1. Statistical analysis of images involves evaluating global and local contrast through measures like mean, variance, and standard deviation calculated from the image histogram.
2. The histogram of an image shows the number of pixels at each intensity level, while the normalized histogram estimates the probability of each intensity level.
3. Contrast enhancement techniques like histogram equalization aim to produce a uniform distribution of intensity levels across the histogram in order to increase contrast.
Image Segmentation
Types of Image Segmentation
Semantic Segmentation
Instance Segmentation
Types of Image Segmentation Techniques based on the image properties:
Threshold Method.
Edge Based Segmentation.
Region-Based Segmentation.
Clustering Based Segmentation.
Watershed Based Method.
Artificial Neural Network Based Segmentation.
Image Segmentation
Types of Image Segmentation
Semantic Segmentation
Instance Segmentation
Types of Image Segmentation Techniques based on the image properties:
Threshold Method.
Edge Based Segmentation.
Region-Based Segmentation.
Clustering Based Segmentation.
Watershed Based Method.
Artificial Neural Network Based Segmentation.
Lecture 1 for Digital Image Processing (2nd Edition)Moe Moe Myint
-What is Digital Image Processing?
-The Origins of Digital Image Processing
-Examples of Fields that Use Digital Image Processing
-Fundamentals Steps in Digital Image Processing
-Components of an Image Processing System
Image processing, Noise, Noise Removal filtersKuppusamy P
Basics of images, Digital Images, Noise, Noise Removal filters
Reference:
Richard Szeliski, Computer Vision: Algorithms and Applications, Springer 2010
COM2304: Intensity Transformation and Spatial Filtering – I (Intensity Transf...Hemantha Kulathilake
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
Lecture 1 for Digital Image Processing (2nd Edition)Moe Moe Myint
-What is Digital Image Processing?
-The Origins of Digital Image Processing
-Examples of Fields that Use Digital Image Processing
-Fundamentals Steps in Digital Image Processing
-Components of an Image Processing System
Image processing, Noise, Noise Removal filtersKuppusamy P
Basics of images, Digital Images, Noise, Noise Removal filters
Reference:
Richard Szeliski, Computer Vision: Algorithms and Applications, Springer 2010
COM2304: Intensity Transformation and Spatial Filtering – I (Intensity Transf...Hemantha Kulathilake
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
Fond of wines? want to trace back the history of wines?
This ppt covers the details of the wines from New Zealand. The viniculture and viticulture process. The old and New World wines.
Чтобы более подробно ознакомиться с работой сервиса, прочтите эту простую инструкцию. Она пригодится Вам, чтобы узнать, как максимально эффективно использовать Копикот.ру и всегда получать кэшбэк.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
Image Acquisition and Representation
A Simple Image Formation Model
Image Sampling and Quantization
Image Interpolation
Image quantization
Nearest Neighbor Interpolation
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
Model Attribute Check Company Auto PropertyCeline George
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Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
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Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
2. Statistical Characteristics of Images
• Statistical analysis of images is very important
for
global contrast evaluation and its
enhancement
local contrast evaluation and its
enhancement
estimation of noise
2
3. Histogram
• The histogram of an NxM digital image with
intensity levels {0,1,…,L-1} is a discrete
function
where is the kth intensity value and is
the number of pixels in the image with
intensity
• Evidently,
3
( ) ; 0,1,..., 1k kh r n k L= = −
kr kn
kr
( )
1
0
L
k
k
h r MN
−
=
=∑
4. Normalized Histogram
• The normalized histogram of an NxM image
with intensity levels {0,1,…,L-1} is its histogram
normalized by the product NM (the number
of pixels in the image). The normalized
histogram is an estimate of the probability of
occurrence of each intensity level in the
image:
• Evidently,
4
( )
( ); 0,1,..., 1k k
k
h r
p r k L
NM
= = −
( )
1
0
1
L
k
k
p r
−
=
=∑
5. Mean (Statistically Correct
Definition)
• The global mean value of an image is the
average intensity of all the pixels in the image
• Let A be NxM image. Then its global mean
where is the kth intensity value, is
the probability of occurrence the intensity
5
( )
1
0
L
k k
k
m r p r
−
=
= ∑
kr ( )kp r
kr
6. Sampling Mean
• The global sampling mean value of an image is
the average intensity of all the pixels in the image
• Let A be NxM image. Then its global mean
• Evidently, the global sampling mean is equal to
the global mean (statistical). We will call them
both simply mean
6
( )
1 1
0 0
1
,
N M
i j
m A i j
NM
− −
= =
= ∑∑
7. Variance (Dispersion) –
Statistically Correct Definition
• Variance (Dispersion) is the second moment of
intensity about its mean
where m is the mean, is the kth intensity
value, is the probability of occurrence
the intensity
7
( ) ( )
1
22
2
0
( )
L
k k
k
mr r p rσ µ
−
=
= = −∑
kr
( )kp r
kr
8. Sampling Variance (Dispersion)
• Variance (Dispersion) is the second moment of
intensity about its mean
where m is the mean
• Evidently, the global sampling variance is equal
to the global variance (statistical). We will call
them both simply variance (dispersion)
8
( )( )
1 1
22
2
0 0
1
( ) ,
N M
i j
r A i j
NM
mσ µ
− −
= =
= = −∑∑
9. Standard Deviation
• Standard Deviation is the square root of the
variance
9
( ) ( )
( )( )
1 1
2
1
2 0 02
0
,
N M
L
i j
k k
k
A i j m
r m p r
NM
σ σ
− −
−
= =
=
−
= = − =
∑∑
∑
10. Importance of Mean,
Variance, and Standard Deviation)
• Mean is a measure of average intensity
• The closer is mean to the middle of the
dynamic range, the higher contrast should be
expected
• Variance (standard deviation) is a measure of
contrast in an image
• The larger is variance (standard deviation), the
higher is contrast
10
11. Signal-to-Noise Ratio (SNR)
• SNR is a measure , which is used to quantify how
much a signal has been corrupted by noise
• Pimage – image power, Pnoise – noise power
• Asignal – image amplitude, Anoise – noise amplitude
11
2
10 , ,
2
10 10
10log
10log 20log
image image
noise noise
image
DB signal DB noise DB
noise
image image
DB
noise noise
P A
SNR
P A
P
SNR P P
P
A A
SNR
A A
= =
= = −
= =
12. Signal-to-Noise Ratio (SNR)
• Since clean image and noise amplitudes are
unknown, to estimate SNR, the following
method is used
• where m is the mean and σ is the standard
deviation of an image
12
m
SNR
σ
≈
13. Signal-to-Noise Ratio (SNR)
• The Rose criterion says that if an image has
SNR>5, then a level of noise is so small that it
shall be considered negligible and the image shall
be considered clean. If SNR<5, then some noise
should be expected.
