presentation of pattern recognition

What Is Pattern Recognition?

Pattern recognition is an automated process thanks to the availability of computer power to ingest data, process it, recognize its patterns and share it for further analysis. Here’s how pattern recognition works.

Pattern recognition is a process for automating the identification and exploration of patterns in data sets . Since there’s no single way to recognize data patterns, pattern recognition ultimately depends on:

• The ultimate goal of any given pattern recognition workflow
• The type of data available (quantitative vs. qualitative, time series data vs. point-in-time data)
• The computing power and storage available to process and manage the data

More From Edoardo Romani What Is Database Normalization?

How Pattern Recognition Works

Pattern recognition is a process made of the same steps that anyone concerned with finding patterns in data goes through.

Pattern Recognition Process

• Define the problem
• Be aware of the null hypothesis
• Choose a methodology
• Measure uncertainty
• Test and iterate over the results

1. Define the Problem

Defining the problem is always the first step in any pattern recognition project. This is where you formulate research questions or hypotheses regarding the data and its patterns. For example, you may be concerned with capturing holiday and seasonal effects (patterns) in shopping data coming from shopping malls’ databases . A specific question we may want to ask about this data is whether shoppers tend to display sensitive responses to specific promotions or discounts the company launches through email marketing campaigns and whether these tend to distribute in any particular way throughout the year.

2. Be Aware of the Null Hypothesis 

In the field of statistics and hypothesis testing, searching to prove the existence of a relationship between variables and finding none is called accepting the null hypothesis. Not all data may have patterns hidden within it. Moving into the analysis, it’s important to remember that the process of pattern recognition may also not yield results. That is to say, you may be looking for patterns where there simply are none.

3. Choose a Methodology

There are many different ways to find patterns and it’s important to evaluate all potential models that may apply to the problem at hand. After all, there may be more than one.

4. Measure Uncertainty 

Models used to find data patterns are as accurate as they can be within an uncertain world. It’s important to treat pattern recognition under a probabilistic lens to factor in uncertainty, especially when pattern recognition is put to use for predictive purposes. 

5. Test and Iterate Over the Results

Constant iteration over pattern recognition processes is necessary to ensure optimal results and avoid losing relevance or accuracy as time passes. Once you’ve landed on a problem and model, and measured patterns, it’s important to remember that the workflow does not stop there. 

Keep testing pattern recognition methods to make sure they accurately capture trends in the underlying data even as time and conditions go on.

Features of Pattern Recognition 

Pattern recognition has several applications, but there are a few key tenets that are common regardless of the domain.

Statistical Approach

Pattern recognition is rooted in statistics . When we’re finding patterns in data, we always need to account for variability, uncertainty and the probable distributions, if any, that data holds.

The field of statistics is also the precursor to modern pattern recognition approaches. As a result, a statistical lens is appropriate for most, if not all, modern pattern recognition applications.

More From the Built In Tech Dictionary What Is Statistical Analysis?

Algorithmic Nature

An algorithm is a procedure that follows a precise sequence of steps. Depending on the nature of the problem and the kind of data at hand, you can use many different algorithms.

The main groups of algorithms used for pattern recognition include:

• Classification Algorithms
• Ensemble Learning
• Regression Algorithms
• Sequence Labeling Methods

Data Categorization

While you’re defining a pattern recognition project’s problem, your main concern is usually fitting the data into specific categories, or labels, that are linked to the underlying patterns the data holds.

For example, in time series data analysis , you may be most concerned with understanding the seasonal component of monthly sales data, a category specific to the seasonal pattern you see in the data. You might see sales spikes during the Christmas holiday season.

Reliance on Abundant Data and Processing Power

Pattern recognition has become increasingly prevalent since the technological advances in computing started around the turn of the 21st century. With these advances we can:

• process more data
• process data faster (given equal data size) thanks to making use of grid computing , which is the use of many different computers to distribute the computational load across a higher number of servers
• store data less expensively thanks to the rise of modern cloud database management solutions

Advantages of Pattern Recognition

High automation potential.

Pattern recognition workflows have the benefit of being a great fit for full end-to-end automation. This means we can configure, program and structure pattern recognition workflows to run with minimal human intervention, once we’ve completed the initial setup and analysis.

In other words, teams developing pattern recognition solutions can benefit from a low-touch, high-return analytical workflow. 

Efficiency 

Automation also brings an additional advantage, which is letting subject-matter experts focus on the least intuitive and most complex parts of pattern recognition problems. This is resource-efficient because it brings down the cost of labor and overall time dedicated to developing solutions.

