This is the first part of the presentation series on one of the powerful open sources libraries, the opencv. this presentation is about the introduction, installation, some basic functions on images and some basic image processing on the images
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
Introduction to Computer Vision using OpenCVDylan Seychell
This is an introductory deck to computer vision using OpenCV and Python, through examples. This presentation is a step by step codelab through the basic functions of OpenCV.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
This is the first part of the presentation series on one of the powerful open sources libraries, the opencv. this presentation is about the introduction, installation, some basic functions on images and some basic image processing on the images
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
Introduction to Computer Vision using OpenCVDylan Seychell
This is an introductory deck to computer vision using OpenCV and Python, through examples. This presentation is a step by step codelab through the basic functions of OpenCV.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
Ray casting is a rendering technique used in computer graphics and computational geometry.
It is capable of creating a three-dimensional perspective in a two-dimensional map.
Developed by scientists at the Mathematical Applications Group in the 1960.
it is considered one of the most basic graphics-rendering algorithms.
Ray casting makes use of the same geometric algorithm as ray tracing.
Advantage:
Ray casting is fast, as only a single computation is needed for every vertical line of the screen.
Compared to ray tracing, ray casting is faster, as it is limited by one or more geometric constraints.
his is one of the reasons why ray casting was the most popular rendering tool in early 3-D video games.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine.
Slides for the talk: https://www.pycon.it/conference/talks/deep-diving-into-gans-form-theory-to-production
Github repo: https://github.com/zurutech/gans-from-theory-to-production
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Simplilearn
This presentation on Convolutional neural network tutorial (CNN) will help you understand what is a convolutional neural network, hoe CNN recognizes images, what are layers in the convolutional neural network and at the end, you will see a use case implementation using CNN. CNN is a feed forward neural network that is generally used to analyze visual images by processing data with grid like topology. A CNN is also known as a "ConvNet". Convolutional networks can also perform optical character recognition to digitize text and make natural-language processing possible on analog and hand-written documents. CNNs can also be applied to sound when it is represented visually as a spectrogram. Now, lets deep dive into this presentation to understand what is CNN and how do they actually work.
Below topics are explained in this CNN presentation(Convolutional Neural Network presentation)
1. Introduction to CNN
2. What is a convolutional neural network?
3. How CNN recognizes images?
4. Layers in convolutional neural network
5. Use case implementation using CNN
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
Image segmentation is an important image processing step, and it is used everywhere if we want to analyze what is inside the image. Image segmentation, basically provide the meaningful objects of the image.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-opencv
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Gary Bradski, President and CEO of the OpenCV Foundation, presents the "The OpenCV Open Source Computer Vision Library: What’s New and What’s Coming?" tutorial at the May 2016 Embedded Vision Summit.
OpenCV is an enormously popular open source computer vision library, with over 14 million downloads expanding recently to 200K downloads per month. Originally used mainly for research and prototyping, in recent years OpenCV has increasingly been used in deployed products on a wide range of platforms from cloud to mobile. The latest version, OpenCV 3.1, was just released. The previous version, 3.0, was a major overhaul, bringing OpenCV up to modern C++ standards and incorporating expanded support for 3D vision and augmented reality. The new 3.1 release introduces support for deep neural networks, as well as new and improved algorithms for important functions such as calibration, optical flow, image filtering, segmentation and feature detection.
In this talk, Gary Bradski, head of the OpenCV Foundation, provides an insider’s perspective on the new version of OpenCV and how developers can utilize it to maximum advantage for vision research, prototyping, and product development. Gary also offers a sneak peek into where OpenCV is headed next.
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
Ray casting is a rendering technique used in computer graphics and computational geometry.
It is capable of creating a three-dimensional perspective in a two-dimensional map.
Developed by scientists at the Mathematical Applications Group in the 1960.
it is considered one of the most basic graphics-rendering algorithms.
Ray casting makes use of the same geometric algorithm as ray tracing.
Advantage:
Ray casting is fast, as only a single computation is needed for every vertical line of the screen.