• The lower is SNR, the stronger is noise
• Small-detailed images (for example, satellite
images) may have lower SNR because the
presence of many small details always lead to the
higher standard deviation
13
14. Mean Square Error and Standard Deviation
Between Two Images
• For two NxM digital images A and B the mean
square error (deviation) (MSE) is defined as
follows:
• For two NxM digital images A and B the
standard deviation (the root mean square
error - RMSE) is defined as follows:
14
( ) ( )( )
1 1
2
0 0
1
, ,
N M
i j
MSE A i j B i j
NM
− −
= =
= −∑∑
( ) ( )( )
1 1
2
0 0
1
, ,
N M
i j
RMSE MSE A i j B i j
NM
− −
= =
= = −∑∑
15. Peak Signal-to-Noise Ratio (PSNR)
• PSNR is the ratio between the maximum
possible power of an image and a power of
corrupting noise
• To estimate PSNR of an image, it is necessary
to compare this image to an “ideal” clean
image with the maximum possible power
15
16. Peak Signal-to-Noise Ratio (PSNR)
• where L is the number of maximum possible
intensity levels (the minimum intensity level
suppose to be 0) in an image, MSE is the mean
square error, RMSE is the root mean square
error between a tested image and an “ideal”
image
16
2
10 10
( 1) 1
10log 20log
L L
PSNR
MSE RMSE
− −
=
17. Peak Signal-to-Noise Ratio (PSNR)
• PSNR is commonly used to estimate the efficiency
of filters, compressors, etc.
• How it works: a clean image should be distorted
by noise or compressed, respectively. Then a
noisy image, a filtered image or a compressed
image shall be compared to the clean one
(“ideal”) in terms of PSNR
• The larger is PSNR, the more efficient is a
corresponding filter or compression method, and
the fewer an analyzed image differs from the
“ideal” one
17
18. Peak Signal-to-Noise Ratio (PSNR)
• PSNR <30.0 (this corresponds to RMSE>11) is
commonly considered low. This means the presence of
clearly visible noise or smoothing of many edges
• PSNR > 30.0 (this corresponds to RMSE <8.6) is
commonly considered as acceptable (some noise is still
visible or small details are still smoothed)
• PSNR > 33.0 (this corresponds to RMSE <5.6) is
commonly considered as good
• PSNR > 35.0 (this correspond to RMSE <4.5) is
commonly considered as excellent (it is usually not
possible to find any visual distinction from the “ideal”
image)
18
19. Mean Absolute Error
between two images (MAE)
• Mean Absolute Error between two NxM digital
images A and B measures the absolute
closeness of these images to each other:
19
( ) ( )
1 1
0 0
1
, ,
N M
i j
MAE A i j B i j
NM
− −
= =
−∑∑
20. Contrast Enhancement
• If an actual dynamic range of an image
is too narrow, its histogram is also too narrow
and, as a result, this image has a low contrast
• To take care of this problem, the range of the
image should be extended and its histogram
should be equalized, to ensure that more
intensity values are presented in the image
and there are no sharp spikes in the histogram
20
{ }0 1 0, ,..., , ;k kr r r k L r r L<< − <<
21. Histogram Equalization
• The histogram of an image can be equalized
using the following transformation
where r is the initial intensity value, s is the
intensity value after the transformation
• Function T must be monotonically increasing
and limited
is a new range
21
0 ( ) ls T r s≤ ≤
0( ); ks T r r r r= ≤ ≤
{ }0 ,..., ls s
28. Linear Contrast Correction
• Linear contrast correction is a kind of
histogram equalization for an image with the
range ,
which produces an output image with the pre-
determined mean and standard deviation
28
;
0,1,...,
new new
i i new old
old old
s r m m
i k
σ σ
σ σ
= + −
=
{ }0 1, ,..., , 1kr r r k L≤ −