This document provides an introduction to pattern recognition, including definitions, applications, and key concepts. It discusses examples of pattern recognition problems like optical character recognition and biometric classification. Important terminology is defined, such as features, feature vectors, decision boundaries, training and test data. Bayesian decision theory is introduced as a framework for pattern recognition that quantifies decision costs. Key concepts explained include priors, likelihoods, posteriors, and the Bayes decision rule for minimizing risk. The example of classifying fish by species is used to illustrate these concepts.
It is an introductory slide for pattern recolonization. This presentation was quit emotional for me because it was the last academic presentation in Green University of Bangladesh. For that i have used a sad emo at the first of the slide.
What is pattern recognition (lecture 4 of 6)Randa Elanwar
In this series I intend to simplify a beautiful branch of computer science that we as humans use it in everyday life without knowing. Pattern recognition is a sub-branch of the computer vision research and is tightly related to digital signal processing research as well as machine learning and artificial intelligence.
A brief introduction to Pattern Recognition. Slides were used for a Seminar at the Interactive Art PhD at School of Arts of the UCP, Porto, Portugal (http://artes.ucp.pt)
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
It is an introductory slide for pattern recolonization. This presentation was quit emotional for me because it was the last academic presentation in Green University of Bangladesh. For that i have used a sad emo at the first of the slide.
What is pattern recognition (lecture 4 of 6)Randa Elanwar
In this series I intend to simplify a beautiful branch of computer science that we as humans use it in everyday life without knowing. Pattern recognition is a sub-branch of the computer vision research and is tightly related to digital signal processing research as well as machine learning and artificial intelligence.
A brief introduction to Pattern Recognition. Slides were used for a Seminar at the Interactive Art PhD at School of Arts of the UCP, Porto, Portugal (http://artes.ucp.pt)
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
OCR for PDFs: https://nanonets.com/blog/pdf-ocr/
PDF to CSV converter - https://nanonets.com/convert-pdf-to-csv
PDF to Excel converter - https://nanonets.com/tools/pdf-to-excel
Online OCR - https://nanonets.com/online-ocr
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data-mining algorithm used to perform hierarchical clustering over, particularly large data sets.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
OCR for PDFs: https://nanonets.com/blog/pdf-ocr/
PDF to CSV converter - https://nanonets.com/convert-pdf-to-csv
PDF to Excel converter - https://nanonets.com/tools/pdf-to-excel
Online OCR - https://nanonets.com/online-ocr
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data-mining algorithm used to perform hierarchical clustering over, particularly large data sets.
[UMAP2013] Recommendation with Differential Context WeightingYONG ZHENG
Context-aware recommender systems (CARS) adapt their recommendations to users’ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to sparsity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called differential context relaxation (DCR), in which different subsets of contextual features were applied in different components of a recommendation algorithm. In this paper, we expand on our previous work on DCR, proposing a more general approach — differential context weighting (DCW), in which contextual features are weighted. We compare DCR and DCW on two real-world datasets, and DCW demonstrates improved accuracy over DCR with comparable coverage. We also show that particle swarm optimization (PSO) can be used to efficiently determine the weights for DCW.
A lot of people talk about Data Mining, Machine Learning and Big Data. It clearly must be important, right?
A lot of people are also trying to sell you snake oil - sometimes half-arsed and overpriced products or solutions promising a world of insight into your customers or users if you handover your data to them. Instead, trying to understanding your own data and what you could do with it, should be the first thing you’d be looking at.
In this talk, we’ll introduce some basic terminology about Data and Text Mining as well as Machine Learning and will have a look at what you can on your own to understand more about your data and discover patterns in your data.
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Automatic System for Detection and Classification of Brain TumorsFatma Sayed Ibrahim
Automatic system for brain tumors detection based on DICOM MRI images
Surveying methodologies of from preprocessing to classifications
Implementing comparative study.
Proposed technique with highest accuracy and lest elapsed time.
Lec14: Evaluation Framework for Medical Image SegmentationUlaş Bağcı
How to evaluate accuracy of image segmentation?
– Gold standard ~ surrogate of truths
– Qualitative • Visual
• Inter-andintra-observeragreementrates – Quantitative
• Volumetricmeasurements(regression) • Regionoverlaps
• Shapebasedmeasurements
• Theoreticalcomparisons
• STAPLE,Uncertaintyguidance,andevaluationw/otruths
Clustering – K-means – FCM (fuzzyc-means) – SMC (simple membership based clustering) – AP(affinity propagation) – FLAB(fuzzy locally adaptive Bayesian) – Spectral Clustering Methods ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
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
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.
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• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
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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.
1. Advanced Digital Image Processing
(EE-836)
National University of Sciences and
Technology (NUST)
School of Electrical Engineering and Computer Science
(SEECS)
Khawar Khurshid, 20121
3. What is a Pattern?
“A pattern is the opposite of a chaos; it is an entity vaguely
defined, that could be given a name.”
