This document provides an overview of an introduction to machine vision course. The course introduces concepts of machine vision including image formation and filtering. It addresses machine vision techniques such as feature detection, extraction, and pattern recognition. Students will explore applications and learn about enabling technologies. The course involves assignments, midterm and final exams to assess learning outcomes including understanding, applying, analyzing, evaluating, and designing approaches related to machine vision. Related fields, optical illusions, sample applications, software, and resources are also discussed.
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.
Object Detection and tracking in Video SequencesIDES Editor
This paper focuses on key steps in video analysis
i.e. Detection of moving objects of interest and tracking of
such objects from frame to frame. The object shape
representations commonly employed for tracking are first
reviewed and the criterion of feature Selection for tracking is
discussed. Various object detection and tracking approaches
are compared and analyzed.
A Lecture I gave to an Artificial Intelligence undergraduate class taught by Hien Nguyen, Ph.D. at the University of Wisconsin Whitewater in the fall of 2011
DIGITAL RESTORATION OF TORN FILMS USING FILTERING T ECHNIQUESAM Publications
The acceptance of digital imaging is motivating many photography enthusiasts to transfer their
photographic archive to digital form. Scans of negatives and positives are preferred to be scanned at high resolution
which makes small cracks and scratches very apparent. These unsightly defects have become an important issue
for consumers. Filtering techniques are used for the restoration process which is fully automatic whereas the existing
systems were semi-automatic or completely manual. The method used for the detection of tear is dilation process and
top-hat transform. Top-hat transform might misinterpret dark brush strokes as cracks. In order to avoid these
unwanted alterations to the original image, brush strokes are separated from the actual cracks using clustering
technique. Tear removal includes order statistics filtering which deals with the reconstruction of missing or
damaged image areas.
Overview Of Video Object Tracking SystemEditor IJMTER
The goal of video object tracking system is segmenting a region of interest from a video
scene and keeping track of its motion, positioning and occlusion. There are the three steps of video
object tracking system those are object detection, object classification and object tracking. Object
detection is performed to check existence of objects in video. Then the detected object can be
classified in various categories on the basis on their shape, motion, color and texture. Object tracking
is performed using monitoring object changes. This paper we are going to take overview of different
object detection, object classification and object tracking techniques and also the comparison of
different techniques used for various stages of tracking.
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.
Object Detection and tracking in Video SequencesIDES Editor
This paper focuses on key steps in video analysis
i.e. Detection of moving objects of interest and tracking of
such objects from frame to frame. The object shape
representations commonly employed for tracking are first
reviewed and the criterion of feature Selection for tracking is
discussed. Various object detection and tracking approaches
are compared and analyzed.
A Lecture I gave to an Artificial Intelligence undergraduate class taught by Hien Nguyen, Ph.D. at the University of Wisconsin Whitewater in the fall of 2011
DIGITAL RESTORATION OF TORN FILMS USING FILTERING T ECHNIQUESAM Publications
The acceptance of digital imaging is motivating many photography enthusiasts to transfer their
photographic archive to digital form. Scans of negatives and positives are preferred to be scanned at high resolution
which makes small cracks and scratches very apparent. These unsightly defects have become an important issue
for consumers. Filtering techniques are used for the restoration process which is fully automatic whereas the existing
systems were semi-automatic or completely manual. The method used for the detection of tear is dilation process and
top-hat transform. Top-hat transform might misinterpret dark brush strokes as cracks. In order to avoid these
unwanted alterations to the original image, brush strokes are separated from the actual cracks using clustering
technique. Tear removal includes order statistics filtering which deals with the reconstruction of missing or
damaged image areas.
Overview Of Video Object Tracking SystemEditor IJMTER
The goal of video object tracking system is segmenting a region of interest from a video
scene and keeping track of its motion, positioning and occlusion. There are the three steps of video
object tracking system those are object detection, object classification and object tracking. Object
detection is performed to check existence of objects in video. Then the detected object can be
classified in various categories on the basis on their shape, motion, color and texture. Object tracking
is performed using monitoring object changes. This paper we are going to take overview of different
object detection, object classification and object tracking techniques and also the comparison of
different techniques used for various stages of tracking.
Lecture 4 in the 2022 COMP 4010 lecture series on AR/VR. This lecture is about AR Interaction techniques. This was taught by Mark Billinghurst at the University of South Australia in 2022.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software, but also in advanced interface between people and computers, advanced control methods and many other areas.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
Summary: Graphs are structures commonly used in computer science that model the interactions among entities. I will start from introducing the basic formulations of graph based machine learning, which has been a popular topic of research in the past decade and led to a powerful set of techniques. Particularly, I will show examples on how it acts as a generic data mining and predictive analytic tool. In the second part, I am going to discuss applications of such learning techniques in media analytics: (1) image analysis, where visually coherent objects are isolated from images; (2) social analysis of videos, where actors' social properties are predicted from videos. Materials in this part are based on our recent publications in highly selective venues (papers on https://sites.google.com/site/leiding2010/ ).
