Image processing involves algorithms that take images as input and output other images. It is used to prepare digital images for viewing or analysis by enhancing structures within images. Common applications of image processing include adjusting properties like brightness, contrast, and gamma; detecting edges; blurring or sharpening; and performing operations like erosion and dilation. Principal component analysis (PCA) is a technique used to reduce the dimensionality of image data for analysis and recognition. Face recognition systems use PCA to extract feature vectors from images, then compare new images to the training set to identify faces.

2. Image processing
is the study of any algorithm that takes an image as input and returns an image
as output.
WHY DO WE NEED IMAGE PROCESSING?…
Since the digital image is “invisible” it must be prepared for viewing
on one or more output device (laser printer,monitor,etc)
The digital image can be optimized for the application by enhancing or altering the
appearance of structures within it (based on: body part, diagnostic task, viewing
preferences,etc)
It might be possible to analyze the image in the computer and provide cues to the
radiologists to help detect important/suspicious structures (e.g.: Computed Aided
Diagnosis, CAD)

3. Components of an Image Processing System

4. Sensors
Two elements required to acquire digital images
Physical device: sensitive to the energy radiated by the object we wish to image.
Digitizer: converts output of physical sensing device into digital form.
Specialized image processing hardware:
Digitizer + hardware
Hardware: Performs Arithmetic Logic Unit (ALU) on entire image.
Computer:
Image processing system ranging from PC to supercomputer.
Image Processing Software:
Specialized modules performing specific tasks

5. Mass Storage:
Short-term storage for use during processing.
on line storage for relatively fast recall
Archival storage, characterized by infrequent access
Image Displays:
Flat screen, TV, Monitors, LCD, LED, 3D displays.
Hardcopy:
Laser Printers, Camera Films, Heat Sensitive devices, inkjet units, digital units
like optical and CD-ROM.

6. Image processing software

7. 1-KS400
 One of commercial image processing software packages used for some of the
applications is the KONTRON Imaging System KS400.
 very powerful and convenient in use.
the KS400 software has one essential disadvantage:
 it does not allow to have direct access form a macro to single pixels in the image.
 In practice this means
that the functionality of the software is limited by the set of standard functions.
 In principle, the problem can be overcome by using Free Programming KONTRON
Software Development Kit which is supplied with KS400.

8. 2-Image processing/analysis software :
Developed The software written for UNIX consists of a set of low-level image
processing/analysis functions including:-
Sun raster file image (RAS) reading/writing;
automatic and manual image thresholding;
gray-scale and binary morphology;
fractal analysis of contours using 'hand and dividers' method;
fractal analysis of percolation networks;
image correlation.

9. 3-Image processing with MathCAD and
Matlab.
The MatLab and MathCAD environments are ideally suited to image
processing.
programs have Image Processing Toolboxes which provide a powerful and
flexible environment for image processing and analysis. Both programs were used
to perform different calculations on images.
-There are several advantages
 One of them is the ability to have direct access to any portion of available
information what in general is not possible with many commercial image
analysis systems.

10. Image processing algorithms

11. 1-FINDING THE NEAREST COLOUR:
1. Start at the first pixel in the image.
2. Get the actual color of the pixel.
3. Find the nearest color of this pixel from the available
palette.
4. Replace the pixel with the nearest color.
5. Have we reached the end of the image? If so stop
here.
6. Move on to the next pixel.
7. Go back to step 2.

12.  There are a few methods for finding the nearest color.
The one that I am going to explain here is called the Euclidean
distance method.
The algorithm for this is as follows:
1. Set minimum distance to a value higher than the highest possible.
2. Start at the first color in the palette.
3. Find the difference between each RGB value of the actual color and the current
palette color.
4. Calculate the Euclidean distance.
5. If the distance is smaller than the minimum distance set minimum distance to the
smaller
value and note the current palette color.
6. Have we reached the end of the palette? If so stop here.
7. Move on to the next color in the palette.
8. Go back to step 3.

