Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram processing
Using histogram statistics for image enhancement
Uses for Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram Processing
Basics of Spatial Filtering
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
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.
Digital Image Processing denotes the process of digital images with the use of digital computer. Digital images are contains various types of noises which are reduces the quality of images. Noises can be removed by various enhancement techniques. Image smoothing is a key technology of image enhancement, which can remove noise in images.
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.
Digital Image Processing denotes the process of digital images with the use of digital computer. Digital images are contains various types of noises which are reduces the quality of images. Noises can be removed by various enhancement techniques. Image smoothing is a key technology of image enhancement, which can remove noise in images.
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
Image Acquisition and Representation
A Simple Image Formation Model
Image Sampling and Quantization
Image Interpolation
Image quantization
Nearest Neighbor Interpolation
Adaptive Median Filters
Elements of visual perception
Representing Digital Images
Spatial and Intensity Resolution
cones and rods
Brightness Adaptation
Spatial and Intensity Resolution
Image Restoration And Reconstruction
Mean Filters
Order-Statistic Filters
Spatial Filtering: Mean Filters
Adaptive Filters
Adaptive Mean Filters
Adaptive Median Filters
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
3. Histograms
Histograms plots show many times (frequency) each
intensity value in image occurs
Example:
Image (left) has 256 distinct gray-levels (8bits)
Histogram (right) shows frequency (how many times)each
gray-level occurs
4. Histograms
Many cameras display real time histograms of scene
Helps avoid taking over-exposed pictures
Also easier to detect types of processing previously applied
to image
5. Histograms
E.g. L = 16, 10 pixels have intensity value = 2
Histograms: only statistical information
No indication of location of pixels
7. Histograms
Different images can have same histogram
3 images below have same histogram
Half of pixels are gray, half are white
Same histogram = same statistics
Distribution of intensities could be different
Can we reconstruct image from histogram?
8. Histograms
A histogram for a grayscale image with intensity values in
range I(x, y) [0, L—1] would contain exactly L entries
E.g. 8-bit grayscale image, L = 2^8 = 256
Each histogram entry is defined as:
h(i) = number of pixels with intensity i for all 0 <= i< L.
E.g: h(255) = number of pixels with intensity = 255
9. Image Contrast
The contrast of a grayscale image indicates how easily
objects in the image can be distinguished
High contrast image: many distinct intensity values
Low contrast: image uses few intensity values
12. Uses for Histogram Processing
Image enhancements
Image statistics
Image compression
Image segmentation
Simple to calculate in software
Economic hardware implementations
Popular tool in real-time image processing
A plot of this function for all values of k provides a global
description of the appearance of the image (gives useful
information for contrast enhancement)
13. Uses for Histogram Processing
Four basic image types and their corresponding
histograms
– Dark
– Light
– Low contrast
– High contrast
Histograms commonly
viewed in plots as
16. Dynamic Range
2/16/2018 16
• Dynamic Range: Number of distinct pixels in
image
• High dynamic range means very bright and very
dark parts in a single image (many distinct values)
18. Histogram Equalization
2/16/2018 18
The intensity levels in an image may be viewed as
random variables in the interval [0, L-1].
Let ( ) and ( ) denote the probability density
function (PDF) of random variables and .
r sp r p s
r s
19. 2/16/2018 19
Histogram Equalization
( ) 0 1s T r r L
. T(r) is a strictly monotonically increasing function
in the interval 0 -1;
. 0 ( ) -1 for 0 -1.
a
r L
b T r L r L
20. 2/16/2018 20
Histogram Equalization
. T(r) is a strictly monotonically increasing function
in the interval 0 -1;
. 0 ( ) -1 for 0 -1.
a
r L
b T r L r L
( ) 0 1s T r r L
( ) is continuous and differentiable.T r
( ) ( )s rp s ds p r dr
21. Histogram Equalization
0
( ) ( 1) ( )
r
rs T r L p w dw
2/16/2018 21
0
( )
( 1) ( )
r
r
ds dT r d
L p w dw
dr dr dr
( 1) ( )rL p r
( ) 1( ) ( )
( )
( 1) ( ) 1
r r r
s
r
p r dr p r p r
p s
L p rdsds L
dr
22. 2/16/2018 22
Example
2
Suppose that the (continuous) intensity values
in an image have the PDF
2
, for 0 r L-1
( 1)( )
0, otherwise
Find the transformation function for equalizing
the image histogra
r
r
Lp r
m.
