### Slides

### Week 1

### Introduction

#### Introduction to Photogrammetry (Cyrill Stachniss)

Introductory Lecture for the Photogrammetry Module consisting of the courses Photogrammetry I and & at the University of Bonn.

Slides: PDF

#### Photogrammetry Course – Lecture & Tutorial Information for Students Enrolled at the University of Bonn

Notes about lectures, tutorials, homework assignments, and formal aspects for the Photogrammetry I & II Course, taught in the BSc programme Geodesy and Geoinformation at the University of Bonn for the summer term 2021 and winter term 2021/2022.

#### Python Crash Course For Beginners

### Week 2

### Python Crash Course (cont.)

#### Jupyter Notebook Lab Tutorial

#### Python NumPy for Beginners

#### Matplotlib (Part 1): Creating and Customizing Our First Plots

### Week 3

### Technical Content on Photogrammetry Starts

#### What Cameras Measure – 5 Minutes with Cyrill

What do cameras actually measure explained in 5 minutes

Series: 5 Minutes with Cyrill

Credits:

Video by Cyrill Stachniss

Special thanks to Olga Vysotska and Igor Bogoslavskyi

Partial image courtesy Brunox983@pixabay

Intro music by The Brothers Records

#### Camera Basics and Propagation of Light (Cyrill Stachniss)

Camera Basics and Propagation of Light

Slides: PDF

### Week 4

#### Image Histograms – 5 Minutes with Cyrill

Image histograms explained in 5 minutes

Series: 5 Minutes with Cyrill

Credits:

Video by Cyrill Stachniss

Special thanks to Olga Vysotska and Igor Bogoslavskyi

Image courtesy by O. Vysotska, W. Förstner, Alexas_Fotos@pixabay, P. Carper

Intro music by The Brothers Records

#### Image Histograms – Part1: Histograms and Point Operators (Cyrill Stachniss)

Image Histograms Part 1: Image Histograms and Simple Point Operators

Slides: PDF

#### Image Histograms – Part2: Histograms Transformations (Cyrill Stachniss)

Image Histograms – Part 2: Histograms Transformations, Histogram Equalizations and Noise Variance Equalizations

Slides: PDF

### Week 5

#### Binary Images (Cyrill Stachniss)

Binary Images and Commonly used Operations: Connected Components, Distance Transform, Morphological Operators

Slides: PDF

#### Local Operators Through Convolutions – Part 1: Smoothing (Cyrill Stachniss)

Local operators defines in the framework of convolutions looking into two smoothing kernels, namely the box filter and binomial filter.

Slides: Link

### Week 6

#### Local Operators Through Convolutions – Part 2: Gradient Filters (Cyrill Stachniss)

Local operators defines in the framework of convolutions looking into gradient kernels such as Sobel, Scharr, or Laplace. The video basically explains how to compute a derivative of an image.

Slides: Link

#### Geometric Transformation of Images (Cyrill Stachniss)

Geometric Transformation of Images

Slides: Link

### Week 7

#### Image Matching using Cross Correlation (Cyrill Stachniss)

Image Matching using Cross Correlation

Slides: Link

#### Visual Feature Part 1: Computing Keypoints (Cyrill Stachniss)

Visual Feature Part 1: Computing Keypoints

Slides: PDF

### Week 8

#### SIFT – 5 Minutes with Cyrill

SIFT features explained in 5 minutes

Series: 5 Minutes with Cyrill

#### Binary Features – 5 Minutes with Cyrill

Binary features explained in 5 minutes

Series: 5 Minutes with Cyrill

#### Visual Features Part 2: Features Descriptors (Cyrill Stachniss)

Visual Features Part 2: Features Descriptors

Slides: PDF

#### Image Segmentation using Mean Shift (Cyrill Stachniss)

Image Segmentation using the Mean Shift Algorithm

Slides: PDF

### Week 9

#### Introduction to Classification (Nived Chebrolu)

Introduction to Classification

Slides: PDF

#### Classification – Ensemble Methods (Nived Chebrolu)

Classification – Ensemble Methods

Slides: PDF

### Week 10

#### Introduction to Neural Networks – Part 1: The Basics (Cyrill Stachniss)

Introduction to Neural Networks – What are neural networks and how do they work covering MLPs, weights, biases, and activations and examples how the hidden layers of a network look like.

Slides: PDF

#### Introduction to Neural Networks – Part 2: Learning (Cyrill Stachniss)

Introduction to Neural Networks – Part 2: Learning (Parameter Learning, Stochastic Gradient Descent, Backprop)

Slides: Link

Errata in the video (corrected in the pdf file of the slides):

* At 55:23 the value of dL\df is not specified and only indicated as “…”. This is suboptimal for the example as this value has to be multiplied with dL\da and dL\db. Thus, the example might be a bit misleading.

* At 59:37 the derivative of “z^2” is “2z” and not “z”, thus the last dimension of the gradient in the example must be multiplied with 2.

### Week 11

### Math Basics (to be recorded)

### Homogeneous Coords

### Week 12

### Camera Parameters

### Direct Linear Transform (DLT)