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.
We create a group presentation for Simulation & Modeling. This presentation has so many related fields as like artificial intelligence ,Information engineering,Neurology, Signal processing etc.
Blue Eyes technology aims at creating computational Machines with perceptual and sensory abilities like those of human beigns. Blue Eyes system is thus a versatile system which can be modified to cater to the working environment. The Blue Eyes system has hardware with software loaded on it Blue Eyes systemcan be applied in every working environment requiring permanent operator''s attention for it. The hardware comprises of data acquisition unit and central system unit. The heart of Data acquisition unit is ATMEL 89C52 microcontroller Bluetooth technology is used for communication and coordination between the two units.Blue eye system can be applied in every working environment which requires pemanent operator''s attention. Blue eyes sytem provides technical means for monitoring and recording human operator''s physiological condition. A blue eyes is a project aiming to be a means of stress reliever driven by the advanced, technology of syudying the facial expressions for judgment of intensity of stress handled. In totality blue eyes aims at adding perceptual abilities which would end up in a healthy stress free environment and can be applied in every working environment requiring permanent operator''s attention.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
We create a group presentation for Simulation & Modeling. This presentation has so many related fields as like artificial intelligence ,Information engineering,Neurology, Signal processing etc.
Blue Eyes technology aims at creating computational Machines with perceptual and sensory abilities like those of human beigns. Blue Eyes system is thus a versatile system which can be modified to cater to the working environment. The Blue Eyes system has hardware with software loaded on it Blue Eyes systemcan be applied in every working environment requiring permanent operator''s attention for it. The hardware comprises of data acquisition unit and central system unit. The heart of Data acquisition unit is ATMEL 89C52 microcontroller Bluetooth technology is used for communication and coordination between the two units.Blue eye system can be applied in every working environment which requires pemanent operator''s attention. Blue eyes sytem provides technical means for monitoring and recording human operator''s physiological condition. A blue eyes is a project aiming to be a means of stress reliever driven by the advanced, technology of syudying the facial expressions for judgment of intensity of stress handled. In totality blue eyes aims at adding perceptual abilities which would end up in a healthy stress free environment and can be applied in every working environment requiring permanent operator''s attention.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
Presentation on the New Technology based on the recognition of letters that would be available on Soft and Hard copy both and allow all the format in Soft Copy. Optical character Recognition based on the recognition of letters with all the existing languages.
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.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
20 Latest Computer Science Seminar Topics on Emerging TechnologiesSeminar Links
A list of Top 20 technical seminar topics for computer science engineering (CSE) you should choose for seminars and presentations in 2019. The list also contains related seminar topics on the emerging technologies in computer science, IT, Networking, software branch. To download PDF, PPT Seminar Reports check the links.
Presentation on the New Technology based on the recognition of letters that would be available on Soft and Hard copy both and allow all the format in Soft Copy. Optical character Recognition based on the recognition of letters with all the existing languages.
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.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
20 Latest Computer Science Seminar Topics on Emerging TechnologiesSeminar Links
A list of Top 20 technical seminar topics for computer science engineering (CSE) you should choose for seminars and presentations in 2019. The list also contains related seminar topics on the emerging technologies in computer science, IT, Networking, software branch. To download PDF, PPT Seminar Reports check the links.
Computer vision techniques can be seen in various aspects in our daily life with tremendous impacts. This slides aim at introducing basic concepts of computer vision and applications for the general public.
Download link: https://uofi.box.com/shared/static/24vy7aule67o4g6djr83hzurf5a9lfp6.pptx
Chen Sagiv, co founder and co CEO of SagivTech, gave an introduction talk to Computer Vision at She Codes branch in Google Campus TLV.
In the talk an overview was given on what is computer vision, where it is used, some basic notions and algorithms and the AI revolution.
Data quality is important for Machine Learning applications. Sometimes the data is more important than the model, in this slides I presend some tips & tricks I learned during real world projects I ported to production.
Types of Machine Learning- Tanvir Siddike MoinTanvir Moin
Machine learning can be broadly categorized into four main types based on how they learn from data:
Supervised Learning: Imagine a teacher showing you labeled examples (like classifying pictures of cats and dogs). Supervised learning algorithms learn from labeled data, where each data point has a corresponding answer or label. The algorithm analyzes the data and learns to map the inputs to the desired outputs. This is commonly used for tasks like spam filtering, image recognition, and weather prediction.