23. 2/16/2018 23
Example
0
( ) ( 1) ( )
r
rs T r L p w dw
20
2
( 1)
( 1)
r w
L dw
L
2
1
r
L
24. 2/16/2018 24
Histogram Equalization
0
Continuous case:
( ) ( 1) ( )
r
rs T r L p w dw
0
Discrete values:
( ) ( 1) ( )
k
k k r j
j
s T r L p r
0 0
1
( 1) k=0,1,..., L-1
k k
j
j
j j
n L
L n
MN MN
25. 2/16/2018 25
Example: Histogram Equalization
Suppose that a 3-bit image (L=8) of size 64 × 64 pixels (MN = 4096) has the
intensity distribution shown in following table.
Get the histogram equalization transformation function and give the ps(sk)
for each sk.
26. 2/16/2018 26
Example: Histogram Equalization
0
0 0
0
( ) 7 ( ) 7 0.19 1.33r j
j
s T r p r
1
1
1 1
0
( ) 7 ( ) 7 (0.19 0.25) 3.08r j
j
s T r p r
3
2 3
4 5
6 7
4.55 5 5.67 6
6.23 6 6.65 7
6.86 7 7.00 7
s s
s s
s s
31. 2/16/2018 31
Histogram Matching
Histogram matching (histogram specification)
— generate a processed image that has a specified histogram
Let ( ) and ( ) denote the continous probability
density functions of the variables and . ( ) is the
specified probability density function.
Let be the random variable with the prob
r z
z
p r p z
r z p z
s
0
0
ability
( ) ( 1) ( )
Define a random variable with the probability
( ) ( 1) ( )
r
r
z
z
s T r L p w dw
z
G z L p t dt s
32. 2/16/2018 32
Histogram Matching
0
0
( ) ( 1) ( )
( ) ( 1) ( )
r
r
z
z
s T r L p w dw
G z L p t dt s
1 1
( ) ( )z G s G T r
33. 2/16/2018 33
Histogram Matching: Procedure
• Obtain pr(r) from the input image and then obtain the values of s
• Use the specified PDF and obtain the transformation function G(z)
• Mapping from s to z
0
( 1) ( )
r
rs L p w dw
0
( ) ( 1) ( )
z
zG z L p t dt s
1
( )z G s
34. 2/16/2018 34
Histogram Matching: Example
Assuming continuous intensity values, suppose that an image has the
intensity PDF
Find the transformation function that will produce an image whose
intensity PDF is
2
2
, for 0 -1
( 1)( )
0 , otherwise
r
r
r L
Lp r
2
3
3
, for 0 ( -1)
( ) ( 1)
0, otherwise
z
z
z L
p z L
35. Histogram Matching: Example
Find the histogram equalization transformation for the input image
Find the histogram equalization transformation for the specified histogram
The transformation function
20 0
2
( ) ( 1) ( ) ( 1)
( 1)
r r
r
w
s T r L p w dw L dw
L
2/16/2018 35
2 3
3 20 0
3
( ) ( 1) ( ) ( 1)
( 1) ( 1)
z z
z
t z
G z L p t dt L dt s
L L
2
1
r
L
1/32
1/3 1/32 2 2
( 1) ( 1) ( 1)
1
r
z L s L L r
L
36. 2/16/2018 36
Histogram Matching: Discrete Cases
• Obtain pr(rj) from the input image and then obtain the values of sk,
round the value to the integer range [0, L-1].
• Use the specified PDF and obtain the transformation function G(zq),
round the value to the integer range [0, L-1].
