Image Processing and Computer Vision with Python & OpenCV

Image Processing and Computer Vision with Python & OpenCV, available at $49.99, has an average rating of 3.4, with 77 lectures, based on 98 reviews, and has 597 subscribers.

You will learn about Image Processing with Python (skimage) (90% hands on and 10% theory) Image Processing and Computer Vision with OpenCV (90% hands on and 10% theory) Morphological operations with OpenCV (90% hands on and 10% theory) Face detection with OpenCV (90% hands on and 10% theory) Feature detection with OpenCV (90% hands on and 10% theory) Image matching with skimage (90% hands on and 10% theory) Object detection with OpenCV (90% hands on and 10% theory) Digit recognition with OpenCV (90% hands on and 10% theory) This course is ideal for individuals who are Any one eager to know about Image Processing and Computer Vision It is particularly useful for Any one eager to know about Image Processing and Computer Vision.

Enroll now: Image Processing and Computer Vision with Python & OpenCV

Summary

Title: Image Processing and Computer Vision with Python & OpenCV

Price: $49.99

Average Rating: 3.4

Number of Lectures: 77

Number of Published Lectures: 77

Number of Curriculum Items: 77

Number of Published Curriculum Objects: 77

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Image Processing with Python (skimage) (90% hands on and 10% theory)
Image Processing and Computer Vision with OpenCV (90% hands on and 10% theory)
Morphological operations with OpenCV (90% hands on and 10% theory)
Face detection with OpenCV (90% hands on and 10% theory)
Feature detection with OpenCV (90% hands on and 10% theory)
Image matching with skimage (90% hands on and 10% theory)
Object detection with OpenCV (90% hands on and 10% theory)
Digit recognition with OpenCV (90% hands on and 10% theory)

Who Should Attend

Any one eager to know about Image Processing and Computer Vision

Target Audiences

Any one eager to know about Image Processing and Computer Vision

The Image Processing and Computer Vision world is too big to comprehend. It has been backbone of many industry including Deep Learning. It is used across multiple places. As practitioner, I am trying to bring many relevant topics under one umbrella in following topics.

1. Image Processing with Python (skimage) (90% hands on and 10% theory)

2. Image Processing and Computer Vision with OpenCV (90% hands on and 10% theory)

3. Morphological operations with OpenCV (90% hands on and 10% theory)

4. Face detection with OpenCV (90% hands on and 10% theory)

5. Feature detection with OpenCV (90% hands on and 10% theory)

6. Image matching with skimage (90% hands on and 10% theory)

7. Object detection with OpenCV (90% hands on and 10% theory)

8. Digit recognition with OpenCV (90% hands on and 10% theory)

9. Autonomous vechile detection and movement. (90% hands on and 10% theory)

10. Deep learning concepts useful for Image Processing and Computer Vision. (90% hands on and 10% theory)

11. Python practice from Data Science point of view. (90% hands on and 10% theory)

12. The assignment will make you hands-on in Image Processing and Computer Vision.

13. ML practice useful for Image Processing and Computer Vision. (90% hands on and 10% theory)

14. Many other useful topics in Image Processing and Computer Vision. (90% hands on and 10% theory)

Course Curriculum

Chapter 1: Introduction and Foundation

Lecture 1: Introduction

Lecture 2: Installations

Lecture 3: Technologies

Lecture 4: Definition of image processing and Computer vision

Lecture 5: Explanation of Images attributes

Lecture 6: Color Spaces – BW vs Grey vs RGB

Chapter 2: sklearn – Image processing

Lecture 1: sklearn – explore color image

Lecture 2: sklearn – explore gray image

Lecture 3: images explore using various Numpy features

Lecture 4: images explore by combining two masks

Lecture 5: images explore by channel masks

Lecture 6: Count of White dots

Lecture 7: Few more exploration

Lecture 8: Color image manipulation

Lecture 9: Histograms

Lecture 10: Histogram equalization

Lecture 11: Thresholding

Lecture 12: Color spaces – CIELAB

Lecture 13: Image Filters

Lecture 14: Image Filters – sklearn code

Lecture 15: Edge Detection

Lecture 16: Edge Detection – sklearn code

Chapter 3: opencv – Image processing

Lecture 1: OpenCv – image read and write

Lecture 2: OpenCv – video read and write

Lecture 3: Draw different geometric shapes

Lecture 4: Get the points where mouse is clicked

Lecture 5: Write on each frame of Video

Lecture 6: Playing with color

Lecture 7: Various transformations

Chapter 4: Morphological operations

Lecture 1: Blurring – a low pass filter

Lecture 2: Inbuilt blur functions

Lecture 3: Sharpening and Embossing

Lecture 4: High pass filtering – Edge detection

Lecture 5: Histogram equalization

Lecture 6: Morphological operations – Erosion and Dilation

Lecture 7: Basic image Arithmetic

Lecture 8: Put one image on another background

Lecture 9: Put green image on forest background – Usage of changing color by HSV

Lecture 10: GrabCut Foreground

Lecture 11: Identify color

Lecture 12: Background subtraction using BackgroundSubtractor class

Lecture 13: Contours Prerequisite

Lecture 14: Contours – Extract and Draw

Lecture 15: Image matching using Hu-Moments and count of vertices

Lecture 16: Image grouping using KMeans Clustering

Lecture 17: Convexity defect

Chapter 5: Face detection

Lecture 1: Face Detection for Images

Lecture 2: Face Detection for Videos

Lecture 3: Image Denoising

Lecture 4: Video Denoising

Chapter 6: Feature detection

Lecture 1: Feature Detection

Lecture 2: Harris Corner Detector

Lecture 3: Harris Corner with SubPixel Accuracy

Lecture 4: Shi-Tomasi Corner Detector & Good Features to Track

Lecture 5: FYI – SIFT and SURF

Lecture 6: Features from accelerated segment test (FAST)

Lecture 7: ORB (Oriented FAST and Rotated BRIEF) corner detectors

Chapter 7: Image matching

Lecture 1: Image matching – Introduction

Lecture 2: Image matching – after Erosion and Dilation

Lecture 3: Image matching of human face

Chapter 8: Object detection

Lecture 1: Object detection history and Deep learning models

Lecture 2: Object detection introduction and Installations

Lecture 3: Object detection – model setup

Lecture 4: Object detection – objects on images

Lecture 5: Social Distancing

Lecture 6: Object Tracking

Chapter 9: Digit recognition

Lecture 1: Data preparation

Lecture 2: Model – LinearSVC building

Lecture 3: Model – RandomForest building

Lecture 4: Prediction of digits on image

Lecture 5: Prediction of digits on video – part 1

Lecture 6: Prediction of digits on video – part 2

Chapter 10: Image segmentation

Lecture 1: Image segmentation – summary

Lecture 2: Color Quantization

Chapter 11: Vehicle detection

Lecture 1: Vehicle detection

Lecture 2: Lane Detection

Chapter 12: Miscellaneous

Lecture 1: Definitions

Instructors

Shiv Onkar Deepak Kumar
AI Researcher and Consultant, Chief Data Scientist

Rating Distribution

1 stars: 4 votes
2 stars: 11 votes
3 stars: 20 votes
4 stars: 27 votes
5 stars: 36 votes

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