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Computer Vision with OpenCV

Discover the intriguing realm of computer vision using OpenCV, the open-source library for sophisticated image and video analysis.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Discover the intriguing realm of computer vision using OpenCV, the open-source library for sophisticated image and video analysis.

  2. This course offers practical training to help you understand essential concepts and methods, such as image processing and object detection. – Learn to perform real-time video analysis effectively.

  3. Acquire the expertise needed to develop computer vision applications across various sectors, including healthcare, robotics, and automotive.

  4. Enhance your skill set and open new career opportunities in the rapidly evolving field of computer vision.

CERTIFICATION:

  1. Earn a Certified Computer Vision Specialist with OpenCV credential, showcasing your ability to develop image and video analysis solutions.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Discover the intriguing realm of computer vision using OpenCV, the open-source library for sophisticated image and video analysis.

  2. This course offers practical training to help you understand essential concepts and methods, such as image processing and object detection.

  3. Learn to perform real-time video analysis effectively.

  4. Acquire the expertise needed to develop computer vision applications across various sectors, including healthcare, robotics, and automotive.

  5. Enhance your skill set and open new career opportunities in the rapidly evolving field of computer vision.

Enroll Now

Course Curriculum

Introduction to Computer Vision and OpenCV
  1. Overview of Computer Vision
    • What is computer vision and its applications in various industries (e.g., healthcare, autonomous vehicles, robotics).
    • Key challenges in computer vision.
  2. Introduction to OpenCV
    • What is OpenCV?
    • History and features of OpenCV.
    • Installing OpenCV and setting up the environment in Python.
Understanding Image Processing Fundamentals
  1. Digital Image Representation
    • Pixels, image channels (RGB, Grayscale), and image formats.
    • Image resolution, aspect ratio, and size manipulation.
  2. Basic Image Operations
    • Reading, displaying, and saving images using OpenCV functions.
    • Image properties: shape, size, and type.
    • Basic transformations: resizing, rotating, flipping, and cropping.
  3. Color Spaces and Image Conversion
    • RGB, HSV, Lab, and Grayscale color spaces.
    • Converting between different color spaces.
Image Enhancement and Filtering
  1. Image Filtering
    • Convolution, smoothing, and blurring (e.g., Gaussian blur, median filter).
    • Sharpening images using kernels.
  2. Edge Detection
    • Canny edge detection.
    • Sobel and Laplacian operators for edge detection.
  3. Thresholding Techniques
    • Simple, adaptive, and Otsu thresholding methods.
    • Binary and inverse binary thresholding.
Geometric Transformations and Image Registration
  1. Affine and Perspective Transformations
    • Scaling, rotation, translation, and shearing transformations.
    • Perspective transformations (e.g., homography).
  2. Image Warping
    • WarpAffine and warpPerspective for transforming images.
    • Applications in document scanning and panorama stitching.
Contour Detection and Object Detection
  1. Contours and Shape Detection
    • Finding and drawing contours on images.
    • Approximation of contours and shape detection.
  2. Object Detection
    • Detecting simple objects using color-based or shape-based methods.
    • Using contour area and bounding boxes to identify objects.
  3. Haar Cascades for Face Detection
    • Introduction to Haar feature-based Cascade Classifiers for face and object detection.
    • Training and using pre-trained Haar cascades for face detection.
Feature Detection and Matching
  1. Feature Detection Algorithms
    • Introduction to keypoints and descriptors (SIFT, SURF, ORB).
    • Detecting keypoints and computing descriptors for images.
  2. Feature Matching
    • Matching keypoints between images using BFMatcher and FLANN.
    • RANSAC for robust matching.
Image Segmentation
  1. Thresholding-based Segmentation
    • Simple, adaptive, and Otsu thresholding.
  2. Clustering for Segmentation
    • K-Means clustering for image segmentation.
  3. Watershed Algorithm
    • Using the watershed algorithm for separating overlapping objects in images.
Object Tracking and Motion Analysis
  1. Object Tracking Basics
    • Introduction to object tracking algorithms in OpenCV (e.g., KLT, MeanShift, and CAMShift).
    • Implementing object tracking on video streams.
  2. Optical Flow
    • Understanding optical flow and its application in motion analysis.
    • Implementing optical flow with Lucas-Kanade method.
Deep Learning and Computer Vision
  1. Introduction to Deep Learning in CV
    • Using pre-trained deep learning models for object detection (e.g., YOLO, SSD).
    • OpenCV DNN module for running deep learning models.
  2. Transfer Learning
    • Using pre-trained models (like MobileNet, VGG16) to classify and detect objects.
    • Fine-tuning models for specific tasks using custom datasets.
Capstone Project
  1. End-to-End Computer Vision Project
    • Complete a computer vision project using OpenCV. Examples include:
      • Face recognition system.
      • Object detection in video feeds.
      • Real-time motion tracking for interactive applications.
    • Incorporate data preprocessing, image processing, and deep learning techniques as needed.

Training Features

Hands-on Projects

Practical exercises and projects for solving real-world problems.

Comprehensive OpenCV Library

In-depth understanding and usage of OpenCV’s rich set of tools.

Advanced Computer Vision Techniques

Mastery of advanced concepts such as feature matching, segmentation, and motion analysis.

Real-Time Applications

Building applications for real-time object detection, tracking, and augmented reality.

Deep Learning Integration

Integrating deep learning models for high-performance object detection and classification.

Certification

A professional certificate validating expertise in OpenCV and computer vision.

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