital images include: point, local or neighborhood, and global operations.  Point operations transform pixels without regard to neighboring pixels. The gray value of the output image at a particular pixel depends only on the gray value of the same pixel in the input image.  A local or neighborhood operation or mask operation generates an output pixel whose value depends on the pixel values in a neighborhood of the corresponding input point.  An operation is a global operation if the output value at specific coordinate is dependent on all the values in the input images. Image processing ( or pre-processing) 28
  • 29.  Image enhancement is the process of improving the visual appearance of digital images.  It is usually done by contrast enhancement or histogram manipulation.  Contrast adjustment remaps image intensity values to the full display range of the data type. An image with good contrast has sharp differences between black and white. Image enhancement 29 To illustrate, the image on the left has poor contrast, with intensity values limited to the middle portion of the range. The image on the right has higher contrast, with intensity values that fill the entire intensity range [0, 255]. In the high contrast image, highlights look brighter and shadows look darker (ref. https://www.mathworks.com/)
  • 31.  Histogram manipulation  Concept of an image histogram.  Mathematically, histogram is a graphical presentation of the frequency count of the occurrence of each intensity (brightness value) in an image.  The brightness values (i.e. 0-255) are displayed along the x-axis of the graph.  The frequency of occurrence of each of these values in the image is shown on the y-axis Image enhancement 31 There is no single enhancement procedure which is best Best one is that which best displays the features of interest to the Analyst
  • 32.  Linear stretch: stretch using minimum and maximum values.  Histogram equalization: stretch using a nonlinear function derived from distribution of intensities.  Density slicing (pseud coloring): (Introducing color to a single-band Image) divide the range of values in a single band by assigning each interval into a color. Image enhancement Density slicing (pseud coloring) Linear stretch Histogram equalization 32
  • 34.  All arithmetic operations performed on matrices may be performed on images. Arithmetic operations between images are array operations carried out between corresponding pixel pairs. Hence, the images normally have to be of the same size. These operators are frequently used for reducing noise and image enhancement.  The four arithmetic operations are as follows: Addition Subtraction Multiplication Division Arithmetic operations 34
  • 35.  Filtering is a process that selectively enhances or suppresses particular frequency (wavelengths) within an image.  Two approaches to digitally filtering data: Filtering in the spatial domain  Convolution, correlation etc Filtering in the frequency domain  Edge detection and Enhancement  Fourier transform  Wavelet transform Filtering Techniques 35
  • 36.  Low pass filters: e.g., Average (mean), median, and Gaussian filters Removes high frequency. Low pass filtering causes blurring  High pass filters: e.g., Gradient and Laplacian kernels Opposite of low pass filtering: eliminate low frequency values. High pass filtering causes image sharpening Filtering Techniques 36 Original Low pass High pass
  • 37.  With spatial image filtering technique, a window of finite size and shape is scanned across the entire image, transforming the local intensities in the output image. The window with its weights is called the convolution kernel or filter mask. Filtering Techniques: Spatial filtering 37 https://towardsdatascience.com/convolu tion-vs-correlation-af868b6b4fb5
  • 38.  Two main linear spatial filtering methods are correlation and convolution.  Correlation is the process of moving a filter mask over the image and computing the sum of products at each location  The mechanisms of convolution are the same, except that the filter is first rotated by 180o  Correlation and convolution yield the same result when the filter mask is symmetric. However, basic image processing techniques are mainly based on convolution. Filtering Techniques: Spatial filtering 38
  • 39.  All smoothing filters build a weighted average of the surrounding pixels, and some of them also use the center pixel itself.  Averaging and Gaussian filters are linear filters often used for noise reduction with their operation causing a smoothing in the image but having the effect of blurring edges. Filtering Techniques: Image smoothing and blurring Examples of averaging filter using masks [3x3] and [9x9] (B. Park, R. Lu , 2015) Examples of Gaussian filter using masks [3x3] and [9x9] (B. Park, R. Lu , 2015) 39
  • 40.  Sometimes, non-linear operations on neighborhoods yield better results. An example is the use of a median filter to remove noise. Median filtering replaces each pixel by the median in a neighborhood around the pixel. Filtering Techniques: Image smoothing and blurring Example of median filter using a kernel [33]: the input image (left) contains Gaussian noise, and the noise is removed in the resultant image (right) after 33 median filtering (B. Park, R. Lu , 2015) 40
  • 41.  An edge is an area of an image characterized by sharp changes in gray-level or brightness.  The process of edge detection attenuates high fluctuations in color, i.e., dramatic change in intensity. In the frequency domain, this process refers to the attenuation of high frequencies. Filtering Techniques: Edge Detection and Enhancement 41 https://web.cs.wpi.edu/~emmanuel/courses/cs545/S14/
  • 42.  Among the families of edge detection filters are: Gradient filters, Laplacian, and wavelet transform (Klinger 2003).  Both gradient and Laplacian kernels are of the high-pass filter, which operates by differencing adjacent pixels, because the sharp edges can be described by high frequencies. Filtering Techniques: Edge Detection and Enhancement Edge detection 42
  • 43.  Image segmentation is one of the most important steps in the entire image processing technique, as subsequent extracted data are highly dependent on the accuracy of this operation.  Its main aim is to divide an image into regions that have a strong correlation with objects or area of interest (i.e., ROIs).  Segmentation can be achieved by three different techniques (Sonka et al. 1999; Sun, 2000): Thresholding, Edge-based segmentation, and Region-based segmentation Image segmentation 43
  • 44.  Threshold can be applied to an image to isolate a feature represented by specific range of digital numbers (DN). Image segmentation: Thresholding  To calculate the area of lakes, DNs not representing water are a distraction  Highest DN for water is 35 and is used as the threshold  All DNs > 35 are assigned 255 (saturated to white);  DNs<=35 are assigned zero (black)  The lakes are much more prominent in the image after 44
  • 45. Image segmentation Image segmentation process for bananas (B. Park, R. Lu , 2015) 45
  • 46.  Digital image classification is the process of assigning pixels to classes.  Since, measured reflection values in an image depend on the local characteristics of the object; in other words there is a relationship between objects and measured reflection values.  Therefore, by comparing pixels each other, it is possible to assemble groups of similar pixels into classes and pixels within the same class are spectrally similar each other, however, in practice, they have some variability within classes. Digital image classification 46
  • 47. Comments…. Questions…. Suggestions…. References: B. Park, R. Lu , (2015), Chapter 2: DOI: 10.1007/978-1-4939-2836-1_2 Gonzalez & Woods, Digital Image Processing (2002) https://web.cs.wpi.edu/~emmanuel/courses/cs545/S14/ 47 Thank you !