This slidecast takes an informal approach to image processing using Matlab environment.
Very little math is involved to keep things simple. But the full essence is only felt with the math involved.
The ANPR (Automatic Number Plate Recognition) using ALR (Automatic line
Tracking Robot) is a system designed to help in recognition of number plates of vehicles.
This system is designed for the purpose of the security and it is a security system.
For more details
http://projectsofashok.blogspot.com/2010/04/anprautomatic-number-plate-recognition.html
Abstract:
With an everyday increase in the number of cars on our roads and highways, we are facing numerous problems, for example:
• Smuggling of cars
• Invalid license plates
• Identification of stolen cars
• Usage of cars in terrorist attacks/illegal activities
In order to address the above issues, we took up the project of developing a prototype, which can perform license plate recognition (LPR). This project, as the name signifies, deals with reading, storing and comparing the license plate numbers retrieved from snapshots of cars to ensure safety in the country and ultimately help to reduce unauthorized vehicles access and crime.
License Plate Recognition (LPR) has been a practical technique in the past decades. It is one of the most important applications for Computer Vision, Patter Recognition and Image Processing in the field of Intelligent Transportation Systems (ITS).
Generally, the LPR system is divided into three steps, license plate locating, license plate character segmentation and license plate recognition. This project discusses a complete license plate recognition system with special emphasis on the Localization Module.In this study, the proposed algorithm is based on extraction of plate region using morphological operations and shape detection algorithms. Segmentation of plate made use of horizontal and vertical smearing and line detection algorithms. Lastly, template matching algorithms were used for character recognition.
The implementation of the project was done in the platforms of Matlab and OpenCV.
License Plate Recognition using Morphological Operation. Amitava Choudhury
This paper describes an efficient technique of locating and
extracting license plate and recognizing each segmented
character. The proposed model can be subdivided into four
parts- Digitization of image, Edge Detection, Separation of
characters and Template Matching. In this work, we propose a
method which is based on morphological operations where
different Structuring Elements (SE) are used to maximally
eliminate non-plate region and enhance plate region.
Character segmentation is done using Connected Component
Analysis. Correlation based template matching technique is
used for recognition of characters. This system is
implemented using MATLAB7.4.0. The proposed system is
mainly applicable to Indian License Plates.
Tracking number plate from vehicle usingijfcstjournal
In Traffic surveillance, Tracking of the number plate from the vehicle is an important task, which demands
intelligent solution. In this document, extraction and Recognization of number plate from vehicles image
has been done using Matlab. It is assumed that images of the vehicle have been captured from Digital
Camera. Alphanumeric Characters on plate has been Extracted and recognized using template images of
alphanumeric characters.
This paper presents a new algorithm in MATLAB which has been used to extract the number plate from the
vehicle in various luminance conditions. Extracted image of the number plate can be seen in a text file for
verification purpose. Number plate identification is helpful in finding stolen cars, car parking management
system and identification of vehicle in traffic.
The ANPR (Automatic Number Plate Recognition) using ALR (Automatic line
Tracking Robot) is a system designed to help in recognition of number plates of vehicles.
This system is designed for the purpose of the security and it is a security system.
For more details
http://projectsofashok.blogspot.com/2010/04/anprautomatic-number-plate-recognition.html
Abstract:
With an everyday increase in the number of cars on our roads and highways, we are facing numerous problems, for example:
• Smuggling of cars
• Invalid license plates
• Identification of stolen cars
• Usage of cars in terrorist attacks/illegal activities
In order to address the above issues, we took up the project of developing a prototype, which can perform license plate recognition (LPR). This project, as the name signifies, deals with reading, storing and comparing the license plate numbers retrieved from snapshots of cars to ensure safety in the country and ultimately help to reduce unauthorized vehicles access and crime.
License Plate Recognition (LPR) has been a practical technique in the past decades. It is one of the most important applications for Computer Vision, Patter Recognition and Image Processing in the field of Intelligent Transportation Systems (ITS).
Generally, the LPR system is divided into three steps, license plate locating, license plate character segmentation and license plate recognition. This project discusses a complete license plate recognition system with special emphasis on the Localization Module.In this study, the proposed algorithm is based on extraction of plate region using morphological operations and shape detection algorithms. Segmentation of plate made use of horizontal and vertical smearing and line detection algorithms. Lastly, template matching algorithms were used for character recognition.
