Introduction to Computer Vision and Image Processing

Introduction to Computer Vision and Image Processing

Instructors: Aije Egwaikhide

What you'll learn

Describe the applications of computer vision across different industries.
Apply image processing and analysis techniques to computer vision problems.
Utilize Python, Pillow, and OpenCV for basic image processing and perform image classification and object detection.
Create an image classifier using Supervised learning techniques.

Skills you'll gain

Details to know

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Assessments

10 assignments

Taught in English

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There are 6 modules in this course

Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more. In this beginner-friendly course, you will understand computer vision and learn about its various applications across many industries.

As part of this course, you will utilize Python, Pillow, and OpenCV for basic image processing and perform image classification and object detection. This is a hands-on course and involves several labs and exercises. Labs will combine Jupyter Labs and Computer Vision Learning Studio (CV Studio), a free learning tool for computer vision. CV Studio allows you to upload, train, and test your own custom image classifier and detection models. At the end of the course, you will create your own computer vision web app and deploy it to the Cloud. This course does not require any prior Machine Learning or Computer Vision experience. However, some knowledge of the Python programming language and high school math is necessary.

In this module, we will discuss the rapidly developing field of image processing. In addition to being the first step in Computer Vision, it has broad applications ranging anywhere from making your smartphone's image look crystal clear to helping doctors cure diseases.

What's included

4 videos2 readings2 assignments1 plugin

4 videosTotal 15 minutes

Introduction to Computer Vision4 minutesPreview module
Applications of Computer Vision3 minutes
Recent Research in Computer Vision4 minutes
Brainstorming Your Own Applications3 minutes

2 readingsTotal 12 minutes

Course Overview2 minutes
Articles10 minutes

2 assignmentsTotal 18 minutes

Practice Assessment8 minutes
Graded Quiz: Overview of Computer Vision and its Applications10 minutes

1 pluginTotal 15 minutes

Computer Vision in Action15 minutes

Image processing enhances images or extracts useful information from the image. In this module, we will learn the basics of image processing with Python libraries OpenCV and Pillow.

What's included

6 videos2 assignments9 app items

6 videosTotal 41 minutes

What Is A Digital Image 7 minutesPreview module
Manipulating Images2 minutes
Manipulating Images One Pixel At a Time4 minutes
Pixel Transformations8 minutes
Geometric Operations6 minutes
Spatial Operations in Image Processing9 minutes

2 assignmentsTotal 28 minutes

Practice Assessment8 minutes
Graded Quiz: Image Processing20 minutes

9 app itemsTotal 270 minutes

Image processing with Pillow30 minutes
Image processing with OpenCV30 minutes
Basic Image Manipulation with Pillow30 minutes
Basic Image Manipulation OpenCV30 minutes
Histograms and Intensity Transformations30 minutes
Geometric Transformations with Pillow30 minutes
Geometric Transformations with OpenCV30 minutes
Spatial Filtering Pillow30 minutes
Spatial Filtering OpenCV30 minutes

In this module, you will Learn About the different Machine learning classification Methods commonly used for Computer vision, including k nearest neighbours, Logistic regression, SoftMax Regression and Support Vector Machines. Finally, you will learn about Image features.

What's included

8 videos2 assignments6 app items2 plugins

8 videosTotal 37 minutes

Introduction to Image Classification3 minutesPreview module
Image Classification with KNN5 minutes
Linear Classifiers5 minutes
Logistic Regression Training: Gradient Descent6 minutes
Mini-Batch Gradient Descent2 minutes
SoftMax and Multi-Class Classification3 minutes
Support Vector Machines 6 minutes
Image Features 4 minutes

2 assignmentsTotal 23 minutes

Practice Assessment8 minutes
Graded Quiz: Image Classification15 minutes

6 app itemsTotal 220 minutes

Obtain IBM Cloud Feature Code and Activate Trial Account60 minutes
Label your Data and Perform Image Classification with KNN45 minutes
Logistic Regression With Mini-Batch Gradient Descent15 minutes
Hand-written Digits Image Classification with Softmax25 minutes
Support Vector Machines vs Vanilla Linear Classifier15 minutes
Image Classification with SVM and CV Studio60 minutes

2 pluginsTotal 30 minutes

Set up Computer Vision learning environment15 minutes
Getting started with CV Studio!15 minutes

In this module, you will learn about Neural Networks, fully connected Neural Networks, and Convolutional Neural Network (CNN). You will learn about different components such as Layers and different types of activation functions such as ReLU. You also get to know the different CNN Architecture such as ResNet and LenNet.

What's included

4 videos2 assignments6 app items1 plugin

4 videosTotal 19 minutes

Neural Networks4 minutesPreview module
Fully Connected Neural Network Architecture3 minutes
Convolutional Networks 6 minutes
CNN Architectures4 minutes

2 assignmentsTotal 23 minutes

Practice Assessment8 minutes
Graded Quiz: Neural Networks15 minutes

6 app itemsTotal 193 minutes

Simple Neural Network for XOR25 minutes
Neural Network Rectified Linear Unit (ReLU) vs Sigmoid25 minutes
Training A Neural Network with Momentum25 minutes
Convolutional Neural Network39 minutes
Data Augmentation39 minutes
Use CNN for "Hotdog, Not Hotdog" Classifier and Deploy Model with CV Studio 40 minutes

1 pluginTotal 10 minutes

Deploying a Model10 minutes

In this module, you will learn about object detection with different methods. The first approach is using the Haar Cascade classifier, the second one is to use R-CNN and MobileNet.

What's included

2 videos1 reading2 assignments3 app items

2 videosTotal 9 minutes

Object Detection5 minutesPreview module
Object Detection with Haar Cascade Classifier4 minutes

1 readingTotal 10 minutes

Object Detection with Deep Learning 10 minutes

2 assignmentsTotal 33 minutes

Practice Assessment8 minutes
Graded Quiz: Object Detection25 minutes

3 app itemsTotal 150 minutes

Car Detection with Haar Classifiers60 minutes
Object Detection with Faster R- CNN30 minutes
Object Detection using Pre-trained Models CV Studio60 minutes

In the final week of this course, you will build a computer vision app that you will deploy on the cloud through Code Engine. For the project, you will create a custom classifier, train it and test it on your own images.