Unsupervised Learning: Unlike supervised learning, unsupervised learning deals with unlabeled data. It's like being given a pile of toys and asked to organize them however you see fit. The algorithm finds hidden patterns or structures within the data. This is useful for tasks like customer segmentation, anomaly detection, and recommendation systems.
Reinforcement Learning: This is inspired by how humans learn through trial and error. The algorithm interacts with its environment and receives rewards for good decisions and penalties for bad ones. Over time, it learns to take actions that maximize the rewards. This is used in applications like training self-driving cars and playing games like chess.
Semi-Supervised Learning: This combines aspects of supervised and unsupervised learning. It leverages a small amount of labeled data along with a larger amount of unlabeled data to improve the learning process. This is beneficial when labeled data is scarce or expensive to obtain.
Fundamentals of Wastewater Treatment PlantTanvir Moin
Wastewater treatment is the process of removing contaminants from wastewater and household sewage. It includes physical, chemical, and biological processes to convert wastewater into an environmentally safe outflow that can be reused or discharged into the environment.
Basic Principle of Electrochemical SensorTanvir Moin
Electrochemical sensors are the most versatile and highly developed chemical sensors. Electrochemical sensors are a type of chemical sensor that uses an electrode to detect the concentration of an analyte based on a chemical reaction. They are characterized by their low cost, ease of manufacture, rapid analysis, small size, and ability to detect multiple elements simultaneously. They are also powerful analytical tools because of their: Superior sensitivity and selectivity, Quick response period, Simplicity in operation, and Miniaturization.
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Aerated lagoons are a type of wastewater treatment system that uses artificial aeration to promote the biological oxidation of wastewaters. They are relatively simple and inexpensive to construct and operate, and they can be effective in removing a wide range of pollutants from wastewater, including organic matter, nutrients, and pathogens.
Assessing and predicting land use/land cover and land surface temperature usi...Tanvir Moin
To assess and predict land use/land cover (LULC) and land surface temperature (LST) using Landsat imagery for the Padma Bridge construction area, the following steps can be taken:
Preprocess the Landsat imagery. This includes correcting for geometric distortions, atmospheric effects, and radiometric calibration.
Classify the LULC. This can be done using a variety of supervised and unsupervised classification methods.
Calculate the LST. This can be done using a variety of methods, such as the Mono-Window Algorithm and the Normalized Difference Vegetation Index (NDVI).
Analyze the LULC and LST data. This can be done using a variety of statistical and geospatial methods to identify trends and patterns.
Predict the future LULC and LST. This can be done using a variety of machine learning and time series forecasting methods.
SOLID WASTE MANAGEMENT IN THE PHARMACEUTICAL INDUSTRYTanvir Moin
Solid waste management (SWM) in the pharmaceutical industry in Bangladesh is a complex issue. The industry generates a wide range of solid waste, including:
Expired or unused pharmaceuticals: These wastes can contain hazardous active pharmaceutical ingredients (APIs) and other chemicals.
Packaging waste: This includes glass, plastic, and metal packaging.
Laboratory waste: This includes chemicals, glassware, and other materials used in research and development.
Manufacturing waste: This includes scrap materials, filter cakes, and other wastes generated from the manufacturing process.
Wastewater Characteristics in the Pharmaceutical IndustryTanvir Moin
Wastewater from the pharmaceutical industry is characterized by a wide range of pollutants, including:
Organic compounds: These include active pharmaceutical ingredients (APIs), solvents, and other organic chemicals.
Inorganic compounds: These include heavy metals, salts, and other inorganic chemicals.
Microorganisms: These include bacteria, viruses, and other microorganisms.
The concentration of these pollutants can vary greatly depending on the type of pharmaceutical products being produced. For example, wastewater from the production of antibiotics will contain high levels of antibiotics, while wastewater from the production of other types of pharmaceuticals may contain lower levels of antibiotics but higher levels of other pollutants.
The pharmaceutical industry in Bangladesh is one of the most developed sectors in the country and has emerged as a major exporter of medicines. It has been growing at a rapid pace over the past few decades, and now meets nearly 98% of the domestic demand for pharmaceutical products. The industry is also a significant contributor to the Bangladeshi economy, generating approximately $3 billion in revenue annually.