15. 2-ERROR DIFFUSION:
-Error diffusion works by comparing the actual color
of a pixel against its nearest color and taking the
difference between them.
-This difference is known as the error.
- Portions of the error are split between neighbouring
pixels causing the error to be diffused hence the name
“error diffusion”.

16. The simplest form of error diffusion can be
shown as:

17. -With this form of error diffusion half of the error from the current pixel (represented
by the black dot) is diffused to the pixel on the right and half to the pixel below.
-In a color image error diffusion should be applied to the red, green and blue channels
separately.
- An important point to note here is that the total amount of error diffused should
never exceed a value of 1.
-It is also important to ensure that when a portion of the error is diffused to
neighbouring pixels it does not cause invalid values (e.g. go below 0 or above 255).
- Should a value go outside of the valid range then it should be truncated (e.g. -10
would be truncated to 0 and 260 would be truncated to 255).

18. -The quality of this type of error diffusion however is pretty poor and very few
people would actually use it.
-A better one, and arguably the most famous, is Floyd-Steinberg error diffusion.

19. With Floyd-Steinberg error diffusion the error is distributed amongst
more neighbouring pixels providing a much nicer overall dithering
effect.
Performing the colour reduction with Floyd-Steinberg error diffusion
will produce the following results:
images reduced to 8 colors with error diffusion

20. 3-GREYSCALE CONVERSION:
to convert a particular color into greyscale we need to work out what the intensity or
brightness of that color is.
he formula for this is:
Another method of greyscale conversion which takes into account the human
perception of color uses different weights for the red, green and blue components.

21. • To illustrate the above points let’s have a look at an image of
some colored bars:
• Let’s convert this image to greyscale using the mean method:
• Converting the image to greyscale using the weighted method
will give us the following:

22. 4-BRIGHTNESS ADJUSTMENT:
adding the desired change in brightness to each of the red, green and blue color
components.
The value of brightness will usually be in the range of -255 to +255 for a 24 bit
palette.
Negative values will darken the image and, conversely, positive values will
brighten the image.

23. images which have had the brightness adjusted by -128
(darkened) and +128 (brightened):

24. 5-CONTRAST ADJUSTMENT:
The first step is to calculate a contrast correction factor which is given by the
following formula where the value C is the desired level of contrast:
The next step is to perform the actual contrast adjustment itself. The
following formula shows the adjustment in contrast being made to the red
component of a color
The value of contrast will be in the range of -255 to +255.
Negative values will decrease the amount of contrast and, conversely, positive
values will increase the amount of contrast.

25. 6-GAMMA CORRECTION:
can be described as the relationship between an input and the resulting output.
For the scope of this article the input will be the RGB intensity values of an image.
The relationship in this case between the input and output is that the output is
proportional to the input raised to the power of gamma. The formula for
calculating the resulting output is as follows:
For calculating gamma correction the input value is raised to the power of the
inverse of gamma. The formula for this is as follows:
The range of values used for gamma will depend on the application, but
personally I tend to use a range of 0.01 to 7.99.

26. 7-COLOUR INVERSION AND SOLARISATION :
-Color inversion, also known as the negative effect
- is one of the easiest effects to achieve in image processing.
-Color inversion is achieved by subtracting each RGB color value from the
maximum possible value (usually 255).

27. Another effect which is related to color inversion is the sola rise effect.
The difference between the sola rise effect and color inversion is that with the sola
rise effect only color values above or below a set threshold are inverted.
The images below show the inversion of colours below a threshold of 128 in the
first instance and then the inversion of colours above a threshold of 128 in the
second.

28. Image processing
functions/algorithms
Image processing applications mainly focuses on improving the visual
appearance of images to a human viewer and preparing for measurement of the
features and structures present.
The measurement of images generally requires that features be well defined,
either by edges or unique brightness, colour, texture, or some combination of
these factors.