The implementation of the project was done in the platforms of Matlab and OpenCV.
License Plate Recognition using Morphological Operation. Amitava Choudhury
This paper describes an efficient technique of locating and
extracting license plate and recognizing each segmented
character. The proposed model can be subdivided into four
parts- Digitization of image, Edge Detection, Separation of
characters and Template Matching. In this work, we propose a
method which is based on morphological operations where
different Structuring Elements (SE) are used to maximally
eliminate non-plate region and enhance plate region.
Character segmentation is done using Connected Component
Analysis. Correlation based template matching technique is
used for recognition of characters. This system is
implemented using MATLAB7.4.0. The proposed system is
mainly applicable to Indian License Plates.
Tracking number plate from vehicle usingijfcstjournal
In Traffic surveillance, Tracking of the number plate from the vehicle is an important task, which demands
intelligent solution. In this document, extraction and Recognization of number plate from vehicles image
has been done using Matlab. It is assumed that images of the vehicle have been captured from Digital
Camera. Alphanumeric Characters on plate has been Extracted and recognized using template images of
alphanumeric characters.
This paper presents a new algorithm in MATLAB which has been used to extract the number plate from the
vehicle in various luminance conditions. Extracted image of the number plate can be seen in a text file for
verification purpose. Number plate identification is helpful in finding stolen cars, car parking management
system and identification of vehicle in traffic.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Efficient Model to Identify A Vehicle by Recognizing the Alphanumeric Char...IJMTST Journal
Automatic Engine Number Recognition (AENR) is the digital image processing and an important aspect/role to identify the theft vehicles by recognizing characters, digits and special symbols. There is increase in the theft of vehicles, so to identify these theft vehicles, the proposed system is introduced. The proposed system controls the theft vehicles by recognizing a digits and characters in the number plate and chassis region and stores in the database in ASCII format to check the theft vehicles are registered or unregistered. Both system consists of 4 common phases: - Preprocessing, Character Extraction (ROI), Character Segmentation, and Character Recognition. This paper proposes a new scheme for engine number and chassis number extraction from the pre-processed image of the vehicle’s engine and chassis region using preprocess techniques, Region of Interest(ROI), Binarization, thresholding, template matching.
A design of license plate recognition system using convolutional neural networkIJECEIAES
This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.
Bangla Optical Digits Recognition using Edge Detection MethodIOSR Journals
Abstract:This paper is based on Bangla Optical Digit Recognition (ODR) by the Edge detection technique. In this method, Bangla digit image converted into gray-scale which distributed by an M by N array form. Here input data are considered off-line printed digit’s image which collected from computer generated image, scanned documents or printed text. After addressing the gray-scale image against a variable in the form of an M by N array, where the value of array pointers are shown 255 for total white space, 0 (zero) for total dark space and value between 255 and 0 for mix of white and dark space of the image. At the next process, four edgestouch points as well as each touch point’s ratio use as parameters to determine each Bangla digit uniquely. Keywords-Edge, image,gray-scale, Matrix,ODR.
Automatic number plate recognition using matlabChetanSingh134
The project is based on Image processing.It basically detects the number plate while following an algorithm based on image processing.It does that by following certain steps like image detection, character segmentation, OCR, and template matching.Have a look at the ppt and you will understand each step clearly
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Efficient Model to Identify A Vehicle by Recognizing the Alphanumeric Char...IJMTST Journal
Automatic Engine Number Recognition (AENR) is the digital image processing and an important aspect/role to identify the theft vehicles by recognizing characters, digits and special symbols. There is increase in the theft of vehicles, so to identify these theft vehicles, the proposed system is introduced. The proposed system controls the theft vehicles by recognizing a digits and characters in the number plate and chassis region and stores in the database in ASCII format to check the theft vehicles are registered or unregistered. Both system consists of 4 common phases: - Preprocessing, Character Extraction (ROI), Character Segmentation, and Character Recognition. This paper proposes a new scheme for engine number and chassis number extraction from the pre-processed image of the vehicle’s engine and chassis region using preprocess techniques, Region of Interest(ROI), Binarization, thresholding, template matching.
A design of license plate recognition system using convolutional neural networkIJECEIAES
This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.