Compared to ray tracing, ray casting is faster, as it is limited by one or more geometric constraints.
his is one of the reasons why ray casting was the most popular rendering tool in early 3-D video games.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine.
Slides for the talk: https://www.pycon.it/conference/talks/deep-diving-into-gans-form-theory-to-production
Github repo: https://github.com/zurutech/gans-from-theory-to-production
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Simplilearn
This presentation on Convolutional neural network tutorial (CNN) will help you understand what is a convolutional neural network, hoe CNN recognizes images, what are layers in the convolutional neural network and at the end, you will see a use case implementation using CNN. CNN is a feed forward neural network that is generally used to analyze visual images by processing data with grid like topology. A CNN is also known as a "ConvNet". Convolutional networks can also perform optical character recognition to digitize text and make natural-language processing possible on analog and hand-written documents. CNNs can also be applied to sound when it is represented visually as a spectrogram. Now, lets deep dive into this presentation to understand what is CNN and how do they actually work.
Below topics are explained in this CNN presentation(Convolutional Neural Network presentation)
1. Introduction to CNN
2. What is a convolutional neural network?
3. How CNN recognizes images?
4. Layers in convolutional neural network
5. Use case implementation using CNN
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
Image segmentation is an important image processing step, and it is used everywhere if we want to analyze what is inside the image. Image segmentation, basically provide the meaningful objects of the image.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-opencv
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Gary Bradski, President and CEO of the OpenCV Foundation, presents the "The OpenCV Open Source Computer Vision Library: What’s New and What’s Coming?" tutorial at the May 2016 Embedded Vision Summit.
OpenCV is an enormously popular open source computer vision library, with over 14 million downloads expanding recently to 200K downloads per month. Originally used mainly for research and prototyping, in recent years OpenCV has increasingly been used in deployed products on a wide range of platforms from cloud to mobile. The latest version, OpenCV 3.1, was just released. The previous version, 3.0, was a major overhaul, bringing OpenCV up to modern C++ standards and incorporating expanded support for 3D vision and augmented reality. The new 3.1 release introduces support for deep neural networks, as well as new and improved algorithms for important functions such as calibration, optical flow, image filtering, segmentation and feature detection.
In this talk, Gary Bradski, head of the OpenCV Foundation, provides an insider’s perspective on the new version of OpenCV and how developers can utilize it to maximum advantage for vision research, prototyping, and product development. Gary also offers a sneak peek into where OpenCV is headed next.
Looking into the past - feature extraction from historic maps using Python, O...James Crone
Tutorial presentation providing an overview of extracting geospatial features from scanned historic maps in an automated fashion using Python, OpenCV and PostGIS.
This manual is “How to Build” manual for OpenCV with OpenCL for Android.
If you want to “Use OpenCL on OpenCV” ONLY,
Please see
http://github.com/noritsuna/OpenCVwithOpenCL4AndroidNDKSample
Face Recognition with OpenCV and scikit-learnShiqiao Du
A lightweight implementation of Face Recognition system with Python. OpenCV and scikit-learn.
Python, OpenCv, scikit-learnによる簡易な顔認識システムの実装. Tokyo.Scipy5にて発表。
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-opencv
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Gary Bradski, President and CEO of the OpenCV Foundation, presents the "OpenCV Open Source Computer Vision Library: Latest Developments" tutorial at the May 2015 Embedded Vision Summit.
OpenCV is an enormously popular open source computer vision library, with over 9 million downloads. Originally used mainly for research and prototyping, in recent years OpenCV has increasingly been used in deployed products on a wide range of platforms from cloud to mobile.
The latest version, OpenCV 3.0 is currently in beta, and is a major overhaul, bringing OpenCV up to modern C++ standards and incorporating expanded support for 3D vision. The new release also introduces a modular “contrib” facility that enables independently developed modules to be quickly integrated with OpenCV as needed, providing a flexible mechanism to allow developers to experiment with new techniques before they are officially integrated into the library.
In this talk, Gary Bradski, head of the OpenCV Foundation, provides an insider’s perspective on the new version of OpenCV and how developers can utilize it to maximum advantage for vision research, prototyping, and product development.
