Beginner'S Guide To Learn Computer Vision With Python

Learn to implement popular CV algorithms with OpenCV python library

What you'll learn

Learn fundamentals of Computer Vision

Understand state of art image and video processing Algorithms in CV

Understand application of Deep Learning Models in the CV

Learn to implement CV algorithms with OpenCV python library

Requirements

Familiar with python programming language and any python IDE (like PyCharm)

Description

Learn fundamentals of Computer Vision with state of art image and video processing Algorithms.Course StructureIntroduction:IntroductionReal world ApplicationsPopular Computer Vision Techniques:Image SegmentationDemo - Image SegmentationEdge DetectionDemo - Edge DetectionFeature ExtractionDemo - Feature ExtractionApplication of CV techniquesObject Detection, Tracking and Classification:Object DetectionObject TrackingImage ClassificationDemo: Image ClassificationChallenges in CVDeep Learning for Computer Vision:What is Deep Learning?Convolutional Neural Network (CNN)Demo - CNNTransfer LearningBenefits of Deep Learning in CVImage Recognition:Face Detection and RecognitionDemo - Face DetectionOptical Character Recognition (OCR)Demo - OCRAdvanced Techniques - Panorama Creation:Image RegistrationImage StitchingDemo - Image StitchingMotion Analysis:Motion AnalysisVideo ProcessingBackground SubtractionDemo: Background SubtractionRealtime Video Processing:Realtime Video ProcessingDemo - Object DetectionApplication in RoboticsRequirementsBasics knowledge of computer programmingFamiliar with python programming language and any python IDE (like PyCharm)Windows / Linux / Mac OS X Machine with InternetContent teamExpert: Arunkumar KrishnanProduction: Vishnu Sakthivel, Visshwa BalasubramanianWhat you will learn?Learn fundamentals of Computer VisionUnderstand state of art image and video processing Algorithms in CVUnderstand application of Deep Learning Models in the CVLearn to implement CV algorithms with OpenCV python library

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Real word Applications

Lecture 3 Summary

Section 2: Popular Computer Vision Techniques

Lecture 4 Image Segmentation

Lecture 5 Demo - Image Segmentation

Lecture 6 Edge Detection

Lecture 7 Demo - Edge Detection

Lecture 8 Feature Extraction

Lecture 9 Demo - Feature Extraction

Lecture 10 Application of CV techniques

Lecture 11 Summary

Section 3: Object Detection, Tracking and Classification

Lecture 12 Object Detection

Lecture 13 Object Tracking

Lecture 14 Image Classification

Lecture 15 Demo: Image Classification

Lecture 16 Challenges in CV

Lecture 17 Summary

Section 4: Deep Learning in Computer Vision

Lecture 18 What is Deep Learning?

Lecture 19 Convolutional Neural Network (CNN)

Lecture 20 Demo - CNN

Lecture 21 Transfer Learning

Lecture 22 Demo - Transfer Learning

Lecture 23 Benefits of Deep Learning in CV

Lecture 24 Summary

Section 5: Image Recognition

Lecture 25 Face Detection and Recognition

Lecture 26 Demo - Face Detection

Lecture 27 Optical Character Recognition (OCR)

Lecture 28 Demo - OCR

Lecture 29 Summary

Section 6: Advanced Techniques - Panorama Creation

Lecture 30 Image Registration

Lecture 31 Image Stitching

Lecture 32 Demo - Image Stitching

Lecture 33 Summary

Section 7: Motion Analysis

Lecture 34 Motion Analysis

Lecture 35 Video Processing

Lecture 36 Background Subtraction

Lecture 37 Demo: Background Subtraction

Lecture 38 Summary

Section 8: Realtime Video Processing

Lecture 39 Realtime Video Processing

Lecture 40 Demo - Object Detection

Lecture 41 Application in Robotics

Lecture 42 Summary

Freshers and experienced professionals interested in learning 'Computer Vision'