This document describes a visual based product identification system using a Raspberry Pi. The system uses computer vision techniques like color detection using OpenCV to identify objects on a conveyor belt based on their color and shape. Color detection is done by converting the image to HSV color space and thresholding for specific color ranges. Shape detection identifies shapes like triangles, squares and circles by analyzing contours. The overall system aims to automate production and quality control tasks.
Computer vision is the study of analyzing images and videos to understand and interpret visual content. It involves developing techniques to achieve tasks like object detection and recognition. Computer vision has many applications including optical character recognition, face detection, 3D modeling, robotics, medical imaging, and self-driving cars. OpenCV is a popular open source library for computer vision that contains over 2500 optimized algorithms and supports languages like C++, Python, and Java.
IRJET- Number Plate Recognition by using Open CV- PythonIRJET Journal
This document presents a license plate recognition system using OpenCV and Python. The system takes an image as input, pre-processes it by converting it to grayscale and applying thresholding. It then localizes the license plate using contour detection and extracts the plate. The characters on the plate are segmented and recognized using KNN algorithm. The system outputs the recognized characters. It discusses existing license plate recognition methods and proposes this system to address challenges with Indian license plates like variations in fonts, sizes, and colors. The system achieves accurate localization and recognition of license plates.
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.
Kelvin Sanchez is seeking a permanent career where he can utilize his strong work ethic. He has a high school diploma and trade certifications in graphic arts, press printing, and welding. Kelvin has experience in manufacturing as a DISA operator at Urick Foundry, preparing and delivering vehicles as a prep technician at Bianchi Honda, and providing customer service and installations at Autozone. He is bilingual, mechanically inclined, and has a history of training new employees. References are available upon request.
Computer vision is the study of analyzing images and videos to understand and interpret visual content. It involves developing techniques to achieve tasks like object detection and recognition. Computer vision has many applications including optical character recognition, face detection, 3D modeling, robotics, medical imaging, and self-driving cars. OpenCV is a popular open source library for computer vision that contains over 2500 optimized algorithms and supports languages like C++, Python, and Java.
IRJET- Number Plate Recognition by using Open CV- PythonIRJET Journal
This document presents a license plate recognition system using OpenCV and Python. The system takes an image as input, pre-processes it by converting it to grayscale and applying thresholding. It then localizes the license plate using contour detection and extracts the plate. The characters on the plate are segmented and recognized using KNN algorithm. The system outputs the recognized characters. It discusses existing license plate recognition methods and proposes this system to address challenges with Indian license plates like variations in fonts, sizes, and colors. The system achieves accurate localization and recognition of license plates.
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.
Kelvin Sanchez is seeking a permanent career where he can utilize his strong work ethic. He has a high school diploma and trade certifications in graphic arts, press printing, and welding. Kelvin has experience in manufacturing as a DISA operator at Urick Foundry, preparing and delivering vehicles as a prep technician at Bianchi Honda, and providing customer service and installations at Autozone. He is bilingual, mechanically inclined, and has a history of training new employees. References are available upon request.
Un zorro llamado Jordi se enamoró de una zorra llamada Florecita. Aunque Florecita pensaba que Jordi era feo, cambió de opinión cuando él le regaló una caja de bombones. Finalmente se casaron y tuvieron 24,000 hijos, aunque 20 años después la madre estaba cansada de cuidarlos. 50 años después, todos murieron.
This one sentence document does not provide enough context or information to create an accurate 3 sentence summary. The document contains only one word - "Lorem" - which is not meaningful on its own.
this presentation is to assist managers of schools to familiarize themselves with leadership and management of schools. the presentation highlights the various responsibilities of staff and support staff. when done with this presentation you can get to be a super man in management and administration of schools.
The document contains contact information for an individual. It lists Mr Amr El-Sakran and provides his affiliation as Sadex Technical Consulting. No other details are included in the brief document.
El documento habla sobre un diplomado para maestros, la presentación de un trabajo en un blog y sobre el trabajo de un docente llamado Humberto Antonio Muriel.