Tiziran-com, Computer Vision,Deep Learning,Video analysis,Farshid PirahanSiah,OpenCV 3,Ubuntu,DIGITS,Caffe,Recurrent Neural Networks -RNNs-,Long Short-Term Memory -LSTM-,Gated Recurrent Units -GRU-,classify action of human in video,deep learning classifications -LeNet, AlexNet, GoogLeNet and VGGNet-,histograms of optical flow orientation and magnitude,Event Recognition in Surveillance Video Activity,Convolutional neural network -CNN-,video and image stabilization, depth map, depth of field, sharpness image, Motion Analysis and Object Tracking,image processing,machine vision,robotics,humanoid robot,www-tiziran-com,www-pirahansiah-com,thresholding,
Image Detection and Count Using Open Computer Vision (Opencv)IJERA Editor
The purpose of this paper is to introduce and quickly make a reader to provide basics of OpenCV (Open Source Computer Vision) without having to go through the lengthy reference manuals and books. OpenCV is actually an open source library for image and video analysis, originally introduced more than decade ago by Intel. The latest major change took place in 2009 (OpenCV2) which includes main changes to the C++ interface. Nowadays the library has >2500 optimized algorithms. It is extensively used around the world, having >2.5M downloads and >40K people in the user group. Regardless of whether one is a novice C++ programmer or a professional software developer, unaware of OpenCV, the content should be interesting mainly for the researchers and graduate students in image processing and computer vision areas.
Automatic License Plate Recognition using OpenCVEditor IJCATR
Automatic License Plate Recognition system is a real time embedded system which automatically recognizes the license plate of vehicles. There are many applications ranging from complex security systems to common areas and from parking admission to urban traffic control. Automatic license plate recognition (ALPR) has complex characteristics due to diverse effects such as of light and speed. Most of the ALPR systems are built using proprietary tools like Matlab. This paper presents an alternative method of implementing ALPR systems using Free Software including Python and the Open Computer Vision Library.
Automatic License Plate Recognition using OpenCV Editor IJCATR
Automatic License Plate Recognition system is a real time embedded system which automatically recognizes the license plate of vehicles. There are many applications ranging from complex security systems to common areas and from parking admission to urban traffic control. Automatic license plate recognition (ALPR) has complex characteristics due to diverse effects such as of light and speed. Most of the ALPR systems are built using proprietary tools like Matlab. This paper presents an alternative method of implementing ALPR systems using Free Software including Python and the Open Computer Vision Library.
Implementation of embedded arm9 platform using qt and open cv for human upper...Krunal Patel
: In this Paper, A novel architecture for automotive vision using an embedded device will be
implemented on ARM9 Board with highly computing capabilities and low processing power. Currently,
achieving real-time image processing routines such as convolution, thresholding, edge detection and some of the
complex media applications is a challenging task in embedded Device, because of limited memory. An open
software framework, Linux OS is used in embedded devices to provide a good starting point for developing the
multitasking kernel, integrated with communication protocols, data management and graphical user interface for
reducing the total development time. To resolve the problems faced by the image processing applications in
embedded Device a new application environment was developed. This environment provides the resources
available in the operating system which runs on the hardware with complex image processing libraries. This
paper presents the capture of an image from the USB camera, applied to image processing algorithms to Detect
Human Upper Body. The application (GUI) Graphical User Interface was designed using Qt and ARM Linux
gcc Integrated Development Environment (IDE) for implementing image processing algorithm using Open
Source Computer Vision Library (OpenCV). This developed software integrated in mobiles by the cross
compilation of Qt and the OpenCV software for Linux Operating system. The result utilized by Viola and Jones
Algorithm with Haar Features of the image using OpenCV.
On technology transfer: experience from the CARP project... and beyonddividiti
I am Anton Lokhmotov, Founder and CEO of dividiti (http://dividiti.com).
