Image Processing | Opencv By SeaportAi

What you'll learn
What is image processing
How to use OpenCV to solve image problems
What is morphological operation
How to manipulate the images
How to add and subtract images
What are Noising and De-noising models
How to dilate and erode image
What is a color histogram
How to extract features from an image
What is Gaussian blur

Requirements
Basic knowledge of python and machine learning

Description
Unleash the power of computer vision with our comprehensive course on OpenCV and Image Processing. This dynamic course is designed for beginners and intermediate learners, delving deep into the practical aspects of computer vision, leveraging the OpenCV library.Start your journey with a brief introduction to OpenCV, understanding its history, significance, and varied applications. Swiftly move on to its installation process, setting up the coding environment in different operating systems. The course imparts foundational knowledge of image processing techniques such as image manipulation, filtering, and transformation using OpenCV.Get hands-on experience with real-world projects encompassing object detection, face recognition, optical character recognition, and more. Each project will hone your understanding of computer vision algorithms and how to implement them using OpenCV's extensive features.Explore the exciting realm of machine learning in computer vision, applying sophisticated techniques like neural networks and deep learning. Understand how to work with video sequences, perform video analysis, and even tap into motion detection and tracking.By the end of this course, you'll have a strong grasp on OpenCV and Image Processing techniques, propelling you towards a promising career in computer vision. Whether you're an aspiring data scientist, a robotics enthusiast, or a software developer, this course equips you with the skills to build powerful visual recognition systems.

Overview
Section 1: Introduction

Lecture 1 Introduction

Section 2: Basic Modules in OpenCV

Lecture 2 Reading and writing an image

Lecture 3 Color transformations

Lecture 4 Addition and Subtraction (Arithmetic Operations) of images

Lecture 5 Bitwise Operations: And, Or, Not, Xor

Section 3: Image Manipulation

Lecture 6 Image Flip, Rotation, Brightness

Lecture 7 Image Resizing

Lecture 8 Image Blurring

Section 4: Image Enhancement

Lecture 9 Image Enhancement

Section 5: Noising and De-noising models

Lecture 10 Noising and De-noising models

Section 6: Morphological Processing & Algorithms

Lecture 11 Understanding morphological processing

Lecture 12 Dilation and Erosion

Lecture 13 Opening and Closing

Lecture 14 Boundary Extraction and Region Filling

Lecture 15 Convex Hull and Finding Contours

Section 7: Image Segmentation

Lecture 16 Image Segmentation

Section 8: Image/Features Descriptors

Lecture 17 Understanding Descriptors

Lecture 18 LBP and Wavelet Feature Extraction

Lecture 19 Color Histogram

Section 9: Open CV and Deep Learning: Building a Tumor Detection Model

Lecture 20 Open CV and Deep Learning: Building a Tumor Detection Model

Python developers,Machine learning enthusiasts,Data scientists,Students

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