Bangla Optical Digits Recognition using Edge Detection MethodIOSR Journals
Abstract:This paper is based on Bangla Optical Digit Recognition (ODR) by the Edge detection technique. In this method, Bangla digit image converted into gray-scale which distributed by an M by N array form. Here input data are considered off-line printed digit’s image which collected from computer generated image, scanned documents or printed text. After addressing the gray-scale image against a variable in the form of an M by N array, where the value of array pointers are shown 255 for total white space, 0 (zero) for total dark space and value between 255 and 0 for mix of white and dark space of the image. At the next process, four edgestouch points as well as each touch point’s ratio use as parameters to determine each Bangla digit uniquely. Keywords-Edge, image,gray-scale, Matrix,ODR.
Automatic number plate recognition using matlabChetanSingh134
The project is based on Image processing.It basically detects the number plate while following an algorithm based on image processing.It does that by following certain steps like image detection, character segmentation, OCR, and template matching.Have a look at the ppt and you will understand each step clearly
COM2304: Intensity Transformation and Spatial Filtering – III Spatial Filters...Hemantha Kulathilake
At the end of this lecture, you should be able to;
describe sharpening through spatial filters.
Identify usage of derivatives in Image Processing.
discuss edge detection techniques.
compare 1st & 2nd order derivatives used for sharpening.
Apply sharpening techniques for problem solving.
To highlight the contribution made to the total image appearance by specific bits.i.e. Assuming that each pixel is represented by 8 bits, the image is composed of 8 1-bit planes.Useful for analyzing the relative importance played by each bit of the image.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
19BCS1815_PresentationAutomatic Number Plate Recognition(ANPR)P.pptxSamridhGarg
Automatic Number Plate Recognition(ANPR)
We are building a python software for optical character Recognition of the license number plate using various Python libraries and importing various packages such as OpenCV, Matplotlib, numpy, imutils and Pytesseract for OCR(optical Character Recognition) of Number plate from image clicked. Let us discuss complete process step by step in this framework diagram shown above:
Step-1 Image will be taken by the camera(CCTV) or normal range cameras
Step-2 Selected image will be imported in our Software for pre-processing of our image and conversion of image into gray-scale for canny edge-detection
Step-3 We have installed OpenCV library for conversion of Coloured image to black and White image.
Step-4 We installed OpenCV package. Opencv(cv2) package is main package which we used in this project. This is image processing library.
Step-5 We have installed Imutils package. Imutils is a package used for modification of images . In this we use this package for change size of image.
Step-6 We have installed Pytesseract library. Pytesseract is a python library used for extracting text from image. This is an optical character recognition(OCR) tool for python.
Step-7 We have installed Matplotlib Library. In matplotlib library we use a package name pyplot. This library is used for plotting the images. % matplotlib inline is used for plot the image at same place.
Step-8 Image is read by the Imread() function and after reading the image we resize the image for further processing of image.
Step-9 Then our selected image is converted to gray-scale using below function.
# RGB to Gray scale conversion
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
plot_image(gray,"Grayscale Conversion")
Step-10 Then we find canny edges in our gray-scale image and then find contours based on edges. Then we find the top 30 contours from our image.
Step-11 Loop over our contours to find the best possible approximate contour of number plate
Step-12 Then Draw the selected contour on the original image.
Step-13 then we will use the Pytesseract Package to convert selected contour image into String.
Step-14 After fetching the number from number plate we store it in our MySQL database and also we have inculcated the feature of exporting data to excel sheet.
Remember: Most important feature of my project is that I can export my fetched number plate data to Government agencies for further investigation.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
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!
1. Digital Image Processing (DIP)
The Fundamentals - A MATLAB assisted non-mathematical approach
By Abhishek Sharma (EEE 2k7)
2. Materials
Some taken from slides by a NIT Surathkal friend : Varun
Nagaraja. He worked in the same lab at IISc Bangalore. Joined as
a PhD student in University of Maryland – College Park, a week
back.
Some material taken from online works of Sabih D. Khan, a
French-Pakistani researcher.
Some prepared by me!
3. Objective
To take a quasi non-mathematical , MATLAB assisted
approach to DIP.
I cut out the equations because…
1) I don’t want to scare you even before we start the climb
2) I am pathetic at elucidating maths concepts
3) I don’t have a lot of time.
4) You don’t need maths to start working in DIP.