On 17 September 2015, I gave an invited industrial day talk at the Lorentz Center workshop on Verification of Concurrent and Distributed Software. Even though an outsider to the verification community, I could sense that this community craves insights into how to succeed with transferring technology from academia into industry (unlike academics who get frustrated when thinking about commercialisation).
As our story of long term collaboration with Alastair Donaldson of Imperial College London illustrates, technology transfer rules are actually simple. First, accept that rigorous evaluation and reproducibility are essential for research excellence ("corroborate"). Second, on top of your research excellence, create a push from academia and a pull into industry for your technology ("collaborate").
If you adhere to these rules in your research, you will achieve technology transfer as a natural by-product; so natural, in fact, that the push and pull actions will merge to become a flow. Stimulating such flows - by sharing experience and proving encouragement across the community - should lead to excellent research making more impact on our lives.
BEST IMAGE PROCESSING TOOLS TO EXPECT in 2023 – Tutors IndiaTutors India
As the name suggests, processing an image entails a number of steps before we reach our goal.
Check our Pdf for More Information
Visit our work (Source):
https://www.tutorsindia.com/blog/top-13-image-processing-tools-to-expect-2023/
Here is my slide on OpenCV. This slide includes major things about OpenCV such as what is OpenCV?, its appications, Functionalities, uses, pros and cons, Modules of OpenCV and Installation of OpenCV in all platforms.
First day of slides for @GAFFTA workshop http://www.gaffta.org/2012/07/24/hacking-the-kinect-with-openframeworks/
Part 1 of the live stream : http://www.youtube.com/watch?v=WXfy8Cuje-0&feature=plcp
Part 2 of the live stream :
http://www.youtube.com/watch?v=I80FsOlMPj8&feature=plcp
Finding Resource Manipulation Bugs in Linux CodeAndrzej Wasowski
Software projects suffer from conceptually simple resource manipulation bugs, such as accessing a de-allocated memory region, or acquiring a non-reentrant lock twice. The VBDB bug database contains entries for 100 such real bugs from several open source projects, including the Linux Kernel project. These historical bugs have been collected with the aim of giving concrete well understood and documented cases to program analysis researchers, in order to boost program verification research. I will discuss simplicity and complexity of real software manipulation bugs on examples selected from VBDB. One way to reduce the amount of such bugs is to use code scanners such as Smatch or Coccinelle. Unfortunately, while very efficient, code scanners are typically based on syntactic pattern matching, which is insufficient for identifying problems that span multiple functions and involve dynamically allocated memory. We have developed a shape-and-effect inference system for C that constructs a lightweight semantic abstraction, more analyzable than syntax. A model checker is then used to match semantic bug patterns over the control flow graph decorated with the shape-and-effect abstractions. Experiments run with our prototype analyzer (EBA) shows better precision and effectiveness than with syntactic bug scanners. We have been so far able to identify 10 previously unknown locking bugs in the Linux kernel. The bugs are confirmed as real by the Kernel developers, and five of them have been already fixed in response to our reports. I will conclude, sketching how we combine EBA with another tool, RECONFIGURATOR, to massively scan Linux kernel code for bugs in atypical source configurations.
Build an application upon Semantic Web models. Brief overview of Apache Jena and OWL-API.
Semantic Web course
e-Lite group (https://elite.polito.it)
Politecnico di Torino, 2017
Introduction to the Python programming language (version 2.x)
Ambient intelligence: technology and design
http://bit.ly/polito-ami
Politecnico di Torino, 2015
PowerOnt: an ontology-based approach for power consumption estimation in Smar...Luigi De Russis
Presentation given at the 1st Cognitive Internet of Things Technologies (COIOTE 2014)
October 27, 2014, Rome, Italy
The paper is available on the PORTO open access repositor of Politecnico di Torino: http://porto.polito.it/2570936/
Short seminar about the Semantic Web for the "Artificial Intelligence" course at Politecnico di Torino (academic year 2012/2013)
An updated version is available at http://www.slideshare.net/luigidr/semantic-web-an-introduction
An overview on social network technologies: are they "typical" website? Or do they work in a different way? How many and what technologies do Facebook and Instagram use?