29. Spatial filtering
An image can be filtered to remove a band of spatial frequencies
as high frequencies and low frequencies. Where rapid brightness transitions are
established, high frequencies will be there
Spatial filtering operations include high pass, low pass and edge detection filters.

30. Sharpening
The main aim in image sharpening is to highlight fine detail in the image
or to enhance detail that has been blurred due to noise or other effects.
The Laplacian often used for this purpose. However, image sharpening
too can be interpreted in the frequency domain.
 Sharpening emphasizes edges in the image and make them easier to
see and recognize.
In addition to that, differences between each pixel and its neigbour too,
can influence sharpening effect.

31. Blurring
The visual effect of a low pass filter is image blurring.
This is because the sharp brightness transitions had been attenuated to small
brightness transitions.
It have less detail and blurry.
 Blurring is aimed to diminish the effects of camera noise, unauthentic pixel values or
missing pixel values.
For blurring effect, there are two mostly used techniques which are :-
-neighbourhood averaging (Gaussian filters) .
-edge preserving (median filters).

32. A Gaussian blur filter modifies each pixel by looking at its
neighbours and computing a "weighted average" of their values,
with more weight being given to closer neighbours.
The standard Gaussian blur filter found in most image
processing programs is isotropic; it blends pixels values equally in
all directions.
For median filtering on the other hand, the outcome is that
pixels with outlying values are forced to become more like their
neighbours but at the same time edges are preserved.

33. Edge Detection
Edge detection is an image processing technique for finding
the boundaries of objects within images.
It works by detecting discontinuities in brightness.
Edge detection is used for image segmentation and data
extraction in areas such as image processing, computer vision,
and machine vision.

35. Edge Detection Techniques
Sobel Operator
Robert’s cross operator:
The Roberts Cross operator performs a simple, quick to compute,
2-D spatial gradient measurement on an image.

36. Prewitt’s operator:
Prewitt operator [5] is similar to the Sobel operator and is used
for detecting vertical and horizontal edges in images.

37. 3-Image processing with MathCAD and Matlab.
 The Laplacian is a 2-D isotropic measure of the 2nd spatial derivative
of an image.
The Laplacian is often applied to an image that has first been
smoothed with something approximating a Gaussian Smoothing filter
in order to reduce its sensitivity to noise.
The operator normally takes a single gray level image as input and
produces another gray level image as output.

39.  Canny Edge Detection Algorithm
Canny's intentions were to enhance the many edge detectors already out at the
time he started his work.
The first and most obvious is low error rate.
The second criterion is that the edge points be well localized.
A third criterion is to have only one response to a single edge.
Hysteresis is used to track along the remaining pixels that have not been
suppressed.
Hysteresis uses two thresholds and if the magnitude is below the first threshold, it
is set to zero (made a non edge).
If the magnitude is above the high threshold, it is made an edge. And if the
magnitude is between the 2 thresholds, then it is set to zero unless there is a path
from this pixel to a pixel with a gradient above T2.
Advantages and Disadvantages of Edge Detector

41. Introduction: face recognition systems

42. •Due increasing concern regarding security issues around the
world, the growing interest in general about the extent of private
computer systems to identify the faces of accuracy, thus increasing
the number of security systems and applications in this area, and
has evolved significantly, and the algorithms used in varied
between simplicity and complexity.
•There were many questions:
•What is the extent of the accuracy of the algorithm to identify the
faces even be suitable for such applications?

43. Stages of face recognition systems in
general

44. Mechanism of automated face
recognition system
step1
The formation and extraction memory facial recognition
training set cluster, which is a collection of photos
submitted in advance of the system.

45. step2
• Find the most feature vector closest resemblance within the training
set to beam features extracted from the image provided to the test - any
image desired to know the identity of the owner through identification
system
feature vector An eclectic arrays of arrays original picture, and
represent important values and fundamental part of the original images,
and thereby reduced the size of images to represent the epitome ray
images.