UNACCOUNTED FOR WATER IN URBAN WATER SUPPLY SYSTEM FOR DHAKA CITY Tanvir Moin
Unaccounted for water (UFW) is the difference between the amount of water supplied to a distribution system and the amount of water billed to the customers.
Fabric Manufacturing Technology for Shoe UpperTanvir Moin
Fabric is a plain sheet of cloth, which is made from natural or man-made fibres by weaving or knitting process. Most fabrics are knitted or woven, but some are produced by non-woven processes such as braiding, felting, twisting, etc. Fabric considers a major raw material in the footwear manufacturing process.
YARN MANUFACTURING TECHNOLOGY FOR SHOE UPPERTanvir Moin
Yarn is a long continuous length of interlocked fibres, suitable for use in the production of textiles, sewing, crocheting, knitting, weaving, embroidery, or ropemaking. It can be made of a number of natural or synthetic materials and comes in various colours and thicknesses (referred to as "weights").
A major environmental concern related to nuclear power is the creation of radioactive wastes such as uranium mill tailings, spent (used) reactor fuel, and other radioactive wastes. These materials can remain radioactive and dangerous to human health for thousands of years.
Machine learning is important because it gives enterprises a view of trends in customer behaviour and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations.
Artificial Neural Networks for footwear industryTanvir Moin
An Artificial Neural Network (ANN) is an information processing model inspired by how biological nervous systems, such as the brain, process information. ANN is configured through a learning process for a specific application, such as pattern recognition or data classification. ANN is one of the hopes available to the footwear industry to integrate the elements such as production, cost, quality, information, statistical process control, just-in-time (JIT) manufacturing computer integrated manufacturing etc.
Nanotechnology is used in the characteristics imported to leather and textiles in the footwear industry, which include self-cleaning fabrics, dye capability enhancement, flame retardation, UV and anti-static protection, anti-bacteria, wrinkle resistance, soil resistance, and water repellence
Why is spectrophotometer used in the leather & textile footwear industry?
In the leather & textile footwear industry, using a spectrophotometer to capture both color and appearance on a physical sample has greatly improved quality, consistency, and speed to market. To make color approvals on-screen, the digital color file must also be color-accurate when it is imported into the design software
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
3. What is Computer Vision?
• Computer vision is the science and technology of machines that see.
• Concerned with the theory for building artificial systems that obtain information from images.
• The image data can take many forms, such as a video sequence, depth images, views from multiple cameras, or multi-dimensional data from a medical scanner
4. Computer Vision
Make computers understand images and videos.
What kind of scene?
Where are the cars?
How far is the
building?
…
5. Components of a computer vision system
Lighting
Scene
Camera
Computer
Scene Interpretation
Srinivasa Narasimhan’s slide
7. Vision is really hard
• Vision is an amazing feat of natural intelligence
• Visual cortex occupies about 50% of Macaque brain
• More human brain devoted to vision than anything else
Is that a
queen or a
bishop?
10. A little story
about
Computer
Vision
• In 1966, Marvin Minsky
at MIT asked his
undergraduate student
Gerald Jay Sussman to
“spend the summer linking
a camera to a
• computer and getting
the computer to describe
what it saw”. We now
know that the problem is
slightly more difficult than
that. (Szeliski 2009,
Computer Vision)
11. A little story
about
Computer
Vision
• In 1966, Marvin Minsky
at MIT asked his
undergraduate student
Gerald Jay Sussman to
“spend the summer linking
a camera to a
• computer and getting
the computer to describe
what it saw”. We now
know that the problem is
slightly more difficult than
that.
Founder, MIT AI project
12. A little story about Computer Vision
In 1966, Marvin Minsky at MIT asked his undergraduate student
Gerald Jay Sussman to “spend the summer linking a camera to a
computer and getting the computer to describe what it saw”. We
now know that the problem is slightly more difficult than that.