Este documento proporciona orientaciones para una sesión especial sobre la escritura de crónicas con estudiantes. La sesión consta de tres actividades. La primera actividad implica escribir una crónica sobre un día cotidiano desde la perspectiva subjetiva. La segunda actividad implica escribir una crónica sobre una experiencia personal con la tecnología. La tercera actividad implica compartir las crónicas entre los estudiantes y seleccionar algunas para ser presentadas. El maestro también escribirá una crónica sobre su experiencia
Za sve nastavnike 4.razreda devetogodišnjeg odgoja i obrazovanja tematski isplanirano i usklađene teme prema NPP-u u 4.razredu sa ciljevima i zadacima nastave
This document discusses machine vision systems and their components. A basic machine vision system includes a camera, light source, frame grabber, circuitry and programming, and a computer. Key components of machine vision systems are the image, camera, framegrabber, preprocessor, memory, processor, and output interface. The document also describes CCD and vidicon cameras, their advantages and disadvantages, and the functions of framegrabbers in sampling and quantizing images. Object properties that can be analyzed from pixel grey values include color, specular properties, non-uniformities, lighting. Applications of machine vision systems are also mentioned.
Machine vision uses computer vision techniques to automate inspection and measurement tasks in manufacturing processes. It incorporates computer science, optics, and mechanical engineering. Machine vision systems typically use digital cameras and specialized lenses to capture images that are then processed to check for attributes like dimensions, serial numbers, and defects. Common applications include inspecting semiconductor chips, automobiles, food, and pharmaceuticals. Key components of machine vision systems include cameras, lighting, lenses, and image processing software to analyze the captured images.
This interim report describes a vision-based product identification system being developed by W.F.R. Madushanka and M.S.P. Muthukumaranage. The system uses a Raspberry Pi minicomputer with OpenCV and Python to detect objects on a conveyor based on color and shape in real-time. Initial results show the system can successfully identify red, blue, square and triangular objects. The report outlines the hardware, software, detection methods, and provides results while acknowledging limitations with processing speed and software compatibility.
This document provides an overview of computer vision and OpenCV. It defines computer vision as using algorithms to identify patterns in image data. It describes how images are represented digitally as arrays of pixels and how features like edges and corners are important concepts. It introduces OpenCV as an open source library for computer vision with over 2500 algorithms. It supports languages like C++ and Python. OpenCV has modules for tasks like image processing, video analysis, and object detection. The document provides details on OpenCV data structures like Mat and how to get started with OpenCV in Android Studio by importing the module and adding the native libraries.
1. The document discusses the design and development of a machine vision application for object detection to be implemented on an embedded platform for automatic inspection and analysis.
2. It discusses using techniques like color segmentation for object classification in applications such as detecting red, green, and blue bottles to classify objects in real-time using a Raspberry Pi.
3. Future work includes developing real-time object detection that can distinguish between different objects and count the number of objects in the frame for applications in industrial automation and quality control.
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.
Un zorro llamado Jordi se enamoró de una zorra llamada Florecita. Aunque Florecita pensaba que Jordi era feo, cambió de opinión cuando él le regaló una caja de bombones. Finalmente se casaron y tuvieron 24,000 hijos, aunque 20 años después la madre estaba cansada de cuidarlos. 50 años después, todos murieron.
This one sentence document does not provide enough context or information to create an accurate 3 sentence summary. The document contains only one word - "Lorem" - which is not meaningful on its own.
this presentation is to assist managers of schools to familiarize themselves with leadership and management of schools. the presentation highlights the various responsibilities of staff and support staff. when done with this presentation you can get to be a super man in management and administration of schools.
The document contains contact information for an individual. It lists Mr Amr El-Sakran and provides his affiliation as Sadex Technical Consulting. No other details are included in the brief document.
El documento habla sobre un diplomado para maestros, la presentación de un trabajo en un blog y sobre el trabajo de un docente llamado Humberto Antonio Muriel.