Most organizations can also benefit from plug-and-play situations wherein they simply translate similar pattern recognition problems to their domain with minimal effort. Examples of this include re-using code and/or algorithms already developed by others, especially if they’re available from open-source projects.

Applications for Descriptive and Predictive Analytics 

Pattern recognition is incredibly flexible because it can be used to extract trends from historical data and diagnose what happened in the past (descriptive pattern recognition). We can also use pattern recognition methodologies to make inferences about the future (predictive pattern recognition).

Get More From Built In Experts Intro to Descriptive Statistics for Machine Learning

Examples of Pattern Recognition

Cybersecurity and voice detection.

A cybersecurity company selling digital security services to client firms can use pattern recognition to develop software that automatically recognizes who is speaking from audio files coming from employee phone calls. We can then use this technology for any number of applications where there may be a use case for monitoring professional phone calls for security or training purposes.

Healthcare Technology and Medical Diagnosis

A medical institution is concerned with helping doctors in identifying early-stage cancer development. Using pattern recognition and a set of digital images , the organization can detect early-stage cancer with high probability, thereby helping patients receive earlier treatment with a higher probability of success.

Marketing and Customer Churn Prevention

A grocery store chain is interested in monitoring its base of loyalty card customers for early indications of customer attrition. The company is interested in this information so it can react promptly by offering incentives and additional offers to these customers to stop them from churning .

We can also put pattern recognition algorithms to good use on the chain’s customer data set to cluster them into different levels of churn probability and identify the churn prevention initiative’s target customers.

Applications of Pattern Recognition

Computer vision.

Pattern recognition methodologies are incredibly popular in computer vision . We can put pattern recognition methodologies to use to programmatically develop applications that derive knowledge from images, and effectively understand them as a human being might.

Machine Learning

Machine learning , a subset of data science , makes use of computing power to derive insights from data using specific learning algorithms. This is one of the most prevalent current applications of pattern recognition and is at the heart of the advancements in AI development in most industries. 

Time Series Analysis

Time series data is essentially logs of data over time. Historical stock prices are an example of time series data. You might also think about sensor and telemetry data from video cameras.

Pattern recognition is key to understanding, analyzing, and even forecasting time series data . This is because time series data is filled with different components (or patterns) that are useful to extract and understand to make sense of the data.

Examples of these time series data components are seasonal effects (such as the ones determined by the Black Friday shopping season for example) and cyclical effects (longer-term trends, such as the steady growth in the value of the stock market).

Recent Algorithms Articles

• My presentations

Auth with social network:

Download presentation

We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!

Presentation is loading. Please wait.

What is Pattern Recognition?

Published by Malcolm Ramsey Modified over 5 years ago

Similar presentations

Presentation on theme: "What is Pattern Recognition?"— Presentation transcript:

Introduction to Machine Learning BITS C464/BITS F464

Godfather to the Singularity

INTRODUCTION TO MACHINE LEARNING David Kauchak CS 451 – Fall 2013.

Lesson 6: Telemedicine. What is telemedicine? Telemedicine is the remote diagnosis and treatment of patients by means of telecommunications technology.

ETHEM ALPAYDIN © The MIT Press, Lecture Slides for.

Introduction to Machine Learning Anjeli Singh Computer Science and Software Engineering April 28 th 2008.

CIS 678 Artificial Intelligence problems deduction, reasoning knowledge representation planning learning natural language processing motion and manipulation.

An Introduction to Machine Learning In the area of AI (earlier) machine learning took a back seat to Expert Systems Expert system development usually consists.

Intelligent Systems Group Emmanuel Fernandez Larry Mazlack Ali Minai (coordinator) Carla Purdy William Wee.

Lecture #1COMP 527 Pattern Recognition1 Pattern Recognition Why? To provide machines with perception & cognition capabilities so that they could interact.

Learning Programs Danielle and Joseph Bennett (and Lorelei) 4 December 2007.

INTRODUCTION TO Machine Learning ETHEM ALPAYDIN © The MIT Press, Lecture Slides for.

INTRODUCTION TO Machine Learning 3rd Edition

Presented To: Madam Nadia Gul Presented By: Bi Bi Mariam.

Introduction to machine learning

Introduction to Data Mining Engineering Group in ACL.

CS Machine Learning. What is Machine Learning? Adapt to / learn from data  To optimize a performance function Can be used to:  Extract knowledge.