Image Understanding
13. Ridiculously brief history of computer vision
• 1966: Minsky assigns computer vision as
an undergrad summer project
• 1960’s: interpretation of synthetic
worlds
• 1970’s: some progress on interpreting
selected images
• 1980’s: ANNs come and go; shift toward
geometry and increased mathematical
rigor
• 1990’s: face recognition; statistical
analysis in vogue
• 2000’s: broader recognition; large
annotated datasets available; video
processing starts; vision & graphis; vision
for HCI; internet vision, etc.
Guzman ‘68
Ohta Kanade ‘78
Turk and Pentland ‘91
15. Optical
character
recognition
(OCR)
Digit recognition, AT&T labs
http://www.research.att.com/~yann/
Technology to convert scanned docs to text
• If you have a scanner, it probably came with OCR software
License plate readers
http://en.wikipedia.org/wiki/Automatic_number_plate_recognition
18. Object recognition (in
supermarkets)
• LaneHawk by EvolutionRobotics
• “A smart camera is flush-mounted in the
checkout lane, continuously watching for items.
When an item is detected and recognized, the
cashier verifies the quantity of items that were
found under the basket, and continues to close
the transaction. The item can remain under the
basket, and with LaneHawk,you are assured to
get paid for it… “
20. Login without a password…
Fingerprint scanners on
many new laptops,
other devices
Face recognition systems now
beginning to appear more widely
http://www.sensiblevision.com/
24. Sports
• Sportvision first down
line
• Nice explanation on
www.howstuffworks.com
• http://www.sportvision.c
om/video.html
25. Smart cars
• Mobileye [wiki article]
• Vision systems currently in high-end BMW, GM, Volvo
models
• By 2010: 70% of car manufacturers.
Slide content courtesy of Amnon Shashua
28. Vision in space
Vision systems (JPL) used for several tasks
• Panorama stitching
• 3D terrain modeling
• Obstacle detection, position tracking
• For more, read “Computer Vision on Mars” by Matthies et al.
NASA'S Mars Exploration Rover Spirit captured this westward view from atop
a low plateau where Spirit spent the closing months of 2007.
32. Prerequisites
A good working knowledge of C/C++, Java or Matlab
A good understand of math (linear algebra, basic
calculus, basic probability)
Willing to learn new stuffs (optimization,
statistical learning etc.)
37. Focus
Basic understand of OpenCV face recognition software and algorithms
Methods and Theory behind the EigenFace method for facial recognition
Implementation using Python in a Linux-based environment
Runs on a Raspberry Pi
38. Goal
• General facial recognition methods
• EigenFaces
• OpenCV’s facial recognition
One half research
• Create a system capable of facial recognition
• Real-time
• Able to run on a Raspberry Pi
One half implementation
40. Different Facial Recognition Methods
Geometric
Eigenfaces
Fisherfaces
Local Binary Patterns
Active Appearance
3D Shape Models
41. Geometric
• First method of facial recognition
• Done by hand at first
• Automation came later
• Find the locations of key parts of the face
• And the distances between them
• Good initial method, but had flaws
• Unable to handle multiple views
• Required good initial guess
42. Eigenfaces
• Information theory approach
• Codes and then decodes face images to gain
recognition
• Uses principal component analysis (PCA) to find the
most important bits
43. Fisherfaces
• Same approach as Eigenface
• Instead of PCA, uses linear discriminant analysis
(LDA)
• Better handles intrapersonal variability within
images such as lighting
44. Local Binary
Patterns
• Describes local features of an object
• Comparison of each pixel to its neighbors
• Histogram of image contains information about the
destruction of the local micro patterns
46. Basic Idea
• Let face image 𝐼(𝑥, 𝑦) be a two-dimensional 𝑁 by 𝑁 array
of (8-bit) intensity values
• Can consider image an 𝑁2
vector of dimensions
• Image of 256 by 256 becomes a 65,536 vector of dimension
• Or a point in 65,536-dimensional space
47. Basic Idea
• Images of faces will not differ too much
• This allows a much smaller dimensional subspace to be used to classify them
• PCA analysis finds the vectors that best define the distribution of images
• These vectors are then
• 𝑁2 long
• Describe an 𝑁 by 𝑁 image
• Linear combination of the original face images
48. Basic Idea
• These vectors are called
eigenfaces
• They are the eigenvectors
of the covariance matrix
• Resemble faces
49. Method
• Acquire initial training set of face images
• Calculate eigenfaces
• Keep only 𝑀 eigenfaces that correspond to the highest
eigenvalues
• These images now define the face space
