Photogrammetry I & II Course (2021/22)

Slides

Photogrammetry I:
PDF-00, PDF-01, PDF-02, PDF-03, PDF-04, PDF-05, PDF-06, PDF-07,
PDF-08, PDF-09, PDF-10, PDF-11, PDF-12, PDF-13, PDF-14, PDF-15,
PDF-16, PDF-16, PDF-18, PDF-19, PDF-20, PDF-21, PDF-22, PDF-23.

Photogrammetry II:
PDF-01, PDF-02, PDF-03, PDF-04, PDF-05, PDF-06, PDF-07, PDF-08,
PDF-09, PDF-10, PDF-11, PDF-12, PDF-13, PDF-14, PDF-15, PDF-16.

1st Term:

Photogrammetry I

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: Link

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 (external video)

Python Crash Course For Beginners

Week 2

Python Crash Course (cont.)

Python Crash Course (external video)

Jupyter Notebook Lab Tutorial

Python Crash Course (external video)

Python NumPy for Beginners

Python Crash Course (external video)

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

Camera Basics and Propagation of Light (Cyrill Stachniss)

Camera Basics and Propagation of Light
Slides: Link

Week 4

Image Histograms – 5 Minutes with Cyrill

Image histograms explained in 5 minutes
Series: 5 Minutes with Cyrill

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

Image Histograms Part 1: Image Histograms and Simple Point Operators
Slides: Link

Image Histograms – Part2: Histograms Transformations (Cyrill Stachniss)

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

Week 5

Binary Images (Cyrill Stachniss)

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

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: Link

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: Link

Image Segmentation using Mean Shift (Cyrill Stachniss)

Image Segmentation using the Mean Shift Algorithm
Slides: Link

Week 9

Introduction to Classification (Nived Chebrolu)

Introduction to Classification
Slides: Link

Classification – Ensemble Methods (Nived Chebrolu)

Classification – Ensemble Methods
Slides: Link

Week 10

Neural Networks – 5 Minutes with Cyrill

Neural networks explained in 5 minutes
Series: 5 Minutes with Cyrill

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

Lecture on 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: Link

Gradient Descent – 5 Minutes with Cyrill

Gradient descent explained in 5 minutes
Series: 5 Minutes with Cyrill

Backpropagation – 5 Minutes with Cyrill

Backpropagation explained in 5 minutes
Series: 5 Minutes with Cyrill

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

Lecture on 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.