4. PR Definitions from Literature
“The assignment of a physical object or event to one of several pre-
specified categories” – Duda and Hart
Pattern Recognition is concerned with answering the question
“What is this?” –Morse
5. Examples of PR Applications
Optical Character Recognition (OCR)
– Sorting letters by postal code.
– Reconstructing text from printed
materials.
– Handwritten character recognition
Analysis and identification of human
patterns (Biometric Classification)
– Face recognition
– Handwriting recognition
– Finger prints and DNA mapping
– Iris scan identification
6. Examples of Pattern Recognition Problems
Computer aided diagnosis
– Medical imaging,
EEG, ECG signal
analysis
– Designed to assist (not
replace) physicians
Prediction systems
– Weather forecasting (based on satellite data).
– Analysis of seismic patterns
7. Other Examples
Speech and voice recognition/speaker identification.
Banking and insurance applications
– Credit cards applicants classified by income, credit worthiness,
mortgage amount, # of dependents, etc.
– Car insurance (pattern including make of car, # of accidents,
age, gender, driving habits, location, etc).
8. A Pattern Classification Example
A fish processing plant wants to automate the process of sorting incoming fish
according to species (salmon or sea bass)
The automation system consists of
– A conveyor belt for incoming products
– A vision system with an overhead CCD camera
– A computer to analyze images and control the robot arm
9. objects based on selected features
Recognize objects using
Computer vision, Image Processing and PR
Real
world
probabilistic techniques
Image Acquisition by
sensor
Image enhancement and
restoration
Filtering
Feature Extraction
Edge detection
Texture
Interest points
Classification and Recognition
Classification and interpretation of
Segmentation
Impose some order on group of
pixels to separate them from
each other
10. A Pattern Classification Example
“Sorting incoming Fish on a conveyor according to species using
optical sensing”
11. Purpose:
To classify the future samples based on the data of extracted
features from the training samples
Problem Analysis
Set up a camera and take some training samples to extract features
• Length
• Lightness
• Width
• Number and shape of fins
• Position of the mouth, etc…
– This is the set of all suggested features to explore for use in our
classifier!
13. The length is a poor feature alone!
Select the lightness as a possible feature.
Selection Criterion
Length or Lightness (weight), which one is a better feature?
No value of either feature will “classify” all fish correctly
14. Fish xT = [x1, x2]
Lightness Width
Selection Criterion and Decision Boundary
Adopt the lightness and add the width of the fish
15. Decision Boundary Selection
Which of the boundaries would you
choose?
Simple linear boundary (training error > 0)
Nonlinear complex boundary (tr. error = 0)
Simpler nonlinear boundary (tr. error > 0)
16. Ideally, the best decision boundary should be the one which provides
an optimal performance such as in the following figure:
However, the central aim of designing a classifier is to correctly
classify novel input (classify future samples)
Issue of generalization
Generalization and Decision Boundary
17. Selected decision boundary
A simpler but generalized boundary in this case may be the
simple nonlinear curve as shown below:
Further features can be added to enhance the performance
However redundancy in information may not be desirable
18. A classifier, intuitively, is designed to minimize classification error,
the total number of instances (fish) classified incorrectly.
– Is this the best objective (cost) function to minimize? What kinds
of error can be made? Are they all equally bad? What is the real
cost of making an error?
• Sea bass misclassified as salmon: Pleasant surprise for the
consumer, tastier fish
• Salmon misclassified as sea bass: Customer upset, paid too
much for inferior fish
– We may want to adjust our decision boundary to minimize overall
risk –in this case, second type error is more costly, so we may
want to minimize this error.
Adjustment of decision boundary
20. Terminologies in PR
Features: a set of variables believed to carry discriminating and
characterizing information about the objects under consideration
Feature vector: A collection of d features, ordered in some
meaningful way into a d-dimensional column vector, that
represents the signature of the object to be identified.
Feature space: The d-dimensional space in which the feature
vectors lie. A d-dimensional vector in a d-dimensional space
constitutes a point in that space.
21. Terminologies in PR
Decision boundary:A boundary in the d-dimensional feature space
that separates patterns of different classes from each other.
Training Data: Data used during training of a classifier for which
the correct labels are known
Field Test Data: Unknown data to be classified –for measurements
from which the classifier is trained. The correct class of this data
are not known
22. Terminologies in PR
Cost Function: A quantitative measure that represents the cost of
making an error. The classifier is trained to minimize this
function.
Classifier: An algorithm which adjusts its parameters to find the
correct decision boundaries –through a learning algorithm using
a training dataset –such that a cost function is minimized.
Error: Incorrect labeling of the data by the classifier
Cost of error: Cost of making a decision, in particular an
incorrect one –not all errors are equally costly!
Training Performance: The ability / performance of the classifier
in correctly identifying the classes of the training data, which it
has already seen. It may not be a good indicator of the
generalization performance.