Este documento proporciona orientaciones para una sesión especial sobre la escritura de crónicas con estudiantes. La sesión consta de tres actividades. La primera actividad implica escribir una crónica sobre un día cotidiano desde la perspectiva subjetiva. La segunda actividad implica escribir una crónica sobre una experiencia personal con la tecnología. La tercera actividad implica compartir las crónicas entre los estudiantes y seleccionar algunas para ser presentadas. El maestro también escribirá una crónica sobre su experiencia
Za sve nastavnike 4.razreda devetogodišnjeg odgoja i obrazovanja tematski isplanirano i usklađene teme prema NPP-u u 4.razredu sa ciljevima i zadacima nastave
This document discusses machine vision systems and their components. A basic machine vision system includes a camera, light source, frame grabber, circuitry and programming, and a computer. Key components of machine vision systems are the image, camera, framegrabber, preprocessor, memory, processor, and output interface. The document also describes CCD and vidicon cameras, their advantages and disadvantages, and the functions of framegrabbers in sampling and quantizing images. Object properties that can be analyzed from pixel grey values include color, specular properties, non-uniformities, lighting. Applications of machine vision systems are also mentioned.
Machine vision uses computer vision techniques to automate inspection and measurement tasks in manufacturing processes. It incorporates computer science, optics, and mechanical engineering. Machine vision systems typically use digital cameras and specialized lenses to capture images that are then processed to check for attributes like dimensions, serial numbers, and defects. Common applications include inspecting semiconductor chips, automobiles, food, and pharmaceuticals. Key components of machine vision systems include cameras, lighting, lenses, and image processing software to analyze the captured images.
This interim report describes a vision-based product identification system being developed by W.F.R. Madushanka and M.S.P. Muthukumaranage. The system uses a Raspberry Pi minicomputer with OpenCV and Python to detect objects on a conveyor based on color and shape in real-time. Initial results show the system can successfully identify red, blue, square and triangular objects. The report outlines the hardware, software, detection methods, and provides results while acknowledging limitations with processing speed and software compatibility.
This document provides an overview of computer vision and OpenCV. It defines computer vision as using algorithms to identify patterns in image data. It describes how images are represented digitally as arrays of pixels and how features like edges and corners are important concepts. It introduces OpenCV as an open source library for computer vision with over 2500 algorithms. It supports languages like C++ and Python. OpenCV has modules for tasks like image processing, video analysis, and object detection. The document provides details on OpenCV data structures like Mat and how to get started with OpenCV in Android Studio by importing the module and adding the native libraries.
1. The document discusses the design and development of a machine vision application for object detection to be implemented on an embedded platform for automatic inspection and analysis.
2. It discusses using techniques like color segmentation for object classification in applications such as detecting red, green, and blue bottles to classify objects in real-time using a Raspberry Pi.
3. Future work includes developing real-time object detection that can distinguish between different objects and count the number of objects in the frame for applications in industrial automation and quality control.
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.
Which type of software metric focuses on the efficiency of the development process itself?
*
1 point
a) Process Metric
b) Product Metric
c) Project Metric
d) User Experience Metric
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts,Figma basics: Creating basic responsive elements like
buttons, input elements, etc. to understand frames,
groups, layout, constraints, texts, vector, color palette,
etc.
vector, color palette,
etc.
Figma basics: Creating basic responsive elementsF
The document discusses OpenCV, an open source computer vision and machine learning software library. It provides instructions for compiling OpenCV 3.2 on Windows 10 with Visual Studio 2015, an overview of OpenCV modules for tasks like image processing, video analysis, and machine learning, and examples of how to set up a basic OpenCV project in Visual Studio and write a simple program to read and display an image.
Rhonda Software is an expert in computer vision with experience in video analytics, people counting, audience measurement, object recognition, and face detection. They have developed computer vision products like myAudience-Count and myAudience-Measure and offer custom computer vision solutions. Rhonda also develops the Beholder computer vision framework and optimizes algorithms for speed on platforms like FPGA, DSP, and GPU to enable real-time computer vision processing.