Presentation made for the Multimedia Languages and Environments course at Politecnico di Torino (academic year 2013/2014).
A brief overview about writing clean code. Presentation made for the Multimedia Languages and Environments course at Politecnico di Torino (academic year 2012/2013).
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Introduction to OpenCV
1. Computer Vision
OpenCV
Luigi De Russis
Politecnico di Torino
Dipartimento di Automatica e Informatica (DAUIN)
Torino - Italy
luigi.derussis@polito.it
This work is licensed under the Creative Commons (CC BY-SA)
License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-sa/3.0/
2. What is OpenCV?
Open source Computer Vision library
http://opencv.org/
Originally developed by Intel
Has more than 2500 optimized algorithms
C/C++/Python API
it is written natively in C++
Cross-platform
includes a
also available for Android and iOS
Java API
Released under a BSD license (it’s free)
Current release version: 2.4.3RC (October 2012)
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 2
3. Various applications…
Human-Computer Interaction (HCI)
Object Identification
Object Recognition
Face Recognition
Gesture Recognition
Motion Tracking
Image Processing
Mobile Robotics
… and so on.
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 3
4. Why OpenCV? (I)
Best competitor here: Matlab
Pros
Specific
OpenCV was made for image processing
Matlab is quite generic
Speed
around 30 frames processed per seconds in real time image
processing (OpenCV)
around 4-5 frames processed per seconds in real time image
processing (Matlab)
Efficient
Matlab needs more system resources than OpenCV
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 4
5. Why OpenCV? (II)
Cons
Easy of use
Integrated Development Environment
you can use Eclipse, Netbeans, Visual Studio, Qt, XCode, … a
simple text editor for OpenCV
Memory management
Two more “pros”…
Price (!)
OpenCV Wrappers
SimpleCV, JavaCV, Emgu CV, JavacvPro, …
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 5
6. Modules (I)
OpenCV has a modular structure, i.e., the package
includes several shared or static libraries:
core
basic structures and algorithms
imgproc
Image Processing algorithms (such as image filtering,
geometrical image transformations, histograms, etc.)
video
video analysis (such as motion estimation and object tracking)
highgui
built-in simple UI
in addition, we will use Qt
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 6
7. Modules (II)
calib3d
camera calibration and 3D reconstruction
features2d
2D features framework (feature detectors, descriptors, and
descriptor matchers)
objdetect
detection of objects and other items (e.g., faces, eyes, mugs,
people, …)
ml
machine learning classes used for statistical classification,
regression and clustering of data
gpu
GPU-accelerated algorithms
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 7
8. Data structures (I)
We speak about C++ API
All the OpenCV classes and functions are placed
into the cv namespace
Mat
the primary image structure in OpenCV 2.x
overcomes the “old” IplImage/CvMat problems
(OpenCV 1.x/C API)
automatic memory management (more or less)
two data parts:
matrix header (contains information about the matrix)
a pointer to the matrix containing the pixel values
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 8
9. Data structures (II)
Point_ (Point2f, Point, Point2d)
2D point
defined by x, y coordinates
Point first(2, 3);
Size_ (Size, Size2f)
2D size structure
specify the size (width and height) of an image or
rectangle
Rect
2D rectangle object
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 9
10. Image I/O
Image I/O
imread
legge un’immagine da file e lo salva in un oggetto di tipo Mat
Mat imread(const string& filename, int flags=1)
imwrite
salva un’immagine su file
bool imwrite(const string& filename, InputArray img,
const vector<int>& params=vector<int>())
imshow
mostra un’immagine a schermo (in una finestra creata
precedentemente)
void imshow(const string& winname, InputArray mat)
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 10
11. Drawing operations
Base drawing operations
circle
draws a simple or filled circle with a given center and radius on a
given image
line
draws a line between two point in the given image
ellipse
draws an ellipse outline, a filled ellipse, an elliptic arc, a filled
ellipse sector, …
rectangle
draws a rectangle outline or a filled rectangle
note that negative thickness will fill the rectangle
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 11
12. Color spaces
Converting color spaces
cvtColor
converts an input image from one color space to another
examples:
cvtColor(src, dest, CV_RGB2GRAY);
cvtColor(src, dest, CV_HSV2BGR);
cvtColor(src, dest, CV_RGB2BGR);
important, since images in OpenCV uses BGR instead of
RGB
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13. How we can use OpenCV?