Introduction Lecture 1 Intro to ALS  These lecture notes accompany the book on ALS  They can be used with the book and the software for courses on.

Copyright R. Weber INFO 629 Concepts in Artificial Intelligence Fall 2004 Professor: Dr. Rosina Weber.

METU Informatics Institute Min720 Pattern Classification with Bio-Medical Applications Lecture Notes by Neşe Yalabık Spring 2011.

About project

© 2024 SlidePlayer.com Inc. All rights reserved.

• Trending Now
• Foundational Courses
• Data Science
• Practice Problem
• Machine Learning
• System Design
• DevOps Tutorial

Pattern Recognition | Introduction

Pattern is everything around in this digital world. A pattern can either be seen physically or it can be observed mathematically by applying algorithms. 

Example: The colors on the clothes, speech pattern, etc. In computer science, a pattern is represented using vector feature values.  What is Pattern Recognition?   Pattern recognition is the process of recognizing patterns by using a machine learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation. One of the important aspects of pattern recognition is its application potential. 

Examples: Speech recognition, speaker identification, multimedia document recognition (MDR), automatic medical diagnosis.  In a typical pattern recognition application, the raw data is processed and converted into a form that is amenable for a machine to use. Pattern recognition involves the classification and cluster of patterns. 

• In classification, an appropriate class label is assigned to a pattern based on an abstraction that is generated using a set of training patterns or domain knowledge. Classification is used in supervised learning.
• Clustering generated a partition of the data which helps decision making, the specific decision-making activity of interest to us. Clustering is used in unsupervised learning.

Features may be represented as continuous, discrete, or discrete binary variables. A feature is a function of one or more measurements, computed so that it quantifies some significant characteristics of the object. 

Example: consider our face then eyes, ears, nose, etc are features of the face.  A set of features that are taken together, forms the features vector . 

Example: In the above example of a face, if all the features (eyes, ears, nose, etc) are taken together then the sequence is a feature vector([eyes, ears, nose]). The feature vector is the sequence of a feature represented as a d-dimensional column vector. In the case of speech, MFCC (Mel-frequency Cepstral Coefficient) is the spectral feature of the speech. The sequence of the first 13 features forms a feature vector.  Pattern recognition possesses the following features:  

• Pattern recognition system should recognize familiar patterns quickly and accurate
• Recognize and classify unfamiliar objects
• Accurately recognize shapes and objects from different angles
• Identify patterns and objects even when partly hidden
• Recognize patterns quickly with ease, and with automaticity.

Training and Learning in Pattern Recognition   Learning is a phenomenon through which a system gets trained and becomes adaptable to give results in an accurate manner. Learning is the most important phase as to how well the system performs on the data provided to the system depends on which algorithms are used on the data. The entire dataset is divided into two categories, one which is used in training the model i.e. Training set, and the other that is used in testing the model after training, i.e. Testing set. 

• Training set:   The training set is used to build a model. It consists of the set of images that are used to train the system. Training rules and algorithms are used to give relevant information on how to associate input data with output decisions. The system is trained by applying these algorithms to the dataset, all the relevant information is extracted from the data, and results are obtained. Generally, 80% of the data of the dataset is taken for training data.
• Testing set:   Testing data is used to test the system. It is the set of data that is used to verify whether the system is producing the correct output after being trained or not. Generally, 20% of the data of the dataset is used for testing. Testing data is used to measure the accuracy of the system. For example, a system that identifies which category a particular flower belongs to is able to identify seven categories of flowers correctly out of ten and the rest of others wrong, then the accuracy is 70 %

Real-time Examples and Explanations:   A pattern is a physical object or an abstract notion. While talking about the classes of animals, a description of an animal would be a pattern. While talking about various types of balls, then a description of a ball is a pattern. In the case balls considered as pattern, the classes could be football, cricket ball, table tennis ball, etc. Given a new pattern, the class of the pattern is to be determined. The choice of attributes and representation of patterns is a very important step in pattern classification. A good representation is one that makes use of discriminating attributes and also reduces the computational burden in pattern classification. 

An obvious representation of a pattern will be a vector . Each element of the vector can represent one attribute of the pattern. The first element of the vector will contain the value of the first attribute for the pattern being considered. 