47. Step 3
Here Within this system, we want to know the identity of the
person and discrimination, and pass through the image of that
person to the system, called in this case this image provided to
get to know them in a test image.
In order to draw the features vector images within this project,
we will rely on the PCA algorithm to extract features of X-process
feature vector images.

48. Principal Component Analysis
(PCA) algorithm.

49. PCA algorithm
•PCA algorithm is considered one of the most successful techniques
that have been used in the field of image recognition in the field of
image compression.
•Used this algorithm in MATLAB program
The main goal in the PCA algorithm
• lies in reducing the large dimensions of space data to the
dimensions of the smallest spaces. Usually the new spaces is a
feature spaces (containing the basic and important features of
the data in the original space).

50. Identify problems and large-dimensional
systems
Systems deal with large dimensional spaces.
Can make many improvements and through matching transfer
existing data to the data in less space dimensions. And thus we
have our dimensionality reduction from the original space with
large dimensions to the new space with smaller dimensions.

51. Let it be, for example, we have the following Beam:
•As part of the author
of N after the space , we
reduce the cross-dimensional
moving to another beam
•
To any space of K so that after K <N. consisting

52. Within this context, the PCA calculates linear conversion linear
transformation Which in turn meet with existing data within the top-dimensional
space to approve its information within a least-dimensional
subspace, as is the position below:

53. •Or in other words
•Whereas:
•The optimum conversion Is a conversion in which the value
is smaller.

54. Complete the task of face recognition using
PCA
Representation of each object within the training group
and less space dimensions using PCA algorithm

55. Assuming that the face that is required is a recognizable face one dimensions
Steps facial recognition
Step1:
the representation of the image lee form a single beam
dimension Let named .
Step 2: do b normalize the beam Put across from the average
face
step 3: find the nearest face within the training
group for the face, which is required to identify it Where the
difference between the youngest and error
Step 4: If er <Tr, where Tr is a threshold, it has been identified
on the face on that face

56. Motion detection

57. Motion detection is the first essential process in the extraction of
information regarding moving objects and makes use of stabilization
in functional areas, such as tracking, classification, recognition, and
so on.
Motion Alarm
It is pretty easy to add motion alarm feature to all these motion
detection algorithms
Literature Survey
We review many classes of algorithm used in motion detection,
which include optical flow algorithms, two
complementary background estimation technique, frame difference
method
Method of operation

61. Detection of Independent Motion

62. In this project we investigated two complementary methods for the
detection of moving objects by a moving observer. The first is based
on the fact that, in a rigid environment, the projected velocity at any
point in the image is constrained to lie on a 1-D locus in velocity
space, known as the constraint ray whose parameters depend only
on the observer motion.
If the observer motion is known, an independently moving object
can, in principle, be detected because its projected velocity is
unlikely to fall on this locus. We show how this principle can be
adapted to use partial information about the motion field and
observer motion that can be rapidly computed from real image
sequences.

63. The second method utilizes the fact that the apparent motion of a fixed point due to
smooth observer motion changes slowly, while the apparent motion of many moving
objects such as animals or maneuvering vehicles may change rapidly.
The motion field at a given time can thus be used to place constraints on the future
motion field which, if violated, indicate the presence of an autonomously maneuvering
object.
In both cases, the qualitative nature of the constraints allows the methods to be used
with the inexact motion information typically available from real image sequences.
We have produced Implementations of the methods that run in real time on a parallel
pipelined image processing system.
The pictures show two examples of the real-time system in operation. In the first, the
camera is mounted on a robot platform that is moving vertically with respect to the
room. In the second, the camera is mounted on a vehicle that is being driven towards the
road on which the detected vehicle is moving.

64. Motion Recognition

65. In this project, we investigated the use of robustly computable
motion features can be used directly as a means of recognition. We
have designed, implemented, and tested a general framework for
detecting and recognizing both distributed motion activity on the
basis of temporal texture, and complexly moving, compact objects on
the basis of their activity.