Bio: Lei Ding is a researcher making sense of large amounts of data in all media types. He currently works in Intent Media as a scientist, focusing on data analytics and applied machine learning in online advertising. Previously, he has worked in several research institutions including Columbia University, UIUC and IBM Research on digital / social media analysis and understanding. He received a Ph.D. degree in Computer Science and Engineering from The Ohio State University, where he was a Distinguished University Fellow.
Mika Kaukoranta presents what computer vision is and how it can be utilized in software testing by gaining high-level understanding from digital images or videos.
Lecture 4 from the COMP 4010 course on AR/VR. This lecture reviews optical tracking for AR and starts discussion about interaction techniques. This was taught by Mark Billinghurst at the University of South Australia on August 17th 2021.
Seminar presentation about :
Automatic Image Annotation structure: shallow and deep,
cons and pros of different features and classification methods in AIA and
useful information about databases,toolboxes, authors
Overview of Computer Vision For Footwear IndustryTanvir Moin
Computer vision is an interdisciplinary field that focuses on enabling computers to interpret and analyze visual data from the world around us. It involves the development of algorithms and techniques that allow machines to understand images and videos, just as humans do.
The main goal of computer vision is to create machines that can "see" and understand the world around them, and then use that information to make decisions or take actions. This can involve tasks such as object recognition, scene reconstruction, facial recognition, and image segmentation.
Computer vision has a wide range of applications in various fields, such as healthcare, entertainment, transportation, robotics, and security. Some examples include medical image analysis, autonomous vehicles, augmented reality, and surveillance systems.
In recent years, the development of deep learning techniques, particularly convolutional neural networks (CNNs), has greatly advanced the field of computer vision, allowing machines to achieve state-of-the-art performance on various visual recognition tasks.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
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.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
4. Course Description
Computing with images is no longer just for the realm of the sciences,
but also for the arts and social sciences. Machine Vision extracts
descriptions of the world from picture or sequence of pictures and relies
on a solid understanding of cameras and of the physical process of
image formation. This course begins with introducing the concept of
vision system, image formation, and image filtering. It then addresses
some aspects of machine vision such as feature detection, feature
extraction, pattern recognition, and image matching. Through this
course, students will also explore some applications of machine vision.
5. Learning Outcome
• LO1: Understand various aspects of machine vision algorithms
• LO2: Comprehend the importance of machine vision applications
• LO3: Apply the concept of machine vision to solve real-world
engineering problems
• LO4: Analyze the enabling technologies of machine vision
applications
• LO5: Evaluate the advances and research challenges in machine
vision
• LO6: Design new approaches that can improve the machine vision
applications
6. Textbooks
Forsyth. (2011). Computer Vision a Modern Approach (2nd
Edition). Prentice Hall. New Jersey. ISBN-10: 013608592X.
ISBN-13: 978-0136085928
Szeliski. (2010). Computer Vision: Algorithms and
Applications. Springer. London. ISBN-13: 978-1848829343.
ISBN-10: 1848829345
Gonzales. (2011). Digital Image Processing (3rd Edition).
Prentice Hall. New Jersey. ISBN-10: 013168728X. ISBN-13:
978-0131687288
7. Syllabus
Session Topics Sub Topics
1 Introduction to Machine
Vision
Course introduction
Related fields
Optical Illusions
Application areas
Software
Resources
2 Human Visual System and
Digital Camera
Human Visual System
Digital Cameras
Image Formation
Camera and Image Plane Coordinates
Image Sensing
3 Light and Color The Physics of Color
Human Color Perception
Representing Color
A Model of Image Color
8. Syllabus
Session Topics Sub Topics
4 Image Filtering (1) Filtering in Spatial Domain
First derivative filters
Second derivative filters
5 Image Filtering (2) Filtering in Frequency Domain
Fourier Transform
Wavelet Transform
6 Feature Detection Edge Detection
Canny Edge Detector
Interest Point and Corner
Harris Corner Detector
7 Topics review and midterm
examination
9. Syllabus
Session Topics Sub Topics
8 Shape Features Thresholding
Identifying Boundary
Chain Code
Fourier Descriptor
Identifying Region
Moments
9 Texture Features Texture Analysis
Structural Approaches
Statistical Approaches
LBP, GLCM, Laws Texture Energy, Fourier
Power Spectrum, Wavelet Texture
Features
Texture Segmentation
10 Recognition (1) Pattern Recognition
Supervised Learning
Classification
10. Syllabus
Session Topics Sub Topics
11 Recognition (2) Unsupervised Learning
Clustering
K-Means Clustering
Agglomerative Hierarchical Clustering
12 Image Matching Definition
Invariant Features
Scale Invariant Feature Transform
Feature Matching
13 Sample Applications Content Based Image Retrieval
Face Recognition
14 Topics review and final
examination
12. Class Policies
• Student must active in classroom discussion forum, responding to lecturer’s
questions and discussing with classmates
• Student must active in team room, especially discussing team assignment
• Student must read learning material and other references before class,
reading/case will be distributed before class, team/group and group
discussion/presentation will be notified before class
• Student must complete and submit all personal assignment and team
assignment
• Do not rely on handout distributed by lecturer, student can use other
references
• Achieve a satisfactory average grade on assignments and examinations.