If I am able to make you fall in love with DIP, I hope like
real world love, you will be able to accept it with its’
shortcomings – maths!
4.
5.
6.
7.
8. SixthSense
by Pranav Mistry, MIT Media
Labs
'SixthSense' is a wearable gestural interface that
augments the physical world around us with digital
information and lets us use natural hand gestures to
interact with that information.
9.
10. Image Processing - a definition
Image Processing generally involves extraction of useful
information from an image.
This useful information may be the dimensions of an
engineering component, size of diagnosed tumor, or
even a 3D view of an unborn baby.
11. Intro to DIP with MATLAB
Images can be conveniently represented as matrices in
Matlab.
One can open an image as a matrix using imread
command.
The matrix may simply be m x n form or it may be 3
dimensional array or it may be an indexed
matrix, depending upon image type.
The image processing may be done simply by matrix
calculation or matrix manipulation.
Image may be displayed with imshow command.
Changes to image may then be saved with imwrite
command.
12. Image types
Images may be of three types i.e. black & white, grey
scale and colored.
In Matlab, however, there are four types of images.
Black & White images are called binary images,
containing 1 for white and 0 for black.
Grey scale images are called intensity images,
containing numbers in the range of 0 to 255 or 0 to 1.
Colored images may be represented as RGB Image or
Indexed Image.
13. Image types
In RGB Images there exist three indexed images.
First image contains all the red portion of the
image, second green and third contains the blue portion.
So for a 640 x 480 sized image the matrix will be 640 x
480 x 3.
An alternate method of colored image representation is
Indexed Image.
It actually exist of two matrices namely image matrix
and map matrix.
Each color in the image is given an index number and in
image matrix each color is represented as an index
number.
Map matrix contains the database of which index
number belongs to which color.
14. MATLAB – image conversions
RGB Image to Intensity Image (rgb2gray)
RGB Image to Indexed Image (rgb2ind)
RGB Image to Binary Image (im2bw)
Indexed Image to RGB Image (ind2rgb)
Indexed Image to Intensity Image (ind2gray)
Indexed Image to Binary Image (im2bw)
Intensity Image to Indexed Image (gray2ind)
Intensity Image to Binary Image (im2bw)
Intensity Image to RGB Image (gray2ind, ind2rgb)
15. Image Histograms
There are a number of ways to get statistical information about
data in the image.
Image histogram is one such way.
An image histogram is a chart that shows the distribution of
intensities in an image.
Each color level is represented as a point on x-axis and on y-
axis is the number instances a color level repeats in the image.
Histogram may be view with imhist command.
Sometimes all the important information in an image lies only
in a small region of colors, hence it usually is difficult to extract
information out of that image.
To balance the brightness level, we carry out an image
processing operation termed histogram equalization. Use
MATLAB histeq command
17. Simple character recognition
code
Detect only particular
characters and numbers in an
image.
Characters are in white and of
a fixed size.
Background is black in color.
The image is in binary format.
We will explore various DIP
concepts while we do this….
Let’s start
19. Let’s have some fun… I know my
sense of humor sucks!
Now read the image ‘same color.jpg’ and display it on a window.
Once the image is displayed in the window, select Tools –Data
Cursor or select the shortcut on the toolbar.
Click on point A as shown, on the image. It displays three values
(RGB) since it is a color image. You can try reading pixel values for
the previous image. It will be either 0/1 since it is binary image.
Hold Alt and Click on point B. This creates something called as a
new datatip.
Now for some fun
What are the RGB values at the two points?
20. Morphological Operations
These are image processing operations done on
binary images based on certain morphologies or
shapes.
The value of each pixel in the output is based on the
corresponding input pixel and its neighbors.
By choosing appropriately shaped neighbors one can
construct an operation that is sensitive to a certain
shape in the input image.
22. Skeletonize
It creates skeleton of an object, by removing pixels on
the boundaries but does not allow objects to break
apart.
It is an extremely important operation in image
processing as it removes complexities from an image
without loosing details.
23. Erosion and Dilation
These are the most fundamental of binary
morphological operations.
In dilation if any pixel in the input pixel’s neighborhood
is on, the output pixel is on otherwise off.
In actual dilation grows the area of the object. Small
holes in the object are removed.
In erosion if every pixel in the input pixel’s
neighborhood is on the output pixel is on otherwise off
This in actual works as shrinking the object’s area, thus
small isolated regions disappear.