Example: While representing spherical objects, (25, 1) may be represented as a spherical object with 25 units of weight and 1 unit diameter. The class label can form a part of the vector. If spherical objects belong to class 1, the vector would be (25, 1, 1), where the first element represents the weight of the object, the second element, the diameter of the object and the third element represents the class of the object.  Advantages:   

• Pattern recognition solves classification problems
• Pattern recognition solves the problem of fake biometric detection.
• It is useful for cloth pattern recognition for visually impaired blind people.
• It helps in speaker diarization.
• We can recognize particular objects from different angles.

Disadvantages:   

• The syntactic pattern recognition approach is complex to implement and it is a very slow process.
• Sometimes to get better accuracy, a larger dataset is required.
• It cannot explain why a particular object is recognized.  Example: my face vs my friend’s face.

Applications:   

• Image processing, segmentation, and analysis   Pattern recognition is used to give human recognition intelligence to machines that are required in image processing.
• Computer vision   Pattern recognition is used to extract meaningful features from given image/video samples and is used in computer vision for various applications like biological and biomedical imaging.
• Seismic analysis   The pattern recognition approach is used for the discovery, imaging, and interpretation of temporal patterns in seismic array recordings. Statistical pattern recognition is implemented and used in different types of seismic analysis models.
• Radar signal classification/analysis   Pattern recognition and signal processing methods are used in various applications of radar signal classifications like AP mine detection and identification.
• Speech recognition   The greatest success in speech recognition has been obtained using pattern recognition paradigms. It is used in various algorithms of speech recognition which tries to avoid the problems of using a phoneme level of description and treats larger units such as words as pattern
• Fingerprint identification   Fingerprint recognition technology is a dominant technology in the biometric market. A number of recognition methods have been used to perform fingerprint matching out of which pattern recognition approaches are widely used.

Imagine we have a dataset containing information about apples and oranges. The features of each fruit are its color (red or yellow) and its shape (round or oval). We can represent each fruit using a list of strings, e.g. [‘red’, ’round’] for a red, round fruit.

Our goal is to write a function that can predict whether a given fruit is an apple or an orange. To do this, we will use a simple pattern recognition algorithm called k-nearest neighbors (k-NN).

Here is the function in Python:

 ‘apple’

Please Login to comment...

Similar reads, improve your coding skills with practice.

What kind of Experience do you want to share?

Computer Science & Engineering Department

Cs 479/679 pattern recognition (spring 2024), required text:, optional texts:, important resources, useful software:, course description, course outline (tentative), course prerequisites, exams and assignments, course policies.

• Midterm Exam

Programming Assignments

Paper presentation (graduate students only), presentation guidelines.

Pattern Recognition

Nov 09, 2019

550 likes | 702 Views

Pattern Recognition. Speaker: Wen-Fu Wang Advisor: Jian-Jiun Ding E-mail: [email protected] Graduate Institute of Communication Engineering National Taiwan University, Taipei, Taiwan, ROC. Outline. Introduction Minimum Distance Classifier Matching by Correlation

Share Presentation

• pattern recognition
• pattern classes
• bayes classifier
• matching shape numbers
• 8 directional chain code

Presentation Transcript

Pattern Recognition • Speaker: Wen-Fu Wang • Advisor: Jian-Jiun Ding • E-mail: [email protected] • Graduate Institute of Communication Engineering • National Taiwan University, Taipei, Taiwan, ROC

Outline • Introduction • Minimum Distance Classifier • Matching by Correlation • Optimum statistical classifiers • Matching Shape Numbers • String Matching

Outline • Syntactic Recognition of Strings String Grammars • Syntactic recognition of Tree Grammars • Conclusions

Sensor Feature generation Feature selection Classifier design System evaluation Introduction • Basic pattern recognition flowchart

Introduction • The approaches to pattern recognition developed are divided into two principal areas: decision-theoretic and structural • The first category deals with patterns described using quantitative descriptors, such as length, area, and texture • The second category deals with patterns best described by qualitative descriptors, such as the relational descriptors.

Minimum Distance Classifier • Suppose that we define the prototype of each pattern class to be the mean vector of the patterns of that class: • Using the Euclidean distance to determine closeness reduces the problem to computing the distance measures j=1,2,…,W(1) j=1,2,…,W (2)

Minimum Distance Classifier • The smallest distance is equivalent to evaluating the functions • The decision boundary between classes and for a minimum distance classifier is j=1,2,…,W(3) j=1,2,…,W (4)

Minimum Distance Classifier • Decision boundary of minimum distance classifier

Minimum Distance Classifier • Advantages: 1. Unusual direct-viewing 2. Can solve rotation the question 3. Intensity 4. Chooses the suitable characteristic, then solves mirror problem 5. We may choose the color are one kind of characteristic, the color question then solve.