• Mapping from sk to zq
0 0
( 1)
( ) ( 1) ( )
k k
k k r j j
j j
L
s T r L p r n
MN
0
( ) ( 1) ( )
q
q z i k
i
G z L p z s
1
( )q kz G s
37. 2/16/2018 37
Example: Histogram Matching
Suppose that a 3-bit image (L=8) of size 64 × 64 pixels (MN = 4096) has the
intensity distribution shown in the following table (on the left). Get the
histogram transformation function and make the output image with the
specified histogram, listed in the table on the right.
38. 2/16/2018
Example: Histogram Matching
Obtain the scaled histogram-equalized values,
Compute all the values of the transformation function G,
0 1 2 3 4
5 6 7
1, 3, 5, 6, 7,
7, 7, 7.
s s s s s
s s s
0
0
0
( ) 7 ( ) 0.00z j
j
G z p z
1 2
3 4
5 6
7
( ) 0.00 ( ) 0.00
( ) 1.05 ( ) 2.45
( ) 4.55 ( ) 5.95
( ) 7.00
G z G z
G z G z
G z G z
G z
0
0 0
1 2
65
7
40. 2/16/2018 40
Example: Histogram Matching
Obtain the scaled histogram-equalized values,
Compute all the values of the transformation function G,
0 1 2 3 4
5 6 7
1, 3, 5, 6, 7,
7, 7, 7.
s s s s s
s s s
0
0
0
( ) 7 ( ) 0.00z j
j
G z p z
1 2
3 4
5 6
7
( ) 0.00 ( ) 0.00
( ) 1.05 ( ) 2.45
( ) 4.55 ( ) 5.95
( ) 7.00
G z G z
G z G z
G z G z
G z
0
0 0
1 2
65
7
s0
s2 s3
s5 s6 s7
s1
s4
41. 2/16/2018 41
Example: Histogram Matching
0 1 2 3 4
5 6 7
1, 3, 5, 6, 7,
7, 7, 7.
s s s s s
s s s
0
1
2
3
4
5
6
7
kr
46. 2/16/2018 46
Local Histogram Processing
Define a neighborhood and move its center from pixel to pixel
At each location, the histogram of the points in the neighborhood
is computed. Either histogram equalization or histogram
specification transformation function is obtained
Map the intensity of the pixel centered in the neighborhood
Move to the next location and repeat the procedure
47. 2/16/2018 47
Local Histogram Processing
• The two histogram processing methods discussed:
• Global
• pixels are modified by a transformation
function based on the gray-level content of an
entire image.
• Global approach is suitable for overall enhancement
• there are cases in which it is necessary to
enhance details over small areas in an image
49. 2/16/2018 49
Using Histogram Statistics for Image
Enhancement
1
0
( )
L
i i
i
m r p r
1
0
( ) ( ) ( )
L
n
n i i
i
u r r m p r
1
2 2
2
0
( ) ( ) ( )
L
i i
i
u r r m p r
1 1
0 0
1
( , )
M N
x y
f x y
MN
1 1
2
0 0
1
( , )
M N
x y
f x y m
MN
Average Intensity
Variance
50. 2/16/2018 50
Using Histogram Statistics for Image
Enhancement
1
0
Local average intensity
( )
denotes a neighborhood
xy xy
L
s i s i
i
xy
m r p r
s
1
2 2
0
Local variance
( ) ( )xy xy xy
L
s i s s i
i
r m p r
51. 2/16/2018 51
Using Histogram Statistics for Image
Enhancement: Example
0 1 2
0 1 2
( , ), if and
( , )
( , ), otherwise
:global mean; :global standard deviation
0.4; 0.02; 0.4; 4
xy xys G G s G
G G
E f x y m k m k k
g x y
f x y
m
k k k E
g
52. Enhancement Using Arithmetic/Logic
Operations
Arithmetic/logic operations involving images
are performed on a pixel-by-pixel basis
between two or more images (this excludes
the logic operation NOT, which is performed
on a single image).
The difference between two images f(x, y) and
h(x, y), expressed as
G(x,y)=f(x,y)-h(x,y)
53. Basics of Spatial Filtering
The subimage is called a filter, mask,
kernel, template, or window.
The values in a filter subimage are referred to
as coefficients , rather than pixels.
Smoothing spatial filters