24. Dilation….
Dilation does not necessarily mean dilation of the holes also. The
holes get contracted as shown above.
Also try image erosion. Use MATLAB’s help.
25. Dilation…
adds pixels to the boundaries of objects in an image.
number of pixels added from the objects in an image
depends on the size and shape of the structuring
element
function strel(…)can be used to generate the SEs.
26. Structuring Elements
In mathematical morphology,
structuring element is a shape, used
to probe or interact with a given
image, with the purpose of drawing
conclusions on how this shape fits or
misses the shapes in the image.
Check out help on strel for various
combinations
27. Continuing with the algo…
When the dilated image of the character is
subtracted from the original we get something
like…
Next we create such images for all the
characters that we want to recognize.
(For all those individual character images in
the folder)
28. Hit or miss?
Function, bwhitmiss is employed to check if a particular
character is present in the given image.
bwhitmiss(BW1,SE1,SE2)performs the hit‐miss operation
defined by the structuring elements SE1 and SE2. The
hit‐miss operation preserves pixels whose neighborhoods
match the shape of SE1 and don't match the shape of SE2.
If the matrix returned by bwhitmiss contains nonzero
elements, then the character is found in the image.
Also note the use of functions isempty and nonzeros
You can now use charrec.m to recognize few characters
in a crude way.
31. Image Segmentation
The goal of image segmentation is to cluster pixels
into salient image regions, i.e., regions corresponding
to individual surfaces, objects, or natural parts of
objects.
A segmentation could be used for object recognition,
image compression, image editing, or image database
look-up.
34. Image Segmentation - Global
Thresholding
Disadvantage is when there are multiple colors for objects and
backgrounds.
35. Otsu’s Method
Based on a very simple idea: Find the threshold that
minimizes the weighted within-class variance.
This turns out to be the same as maximizing the
between-class variance.
Operates directly on the gray level histogram [e.g.
256 numbers, P(i)], so it’s fast (once the histogram is
computed).
36. The weighted within-class variance is:
(t) q1 (t) (t) q2 (t) (t)
2
w
2
1
2
2
Where the class probabilities are estimated as:
t I
q1 (t) P(i) q2 (t) P(i)
i t 1
i 1
And the class means are given by:
I
t
iP(i) iP(i)
1 (t) 2 (t)
i 1 q1 (t) i t 1 q2 (t )
41. This was again a very crude way, since we are depending
only on value of area which might not remain constant if
camera changes position.
Most of the times the standard features available with
regionprops() is not sufficient. We will have to write our
own code to extract features.
Also we used hard thresholds for are as to classify CCs.
Again most of the times, this is not followed. Classifiers
using Pattern Recognition techniques are employed.
42. Why edges?
Reduce dimensionality of data
Preserve content information
Useful in applications such as:
◦ object detection
◦ structure from motion
◦ tracking
43. Why not edges?
But, sometimes not that useful, why?
Difficulties:
1. Modeling assumptions
2. Parameters
3. Multiple sources of information
(brightness, color, texture, …)
4. Real world conditions
Is edge detection even well defined?
47. Canny difficulties
1. Modeling assumptions
Step edges, junctions, etc.
2. Parameters
Scales, threshold, etc.
3. Multiple sources of information
Only handles brightness
4. Real world conditions
Gaussian iid noise? Texture…
48. Edge Detection
Edge detection extract edges of objects from an image.
There are a number of algorithms for this, but these
may be classified as derivative based or gradient based.
In derivative based edge detection the algorithm takes
first or second derivative on each pixel in the image.
In case of first derivative at the edge of the image there
is a rapid change of intensity.
While in case of second derivative there is a zero pixel
value, termed zero crossing.
In gradient based edge detection a gradient of
consecutive pixels is taken in x and y direction.
49.
50. Taking derivative on each and every pixel of the image
consumes a lot of computer resources and hence is not
practical.
So usually an operation called kernel operation is
carried out.
A kernel is a small matrix sliding over the image matrix
containing coefficients which are multiplied to
corresponding image matrix elements and their sum is
put at the target pixel.
53. Best one is – Canny’s Method
Uses both the derivative and the gradient to perform
edge detection
The maths is a bit complex
Can through the PDF in your folder later.
Pass ‘canny’ as a parameter to the edge function to
perform canny.