Minimum Distance Classifier • Disadvantages: 1. It costs time for counting samples, but we must have a lot of samples for high accuracy, so it is more samples more accuracy! 2. Displacement 3. It is only two features, so that the accuracy is lower than other methods. 4. Scaling

Matching by Correlation • We consider it as the basis for finding matches of a sub-image of size within an image of size , where we assume that and for x=0,1,2,…,M-1,y=0,1,2,…,N-1(5)

Origin K J o M Matching by Correlation • Arrangement for obtaining the correlation of and at point

Matching by Correlation • The correlation function has the disadvantage of being sensitive to changes in the amplitude of and • For example, doubling all values of doubles the value of • An approach frequently used to overcome this difficulty is to perform matching via the correlation coefficient • The correlation coefficient is scaled in the range-1 to 1, independent of scale changes in the amplitude of and

Matching by Correlation • Advantages: 1.Fast 2.Convenient 3.Displacement • Disadvantages: 1.Scaling 2.Rotation 3.Shape similarity 4.Intensity 5.Mirror problem 6.Color can not recognition

Optimum statistical classifiers • The probability that a particular pattern x comes from class is denoted • If the pattern classifier decides that x came from when it actually came from , it incurs a loss, denoted

Optimum statistical classifiers • From basic probability theory, we know that

Optimum statistical classifiers • Thus the Bayes classifier assigns an unknown pattern x to class

Optimum statistical classifiers • The Bayes classifier then assigns a pattern x to class if, • or, equivalently, if

Optimum statistical classifiers • Bayes Classifier for Gaussian Pattern Classes • Let us consider a 1-D problem (n=1) involving two pattern classes (W=2) governed by Gaussian densities

Optimum statistical classifiers • In the n-dimensional case, the Gaussian density of the vectors in the jth pattern class has the form

Optimum statistical classifiers • Advantages: 1. The way always combine with other methods, then it got high accuracy • Disadvantages: 1.It costs time for counting samples 2.It has to combine other methods

1 2 3 1 0 4 0 2 7 5 6 3 Matching Shape Numbers • Direction numbers for 4-directional chain code, and 8-directional chain code

Matching Shape Numbers • Digital boundary with resampling grid superimposed

Order6 Order4 Chain code: 0321 Difference : 3333 Shape no. : 3333 Chain code: 003221 Difference : 303303 Shape no. : 033033 Order8 Chain code: 00332211 Difference : 30303030 Shape no. : 03030303 Chain code:03032211 Difference :33133030 Shape no. :03033133 Chain code: 00032221 Difference : 30033003 Shape no. : 00330033 Matching Shape Numbers • All shapes of order 4, 6,and 8

Matching Shape Numbers • Advantages: 1. Matching Shape Numbers suits the processing structure simple graph, specially becomes by the line combination 2. Can solve rotation the question 3. Matching Shape Numbers most emphatically to the graph outline, Shape similarity also may completely overcome 4. The Displacement question definitely may overcome, because of this method emphatically to the relative position but is not to the position

Matching Shape Numbers • Disadvantages : 1. It can not uses for a hollow structure 2. Scaling is a shortcoming which needs to change, perhaps coordinates the alternative means 3. Intensity 4. Mirror problem 5. The color is unable to recognize

String Matching • Suppose that two region boundaries, a and b, are coded into strings denoted and ,respectively • Let represent the number of matches between the two strings, where a match occurs in the kth position if

String Matching • A simple measure of similarity between and is the ratio • Hence R is infinite for a perfect match and 0 when none of the corresponding symbols in and match ( in this case)

b b b b b b String Matching • Simple staircase structure. • Coded structure.

String Matching • Advantages: 1.Matching Shape Numbers suits the processing structure simple graph, specially becomes by the line combination 2.Can solve rotation the question 3.Intensity 4.Mirror problem 5.Matching Shape Numbers most emphatically to the graph outline, Shape similarity also may completely overcome 6. The Displacement question definitely may overcome, because of this method emphatically to the relative position but is not to the position

String Matching • Disadvantages: 1.It can not uses for a hollow structure 2.Scaling 3.The color is unable to recognize

Syntactic Recognition of Strings String Grammars • When dealing with strings, we define a grammar as the 4-tuple • is a finite set of variables called non-terminals, • is a finite set of constants called terminals, • is a set of rewriting rules called productions, • in is called the starting symbol.