This document discusses edge detection algorithms for images using a Raspberry Pi single-board computer. It describes configuring the Raspberry Pi operating system, installing development tools like Geany IDE and OpenCV library, and writing Python programs to test edge detection algorithms like Canny, Sobel, and Laplace. Results show that Canny edge detection produced the most accurate edges compared to other methods. The goal is to use edge detection for automated visual inspection in industry applications.
At the 2014 NI Week in Austin, Texas, DMC engineers from Chicago, Boston and Denver came together to share information about High Speed Vision Systems and the work we do here at DMC.
This document provides an introduction and overview of OpenCV (Open Source Computer Vision Library) in Python. It discusses the history and goals of OpenCV, how to install OpenCV on Windows, Linux and Mac systems, and how to perform basic tasks like reading/opening images and video files, accessing the webcam, and applying computer vision techniques like edge detection, image filtering, and Canny edge detection on images and video. The document also mentions some common applications of OpenCV like face recognition and self-driving cars that utilize computer vision.
The document discusses OpenCV and its suitability for image processing on Android devices, noting that OpenCV is an open source library for computer vision and image processing that allows treating images as matrices and provides functions for tasks like blurring, edge detection, and object recognition; it provides an overview of some key OpenCV classes for Android and approaches for building image processing applications using OpenCV on Android.
IRJET- Face Detection based on Image Processing using Raspberry Pi 4IRJET Journal
This document describes a face detection system using a Raspberry Pi 4 and OpenCV. The system uses a camera module to capture images of faces. The Haar cascade classifier is then used to detect and recognize faces by analyzing pixel patterns and comparing to a stored database. If a match is found, a servo motor connected to the Raspberry Pi will open a door for access. The system provides security by only granting access to faces that are recognized from the stored database. Code is written using Python to control the hardware components and perform the face detection and recognition algorithms using OpenCV. The overall goal is to create a real-time face recognition security system to protect homes from theft.
This document provides legal notices and disclaimers for an Intel presentation. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that performance can vary depending on system configuration and that sample source code is released under an Intel license agreement. Finally, it lists various trademarks.
This document summarizes Shirish Jadav's B-Tech project involving internships at two startups - Aspirations and Transpose. At Aspirations, Shirish tested APIs for cloud storage, online gaming rooms and basic camera motions in a bike racing game. At Transpose, Shirish built hardware with a Raspberry Pi to capture traffic video data using a camera, process it to count vehicles, and send the data to a server. The projects helped Shirish gain experience with game development, hardware prototyping, and communicating across disciplines.
This document describes an object color tracker prototype designed using a Raspberry Pi board. It discusses the hardware and software components used, including the Raspberry Pi, Arduino board, camera, motors, and software like Raspbian OS and OpenCV. It explains how color detection is implemented using different color spaces like RGB, YCrCb, and HSV. Threshold values are defined for colors in each color space to identify objects. The color detection results for different color spaces are compared to determine the most effective approach.
Image Detection and Count Using Open Computer Vision (Opencv)IJERA Editor
This document discusses OpenCV (Open Source Computer Vision Library) which is a library of programming functions for real-time computer vision. It introduces OpenCV and describes its features including being cross-platform, open source, and optimized for performance. It also summarizes some common computer vision and image processing algorithms available in OpenCV like smoothing, morphology, resizing, camera calibration, and optical flow calculations. Machine learning methods are also incorporated for tasks like face detection and tracking.
This document is a project report on a face recognition and tracking system. It includes an acknowledgements section thanking those who helped with the project. It also includes an abstract describing the project as building a system for face recognition and tracking using image processing and computer vision toolboxes in MATLAB. The document outlines the various chapters that will be included, such as introductions to image processing and the hardware and software used, including Arduino and MATLAB. It provides block diagrams of the overall system design and hardware.
Similar to Law cost portable machine vision system (20)
5. Page 5
Solution Methodology
• Conveyor design
• Install operating system openCV and other necessary
packages to raspberry pi minicomputer
• Colour detection method
• Shape detection method
• Data send method
6. Page 6
Machine Vision System
Computer
Hardware+Software
Cameras
lighting
Vision systems can be
thought of as
computers with eyes
that can identify,
inspect and
communicate critical
information.