24. Good Features vs. Bad Features
Ideally, for a given group of patterns coming from the same class,
feature values should all be similar
For patterns coming from different classes, the feature values
should be different
26. What is the cost of making a decision –what is the risk of
an incorrect decision?
Determine the right type of classifier, right type of
training algorithm and right type of classifier parameters.
How to choose a classifier that minimizes the risk and
maximizes the performance? Can we add prior
knowledge, context to the classifier?
Identify and extract features relevant to distinguishing sea
bass from salmon, but invariant to noise, occlusion
rotations, scale, shift. What if we already have irrelevant
features? Dimensionality reduction?
Separate & isolate fish from background image
CCD Camera on a conveyor belt
Fish
Components of Pattern Recognition System
27. Example of PR
Consider the problem of recognizing the letters L,P,O,E,Q
– Determine a sufficient set of features
– Design a tree-structured classifier
1
29. Bayes Rule
We now pose the following question: Given that the event A has
occurred. What is the probability that any single one of the event
B’s occur?
This is known as the Bayes rule
i
i i i
30. The Real World: The World of Many Dimensions
In most practical applications, we have more then one feature, and
therefore the random variable x must be replaced with a random
vector x. P(x) P(x)
The joint probability mass function P(x) still satisfies the axioms of
probability
The Bayes rule is then
While the notation changes only slightly, the implications are quite
substantial
31. Bayes Decision theory
Based on quantifying the trade offs betweens various classification
decisions using a probabilistic approach
The theory assumes:
– Decision problem can be posed in probabilistic terms
– All relevant probability values are known (in practice this is not
true)
32. The sea bass/salmon example
State of nature: w1: Sea bass, w2: Salmon
Assume that we know the probabilities of observing sea bass and
salmons, P(w1) and P(w2) (prior probabilities), for a particular
location of fishing and time of year
Based on this information, how would you guess that the type of
the next fish to be caught?
• Decide w1 if P(w1) > P(w2) otherwise decide w2
A reasonable decision rule?
If the catch of salmon and sea bass is equi-probable
P(w1) = P(w2) (uniform priors)
P(w1) + P( w2) = 1 (exclusivity and exhaustivity)
This rule will not be applicable
34. Posterior Probability
Suppose, we know P(w1), P(w2), p(x|w1) and p(x|w2) and that we have observed
the value of the feature (a random variable) x
– How would you decide on the “state of nature”–type of fish, based on this
info?
– Bayes theory allows us to compute the posterior probabilities from prior and
class-conditional probabilities
Posterior Probability: The
(conditional) probability of
correct class being ùj, given
that feature value x has been
observed
Likelihood: The (class-conditional) probability of
observing a feature value of x, given that the correct class is
wj. All things being equal, the category with higher class
conditional probability is more “likely”to be the correct
class.
Prior Probability:The total
probability of correct class
being class wj determined
based on prior experience
Evidence:The total
probability of observing
the feature value as x
j
35. Posterior Probabilities
Bayes rule allows us to compute the posterior probability (difficult to
determine) from prior probabilities, likelihood and the evidence (easier to
determine).
Posterior probabilities for
priors P(w1) = 2/3 and P(w2)
= 1/3. For example, given
that a pattern is measured
to have feature value x =14,
the probability it is in
category w2 is roughly 0.08,
and that it is in w1 is 0.92. At
every x, the posteriors sum
to 1.0.
Which class would you choose now?
What is probability of making an error with this decision?
36. Bayes Decision Rule
Decision given the posterior probabilities
X is an observation for which:
if P(w1 | x) > P(w2 | x) True state of nature = w1
if P(w1 | x) < P(w2 | x) True state of nature = w2
That is Choose the class that has the larger posterior probability !
If there are multiple features, x={x1, x2,…, xd} and multiple classes
Choose wi if P(wi| x) > P(wj| x) for all i = 1, 2, …, c
Error:
whenever we observe a particular x, the probability of error is :
P(error | x) = P(w1 | x) if we decide w2
P(error | x) = P(w2 | x) if we decide w1
Therefore:
P(error | x) = min [P(w1 | x), P(w2 | x)]
(Bayes decision)
37. Bayesian Decision Theory – Generalization
Generalization of the preceding ideas
– Use of more than one feature
– Use more than two states of nature
– Allowing other actions and not only decide on the state of
nature
– Unequal error cost
Allowing actions other than classification primarily allows the
possibility of rejection: refusing to make a decision in close or bad
cases!
The loss function states how costly each action taken is
38. Two-category classification
Definitions
a1 : deciding w1
a2 : deciding w2
eij = e(ai | wj), loss incurred for deciding wi when the true state of nature
is wj
Conditional risk:
R(a1 | x) = e11P(w1 | x) + e12P(w2 | x)
R(a2 | x) = e21P(w1| x) + e22P(w2 | x)
Our rule is the following:
if R(a1| x) < R(a2| x) action a1: “decide w1” is taken