Syntactic Recognition of Strings String Grammars • Object represented by its skeleton • primitives. • structure generated by using a regular string grammar b a c

Syntactic Recognition of Strings String Grammars • Advantages: 1.This method may use to a more complex structure 2.It is a good method for character set • Disadvantages: 1.Scaling 2.Rotation 3.The color is unable to recognize 4.Intensity 5.Mirror problem

Syntactic Recognition of Tree Grammars • A tree grammar is defined as the 5-tuple • and are sets of non-terminals and terminals, respectively • is the start symbol, which in general can be a tree • is a set of productions of the form , where and are trees • is a ranking function that denotes the number of direct descendants(offspring) of a node whose label is a terminal in the grammar

Syntactic Recognition of Tree Grammars • Of particular relevance to our discussion are expansive tree grammars having productions of the form • where are not terminals and k is a terminal

Syntactic Recognition of Tree Grammars • An object • Primitives used for representing the skeleton by means of a tree grammar c e b a d

Syntactic Recognition of Tree Grammars • For example c e b a d

Syntactic Recognition of Tree Grammars • Advantages: 1. This method may use to a more complex structure 2. It is a good method for character set 3. The Displacement question definitely may overcome, because of this method emphatically to the relative position but is not to the position

Syntactic Recognition of Tree Grammars • Disadvantages : 1. Scaling is a shortcoming which needs to change, perhaps coordinates the alternative means 2. Rotation 3. The color is unable to recognize 4. Intensity

Conclusions • The graph recognizes is covers the domain very widespread science, in the past dozens of years, all kinds of method is unceasingly excavated, also acts according to all kinds of probability statistical model and the practical application model but unceasingly improves. • The graph recognizes applies to each different application domain, actually often also simultaneously entrusts with the entire wrap to recognize the system different appearance, which methods thus we certainly are unable to define to are "best" the graph recognize the method.

Conclusions • Summary the seven approach to pattern recognition, each methods has advantages and disadvantages respectively. Therefore, we have to understand each method preciously. Then we choose the adaptable method for efficiency and accuracy. • The A method has obtained extremely good recognizing rate in some application and is unable to express the similar method applies mechanically in another application also can similarly obtain extremely good recognizing rate.

Conclusions • Below provides several possibilities solutions the method • 1. Scaling problem we may the reference area solve. • 2. Neural networks solves for rotation problem. • 3.The color question besides uses RBG to solve also may use the spectrum to recognize differently. • 4. Doing correlation with the reverse match filter for Intensity mirror problem • 5. We can use the measure of area for a hollow structure

References • [1] R. C. Gonzolez, R. E. Woods, "Digital Image Processing, Second Edition", Prentice Hall 2002 • [2] 蒙以正, "數位信號處理應用Matlab",旗標 2005 • [3] S. Theodoridis, K. koutroumbas, "Pattern Recognition", Academic Press 1999 • [4] W. K. Pratt ,"Digital Image Processing, Third Edition", John Wiley & Sons 2001 • [5] R. C. Gonzolez, R. E. Woods, S. L. Eddins, "Digital Image Processing Using MATLAB", Prentice Hall 2005 • [6] 繆紹綱, 數位影像處理 活用-Matlab, 全華2000 • [7] J. Schurmann, " A Unified View of Statistical and Neural Approaches" Pattern Classification, Chap4, John Wiley & Sons, Inc., 1996

References • [8]K. Fukunaga, “Introduction to Statistical Pattern Recognition”, Second Edition, Academic Press, Inc.,1990 • [9] E. Gose, R. Johnsonbaugh, and Steve Jost, "Pattern recognition and Image Analysis", Prentice Hall Inc., New Jersey, 1996 • [10] Robert J. Schalkoff, "Pattern Recognition: Statical, Structural and Neural Approaches", Chap5, John Wiley & Sons, Inc., 1992 • [11] J. S. Pan, F. R. Mclnnes, and M. A. Jack, "Fast Clustering Algorithm for Vector Quantization", Pattern Recognition 29, 511-518, 1996

• More by User

Pattern Recognition. NTUEE 高奕豪 2005/4/14. Outline. Introduction Definition, Examples, Related Fields, System, and Design Approaches Bayesian, Hidden Markov Model, Neural Network, Decision Tree Modern Applications Face, Handwriting, Fingerprint, Speech.

992 views • 40 slides

Pattern recognition

Pattern recognition. Lecture 16 – Linear Discriminant Analysis. Professor Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville URL: www.cvip.uofl.edu ; E-mail: [email protected]. Introduction.