LABINF:
already installed under Windows
version 2.3.1
Qt Creator (4.7.4) is the IDE to be used
At home:
feel free to install OpenCV version 2.4.3
it should be more “stable”
you can use whatever IDE you like
but we give full support only for Qt Creator
Installation:
see the next set of slides for a step-by-step guide
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 13
14. What if I got problems?
Small problems
drop me a line Problems with Qt and a gray scale image
luigi.derussis@polito.it
Awesome student to me
Normal problems Hi,
[…] I’m using “cvtColor(image, gray, CV_BGR2GRAY);” and Indexed8 to convert an image in
gray scale but the image is not shown in Qt…
come to office hours Can you help me?
Regards,
…
every Tuesday, 9:00 - 11:00
LAB6, second floor of DAUIN
please send an e-mail beforehand
Enormous problems
come to the dedicated lessons
Tuesday 30th October, 9:30-12:30, DAUIN
Thursday 8th November, 15:00-18:00, DAUIN
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 14
15. What if I got problems?
OpenCV installation
Small problems Not-So-Awesome student to me
drop me a line Hi,
[…] I followed the guide for installing OpenCV on my Mac but I have an error after step 3. Can
we meet on next Tuesday to solve the problem?
luigi.derussis@polito.it Thanks!
Regards,
Normal problems …
come to office hours
every Tuesday, 9:00 - 11:00
LAB6, second floor of DAUIN
please send an e-mail beforehand
Enormous problems
come to the dedicated lessons
Tuesday 30th October, 9:30-12:30, DAUIN
Thursday 8th November, 15:00-18:00, DAUIN
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 15
16. What if I got problems?
Small problems
drop me a line
luigi.derussis@polito.it
Normal problems Help with OpenCV
come to office hours Good student to me
Hi,
every Tuesday, 9:00 - 11:00 […] I see the solution of Exercise 2.1 but I don’t understand the following expressions:
- main();
- int* number;
LAB6, second floor of DAUIN - &timer.
Can you explain to me what they are?
please send an e-mail beforehand Regards,
…
Enormous problems
come to the dedicated lessons
Tuesday 30th October, 9:30-12:30, DAUIN
Thursday 8th November, 15:00-18:00, DAUIN
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 16
17. An mail not to be sent!!!
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 17
18. Resources
OpenCV Wiki
http://code.opencv.org/projects/opencv/wiki
OpenCV 2.x Official Documentation
http://docs.opencv.org/
User Q&A forum
http://answers.opencv.org/questions/
OpenCV 2.x Tutorials
http://docs.opencv.org/opencv_tutorials.pdf
Books:
Robert Laganière, OpenCV 2 Computer Vision Application Programming
Cookbook, Packt Publishing, ISBN 978-1849513241
Gary Bradsky, Adrian Kaehler, Learning OpenCV: Computer Vision in C++ with
the OpenCV Library, O'Reilly Media, ISBN 978-1449314651 (to be published)
10/26/2012 Luigi De Russis - Computer Vision - OpenCV 18
19. License
This work is licensed under the Creative Commons
“Attribution-NonCommercial-ShareAlike Unported (CC BY-
NC-SA 3,0)” License.
You are free:
to Share - to copy, distribute and transmit the work
to Remix - to adapt the work
Under the following conditions:
Attribution - You must attribute the work in the manner
specified by the author or licensor (but not in any way that
suggests that they endorse you or your use of the work).
Noncommercial - You may not use this work for commercial
purposes.
Share Alike - If you alter, transform, or build upon this work,
you may distribute the resulting work only under the same or
similar license to this one.
26/10/2012 Luigi De Russis - Computer Vision - OpenCV 19