66. Motion-Detection Steps
Motion-detection is a two-step process.
Step1
identifies the objects that have moved between the two frames (using
difference and threshold filter). The difference between each corresponding
pixel of two frames is calculated; and the pixels with difference greater than
the specified threshold are marked in foreground color (white). All the other
pixels are marked in background color (black). So, the output of Step1 will be
a binary-image with only two colors (black and white).
The intensity value (brightness level) of the pixels are used to calculate the
difference. The intensity value of each pixel in a grayscale image will be in the
range 0 to 255. If RGB images are used, then the grayscale intensity value can
be calculated as (R + G + B / 3).

67. Step2
identifies the significant movements and filters out the noise that are
wrongly identified as motions (using erosion-filter). Erosion-filter
removes the pixels that are not surrounded by enough amount of
neighboring pixels. Essentially, this gives the ability to shrink the
objects thereby removing the noisy pixels
i.e. stand-alone pixels. The binary image (output of Step1) is scanned
pixel-by-pixel; and if enough number of pixels in current window are
not turned on, then the entire window is turned off i.e. ignored as
noise.

68. Image processing in Hardware

69. Theories
Field-Programmable Gate Array (FPGA)
A Field-Programmable Gate Array is a large-scale integrated
circuit (LSI) which is programmable.
Finite State Machine (FSM)
Finite State Machine is a behavioral model which consists of a
finite number of states and transitions between the states.

70. Gray-level Co-occurrence Matrix Statistics Image Generation
are images which represent the statistic uniqueness of textures in an
image. Many statistics images are generated.
Gray-level Co-occurrence Matrix (GLCM)
GLCM is a square matrix which contains numbers of times that
patterns of 2 scaled values are found while examining pairs of pixels
through an image.
Static Random Access Memory (SRAM)
SRAM is an electronic memory which is capable of storing data as
long as there is the power supply for the device.

71. Static Random Access Memory (SRAM)
SRAM is an electronic memory which is capable of storing data as long as there is the
power supply for the device.
Timing Diagram of the SRAM Write Operation

72. Experiments
The main object of this experiment is to find the best algorithm to
be implemented in the GLCM statistics image generation operation.
Materials and Equipments
1. Executable files of each algorithm which implements timer functions
in it
2. An Open Dragon image which its size is 3822 pixels by 2560 pixels
3. A Computer with these specifications
a. CPU: Intel Pentium 4 1.6 GHz
b. Motherboard: IBM, Intel i845
c. RAM: DDR 640 MB, 133 MHz

73. hardware devices
1. Prototyping Board: Design Gateway True PCI
2. SRAM: AMIC LP621024D
3. Bi-directional Buffer: 150Ω Resistors
System Components
There are 13 modules in the system. They are …
1. Memory Unit 7. Square Buffer
2. Process Controller 8. GLCM Builder
3. Memory Controller 9. Address Decoder
4. Arbiter 10. Matrix Voter
5. Center Indexer 11. Matrix Integrator
6. Square Fetcher 12. Clock Divider
13. pciif32
Designs

74. Matrix Integrator
Matrix Integrator was designed to calculate the three GLCM
Statistic values by summation all calculated positions of GLCM
matrix is in Memory Unit.
Block structure showing ports of Matrix
Integrator

75. Clock Divider
Clock Divider is responsible for dividing the frequency of a
clock signal into another frequency.
Block structure showing ports of Clock Divider

76. Image Processing in Hardware is the project concentrating on
designing a coprocessor for the computer system to compute the
computationally-intensive part ofoperations in the digital image
processing.

77. Thank you for your attention

78. Done by:-
Eng.Amal Ahmed Almathani
Eng.AnsamMansour
Eng.Eftikhar ali alamri
Eng.SafiaMoqbel
Eng.Somia Abdalhmeed