558 views • 29 slides

Pattern Recognition. Pattern recognition is:. 1. The name of the journal of the Pattern Recognition Society. 2. A research area in which patterns in data are found, recognized, discovered, …whatever. 3. A catchall phrase that includes. classification clustering data mining

691 views • 33 slides

Pattern Recognition. Pattern - complex composition of sensory stimuli that the human observer may recognize as being a member of a class of objects Issue - what cognitive mechanisms need to be inferred to describe this process of recognition?. Bridge with Signal Detection.

919 views • 38 slides

Pattern Recognition. 1/ 6/2009 Instructor: Wen-Hung Liao, Ph.D. Biometrics. Outline. Basic Concepts Fingerprint Iris Scan Hand Geometry Face Recognition. Identification vs Verification. Identification: Who am I? One-to-many search

467 views • 26 slides

Pattern Recognition. CIS 786 Prof. Barry Cohen Pavan Tipirneni Niranjan Mulay Rana Farha Ketal Patel. What is Pattern Recognition?.

1.38k views • 63 slides

Pattern recognition. Lecture 15 – Non Parametric Methods. Professor Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville URL: www.cvip.uofl.edu ; E-mail: [email protected]. Introduction.

601 views • 43 slides

Pattern Recognition. Pattern recognition is:. 1. A research area in which patterns in data are found, recognized, discovered, …whatever. 2. A catchall phrase that includes. classification clustering data mining ….

359 views • 16 slides

Pattern Recognition. Ku-Yaw Chang [email protected] Assistant Professor, Department of Computer Science and Information Engineering Da-Yeh University. Outline. Introduction Features and Classes Supervised v.s. Unsupervised Statistical v.s. Structural (Syntactic)

345 views • 19 slides

Pattern Recognition. Ku-Yaw Chang [email protected] Assistant Professor, Department of Computer Science and Information Engineering Da-Yeh University. Course Information. Reference books

371 views • 11 slides

Pattern Recognition. What Is Pattern Management ?. A systematic approach to making effective use of SMBG data. Establishing pre- and postprandial glucose targets Obtaining data Analyzing data Assessing the impact of therapeutic changes. SMBG= self-monitored blood glucose

221 views • 6 slides

Bayesian Decision Theory & ML Estimation. Pattern Recognition. Bayesian Decision Theory. Bayesian Decision Theory. Fundamental statistical approach to problem classification.

565 views • 30 slides

Chapter 3 Maximum Likelihood and Bayesian Estimation – Part1. Pattern Recognition. Practical Issues. We could design an optimal classifier if we knew: P(  i ) (priors) p(x/  i ) (class-conditional densities) In practice, we rarely have this complete information!

274 views • 17 slides

Pattern Recognition. Dr. R. J. Ramteke Associate Professor, Dept. of Computer Science North Maharashtra University, Jalgaon. More refined and abstract. Wisdom. Knowledge. Information. Data. Information Hierarchy. Information Hierarchy. Data The raw material of information

353 views • 20 slides

Pattern Recognition. Probability Review. Why Bother About Probabilities?. Accounting for uncertainty is a crucial component in decision making (e.g., classification) because of ambiguity in our measurements. Probability theory is the proper mechanism for accounting for uncertainty .

650 views • 49 slides

Pattern Recognition. K-Nearest Neighbor Explained By Arthur Evans John Sikorski Patricia Thomas. Overview. Pattern Recognition, Machine Learning, Data Mining: How do they fit together? Example Techniques K-Nearest Neighbor Explained. Data Mining.

660 views • 44 slides

710 views • 63 slides

Introduction to bioinformatics 2006 Lecture 4. Pattern Recognition. Patterns Some are easy some are not. Knitting patterns Cooking recipes Pictures (dot plots) Colour patterns Maps.

575 views • 51 slides

Pattern recognition. in high energy physics. Measurement system. Measurement system. Measurement system.