Eliminate Defects
Improve Quality
Automate Production
Track & Identify Parts
Reduce Cost
Image Captured
Stored In Memory
Algorithmically
Compared
Visual Based
Product
Identification
System
7. Page 7
Project Objectives
Build a system that can detect, recognize objects according to the
Colour
Shape
The whole process should be done in real time
Should be used ease & efficient algorithms
8. Page 8
Software Part & Hardware Part
Hardware
Lighting system
Web Camera
Raspberry-pi Computer
Monitor
Conveyor
Software
OpenCV
Python
Numpy
9. Page 9
OpenCV
OpenCV
Speed
Resource-saving
Cost
Portability
Open source
computer vision
library
Core module.
Imgproc module
Highgui module
Feature 2D module
Calib 3D module
Library is written in
C & C++
Runs under
linux,windows
Provides interfaces
Python,Ruby,Matlab
,etc
10. Page 10
Programing Software Development
Colour
Detection
Use contours methodShape
Detection
Use RGB to HSV conversion
method
11. Page 11
Colour Detection
STEPS
1. Capture image
2. Convert from BGR to HSV color-space
3. Threshold the HSV image for a range of color
4. Show the mask image.
12. Page 12
RGB to HSV Conversion Method
RGB
In terms of Hue ,Saturation and ValueHSV
In terms of the amount of RED,GREEN,BLUE present
13. Page 13
RGB to HSV Conversion Method
Hue –Represents colour type
Range 0 to 255
Saturation –Represents the vibrancy of the colour
Range 0 to 255
Value –Represents brightness of the colour
0 – Completely DARK
255 – Fully BRIGHT
15. Page 15
Shape Detection
STEPS
1. Capture the image
2. Get an image after 30 frames
3. Delete the camera
4. Threshold the image
5. Find contours
6. Approximate contours
7. Show correct shape
19. Page 19
Raspbery pi Circuit
Raspberry pi
Circuit
Monitor
Keyboard
Mouse
Small credit card
size single board
computer
Low cost
Simplicity
Easy to handle
Rasbian Jessie used
as operating system
OpenCV,
Python,Numpy
should be installed
20. Page 20
Raspberry –pi Circuit
5v Micro usb
HDMI port
CSI Camera
connector Ethernet socket
Usb Ports
Micro SD card slot
21. Page 21
Software Installation
• Installing OpenCV 3 on Raspbian Jessie
Installing OpenCV 3 is a multi-step (and even time consuming) process requiring you to
install many dependencies and pre-requisites.
– Step #1: Install dependencies
– Step #2: Grab the OpenCV source code
– Step #3: Setup Python
– Step #4: Compile and install OpenCV
– Step #5: Finishing the install
– Step #6: Verifying your OpenCV 3 install
22. Page 22
SSH via direct Ethernet cable
• What is the meaning of SSH?
Secure Shell (SSH) is a UNIX-based command
interface and protocol for securely getting access to
a remote computer.
• What is the use of SSH server?
An SSH server is a software program which uses
the secure shell protocol to accept connections from
remote computers. SFTP/SCP file transfers and
remote terminal connections are popular use cases
for an SSH server.
26. Page 26
OpenCV
Since Version 2.2, the OpenCV library is divided into several modules.
1.The opencv_core module that contains the core functionalities of the library, in
particular, basic data structures and arithmetic functions
2.The opencv_imgproc module that contains the main image processing functions
3.The opencv_highgui module that contains the image and video reading and writing
functions along with some user interface functions
4.The opencv_features2d module that contains the feature point detectors and
descriptors and the feature point matching framework
5.The opencv_calib3d module that contains the camera calibration, two-view
geometry estimation, and stereo function
27. Page 27
6.The opencv_video module that contains the motion estimation, feature
tracking, and
foreground extraction functions and classes
7.The opencv_obj detect module that contains the object detection functions
such as
the face and people detectors