• Calculate corresponding distribution in 𝑀-dimensional
weight space for each known individual
50. Method
• Calculate weights for new image by
projecting the input image onto each of
the eigenfaces
• Determine if face is known
• Within some tolerance, close to face
space
• If within face space, classify weights as
either known or unknown
• (Optional) Update eigenfaces and weights
• (Optional) If same face repeats, input into
known faces
51. Classifying
• Four possibilities for an
input image
• Near face space, near
face class
• Known face
• Near face space, not
near face class
• Unknown face
• Not near face space,
near face class
• Not a face, but may
look like one (false
positive)
• Not near face space, not
near face class
• Not a face
52. OpenCV and
Theory
• Beauty about OpenCV is a lot of this
process is completely automated
• Need:
• Training images
• Specify type of training
• Number of eigenfaces
• Threshold
• Input Image
56. Training
• Model was trained using positive and negative images
• Creates training file that holds the 𝑀-dimensional face space
• Now have a base to recognize from
model = cv2.createEigenFaceRecognizer()
model.train(np.asarray(faces),np.asarray(labels))
57. Recognition
• Steps to recognizing face
• Capture image
• Detect face
• Crop and resize around face
• Project across all eigenvectors
• Find face class that minimizes Euclidian distance
• Return label from face class, and Euclidian distance
• Euclidian distance also called Confidence level
model = cv2.createEigenFaceRecognizer()
model.load(config.TRAINING_FILE)
label, confidence = model.predict(image)
58. Test
• Created four different Test
• First data set uses 24 positive training images
• Almost no pose and lighting variation
• Second data set uses 12 positive training images
• Good pose variation, little lighting variation
• Third data set uses 25 positive training images
• Good pose and lighting variation
• Fourth data set uses second and third data set but with Fisherface method
59. Results • Results from Data sets 1-3, each one from 20 input
images
• Confidence represents distance from known face class
Data Set
Mean
Confidence Max Confidence Min Confidence
1 3462 3948 3040
2 2127 2568 1835
3 1709 2196 1217
60. Results • Results from eigenface vs. fisherface comparison
ALGORITHM DATA SET
# TRAINING
IMAGES
MEAN
CONFIDENCE
MAX
CONFIDENCE MIN CONFIDENCE
Eigen 2 12 2127 2568 1835
Fisher 2 12 2029 2538 1468
Eigen 3 25 1709 2196 1217
Fisher 3 25 2017 2748 1530
61. Conclusion
• Theory behind eigenfaces
• Face space
• Training
• Simple implementation of OpenCV’s eigenface recognizer
• Compared different training models
• Number of training images
• Pose and lighting variations
• Compared eigenfaces and fisherfaces
62. Conclusion
• Future work
• Further testing of different training models
• Implement updating facial recognition
64. Process optimization: You can help optimize the manufacturing process by using computer vision to monitor and control the
production line. For example, computer vision can be used to ensure that the correct materials are being used and that they are being
assembled correctly.
Defect detection: Computer vision can be used to detect defects in the footwear products, such as stitching errors or material
inconsistencies. By detecting defects early, the manufacturing process can be adjusted to correct the problem, reducing the need for
manual labor to fix defects later.
Computer vision can be used in the footwear manufacturing industry to automate certain processes and
reduce the need for manual labor. Here are a few ways you can help computer vision for footwear
manufacturing to reduce the need for labor:
65. Quality control: Computer vision can be used to inspect the finished footwear products and ensure that they
meet the required quality standards. This can reduce the need for manual inspection and increase the overall
efficiency of the manufacturing process.
Inventory management: Computer vision can be used to monitor the inventory of materials and finished
products. By automating the inventory management process, the need for manual labor can be reduced.
Automated assembly: Computer vision can be used to automate the assembly of footwear products, such as
attaching soles to uppers. By automating this process, the need for manual labor can be greatly reduced.
Overall, there are many ways to help computer vision for footwear manufacturing to reduce the need
for labor. By contributing to the development of computer vision models and applications, you can help
improve the efficiency and cost-effectiveness of the manufacturing process, while also reducing the
need for manual labor.