472 views • 45 slides

Pattern Recognition. Mathematic Review Hamid R. Rabiee Jafar Muhammadi Ali Jalali. Probability Space. A triple of ( Ω , F, P) Ω represents a nonempty set, whose elements are sometimes known as outcomes or states of nature

471 views • 40 slides

365 views • 26 slides

• Preferences

Pattern Recognition - PowerPoint PPT Presentation

• Pattern Recognition

What do you see in this photograph? It's a Dalmatian! What's this? Is this A Frog? ... One thing is clear, pattern recognition is heavily influenced by context ... – PowerPoint PPT presentation

• Participant is presented a letter by itself or in the context of other letters
• After the letter is presented, the participant identifies which letter had been presented by choosing from two alternatives.
• Bottom-up processing Perceptual experience is built up from incoming sensory information (also referred to as data driven processing)
• Top-down processing Perceptual experience is influenced by ones knowledge and expectations about the world. (also referred to as conceptually driven processing)
• Template Theory
• Feature Theory
• Structural Theory
• The stimulus pattern is compared to templates stored in the brain
• The stimulus pattern is recognized when it is matched against a template
• It would seem to require too many templates to be able to deal with stimulus variability
• It is unclear how it would account context effects (like those of illustrated in Reichers work)
• Stimulus patterns are recognized on the basis of features
• Features are the elementary components of a stimulus pattern
• A finite number of feature detectors potentially could recognize an infinite number of patterns
• This approach is better able to account for stimulus variability
• This approach more easily incorporates the notion of top-down processing
• Wouldnt the theory have to address the spatial arrangement of geons?
• How could we distinguish between to similar objects that consist of the same geons in the same spatial arrangements (e.g., a horse and a cow)?
• How could we recognize something like a puddle which does not have recurring geons?
• Close your eyes, then open and shut your eyes quickly. Do you see a lingering image?
• Look straight ahead. Wave your index finger in front of your eyes. Do you see a trailing afterimage?
• Iconic memory Visual sensory memory
• Echoic memory Auditory sensory memory
• Fall entirely on the fovea of the eye so that no eye movements are necessary
• Be presented for a sufficiently short duration that the person cant shift their attention
• Be presented at high contrast and followed by darkness
• Whole report Participant is required to report the whole matrix (or as much as he or she can)
• Partial report Participant is required to report only a portion of the matrix
• Only 1/3 of the letters registered by the visual system.
• All letters registered, but the participant only had sufficient time to process about 1/3 of the letters.
• If only 1/3 of the letters register, then the participant should only be able to recall 1/3 of the cued letters
• If all letters register, then the participant should recall all (or most?) of the cued letters
• Delay the cue more and more
• Determine the delay at which partial report performance no longer exceeds whole report performance
• At that delay there is no longer evidence of the icon
• That delay provides an estimate of how long the icon lasts, and hence, the duration of iconic memory
• Duration -- about .5 sec
• Capacity -- large (more items than the individual can report)
• Code -- visual (or sensory) code
• Categorical cues -- require that information be identified and categorized
• Pre-categorical cues -- do not require information to be identified and categorized.

PowerShow.com is a leading presentation sharing website. It has millions of presentations already uploaded and available with 1,000s more being uploaded by its users every day. Whatever your area of interest, here you’ll be able to find and view presentations you’ll love and possibly download. And, best of all, it is completely free and easy to use.

You might even have a presentation you’d like to share with others. If so, just upload it to PowerShow.com. We’ll convert it to an HTML5 slideshow that includes all the media types you’ve already added: audio, video, music, pictures, animations and transition effects. Then you can share it with your target audience as well as PowerShow.com’s millions of monthly visitors. And, again, it’s all free.

About the Developers

PowerShow.com is brought to you by  CrystalGraphics , the award-winning developer and market-leading publisher of rich-media enhancement products for presentations. Our product offerings include millions of PowerPoint templates, diagrams, animated 3D characters and more.

GlyphPattern: An Abstract Pattern Recognition for Vision-Language Models

• Kim, Yoolim
• Anderson, Carolyn Jane

Vision-Language Models (VLMs) building upon the foundation of powerful large language models have made rapid progress in reasoning across visual and textual data. While VLMs perform well on vision tasks that they are trained on, our results highlight key challenges in abstract pattern recognition. We present GlyphPattern, a 954 item dataset that pairs 318 human-written descriptions of visual patterns from 40 writing systems with three visual presentation styles. GlyphPattern evaluates abstract pattern recognition in VLMs, requiring models to understand and judge natural language descriptions of visual patterns. GlyphPattern patterns are drawn from a large-scale cognitive science investigation of human writing systems; as a result, they are rich in spatial reference and compositionality. Our experiments show that GlyphPattern is challenging for state-of-the-art VLMs (GPT-4o achieves only 55% accuracy), with marginal gains from few-shot prompting. Our detailed error analysis reveals challenges at multiple levels, including visual processing, natural language understanding, and pattern generalization.

• Computer Science - Computer Vision and Pattern Recognition;
• Computer Science - Computation and Language