• Penalty for cheating and plagiarism will be extremely severe. If you are not
sure about certain activities, consult the instructor. Standard academic honesty
procedure will be followed and active cheating and plagiarism automatically
results FAIL in the final grade.
13. What is Machine Vision?
• A process that produces from
images of the external world a
description that is useful to the
viewer and not cluttered with
irrelevant information (Marr)
• Construction of explicit,
meaningful descriptions of
physical objects from images
(Ballard and Brown)
• To make useful decisions about
real physical objects and scenes
based on sensed images
(Shapiro and Stockman) Some industrial applications of computer vision
15. Related Fields: Image Processing
• Image Processing
– Imagein, imageout
– Usually low level techniques (e.g.,
image enhancement, compression,
edge detection)
– Quantitative measurements
• Computer Vision
– Extracting symbolic descriptions
– Higher level techniques (e.g., object
recognition)
– Semantic (quantitative or
qualitative) output
• Image processing techniques
are often used in computer
vision
16. Related Fields: Computer Graphics
• Computer vision is the inverse of computer graphics Computer Vision
• The forward process is unique, the inverse process is not!
3D Models of objects, locations
Lighting information Camera
parameters
Images
Computer graphics
Computer vision
17. Related fields
• Pattern recognition
– Recognition of patterns (classification)
– Inputs often represented as feature vectors
– Techniques useful for 2D and constrained 3D image
recognition problems, but usually too limited for
general 3D problems
• Photogrammetry
– Concerned with accurately measuring properties from
images
– An older field - historically focused on remote sensing
(e.g., images from airplanes or satellites)
– Computer vision concerned with more than just
measuring
– However, many techniques are the same or similar
18. Vision is not easy for computer
• Objects can be highly variable in shape
– E.g., trees, cars, animals, ...
• Loss of information in sensing process
– 3D objects projected onto 2D images
• Missing data
– Occlusions and hidden surfaces
– Shadows and noise obscure signal
• Confounding effects
– Observed color may be due to
– object albedo or scene lighting
19. Approach to Solution
• Apply assumptions and a priori
knowledge to recover the most
likely description
• Use knowledge of object shape
and the lighting that is present (if
available)
• Use information from multiple
images (stereo, motion
sequences)
• Guess based on cues
– Shading, texture, geometry
– Knowledge about typical real world
objects
21. Optical Illusions
• Human visual system is
good at picking out
structure from noisy,
incomplete, and missing
data
• We make and use
assumptions about the real
world to do this
• Optical illusions occur when
these assumptions are
incorrect
26. Application Areas (example)
• Industrial inspection
– Find known objects in the
scene
– Measure dimensions, verify
features
• Optical character
recognition
– Processing scanned text
pages
– Detect and identify
characters
27. Application Areas (example)
• Scene modeling
– Find and identify objects (ships, buildings, roads)
– Create models of scenes from multiple images
28. Application Areas (example)
• Target recognition
– Find enemy vehicles (which are
trying not to be found!)
• Human Interfaces
– Detect faces and identify people
– Recognize gestures, activities
29. Application Areas (example)
• Robotics
– Recognize objects
– Estimate motion and position
• Medical modeling
– Surgical planning and
navigation
30. Software
• Matlab
– Commercial package (but only $99 for students)
– Interpreted language – easy to prototype
– Image processing toolbox; computer vision toolbox; user
contributed toolboxes
• OpenCV – Free
– www.opencv.org
• Several applications have simple image processing and
computer vision functions
– Image
– CVIP tools
31. Resources
• Some key journals
– IEEE Trans. Pattern Analysis & Machine Intelligence
– International Journal of Computer Vision
• Some key conferences
– Computer Vision and Pattern Recognition (CVPR)
– International Conference on Computer Vision (ICCV)
• Some good websites
– Extensive collection of material -
http://www.cs.cmu.edu/~cil/vision.html
– Indexed bibliography -
http://iris.usc.edu/Vision-Notes/bibliography/contents.html
32. Acknowledgment
Some of slides in this PowerPoint presentation are adaptation from
various slides, many thanks to:
1. Professor William Hoff, Department of Electrical Engineering &
Computer Science (http://inside.mines.edu/~whoff/)
2. Dr. Brian Mac Namee, School of Computing at the Dublin Institute of
Technology (http://www.comp.dit.ie/bmacnamee/gaip.htm)