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MAI5021 - Image Analysis and Representation (2023)
Startseite
Kurse
2023
ICMC
MAI
MAI5021-2023
Allgemeines
Git Repository with the Notebooks used on lectures
Git Repository with the Notebooks used on lectures
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Git Repository with the Notebooks used on lectures
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Course Presentation
DIP_01_Fundamentals
[interactive video 1-1] Image, digital image and sensors
[interactive video 1-2] Natural vision and quantization
[interactive video 1-3] Color quantization and brightness perception
[video 1-4] Coding environment Python 3 and Jupyter Notebook
[interactive video 1-5] Implementing an image processing program in Python
DIP_06_Color
[interactive video 6-1] Introduction: what is color, light spectrum
[interactive video 6-2] Color models: RGB, CMYK, XYZ, Lab, HSV
[interactive video 6-3] Examples of color image processing in Python
Exam - Fundamentals of Image Processing
Exam - Color Image Processing
DIP_07_TextureAnalysis
[interactive video 7-1] Basic color and texture descriptors
[interactive video 7-2] Color histograms and distance functions in python
[interactive video 7-3] Texture descriptors: Haralick (GLCM) and LBP
[video 7-4] Texture analysis with LBP implemenation in python and skimage
[interactive video 7-5] Bag of Features
Exam - Image Description and Representation
DIP_08_Morphological_processing
[interactive video 8-1] Mathematical Morphology definitions and basic operators
[interactive video 8-2] Dilation and Erosion in Python
[interactive video 8-3] building new operators: opening and closing
[interactive video 8-4] Implementing the hit-or-miss in python
[interactive video 8-5] Graylevel image processing with morphology
Exam - Mathematical Morphology
DIP_09_Segmentation
[interactive video 9-1] Segmentation and threshold-based methods
[interactive video 9-2] Thresholding and Otsu in Python
[interactive video 9-3] Edge detection and Region-based segmentation
[video 9-4] Region-based pixel aggregation in python
[interactive video 9-5] Introduction to Hough transform and (very brief introduction) to Watershed segmentation
[interactive video 9-6] Watershed implementation in Python and Distance Transform
Exam - Image Segmentation
DIP_10_CNNs
[interactive video 10-1] Deep Neural Networks for Image Classification - Motivation
[video 10-2] Machine Learning vs Deep Learning
[video 10-3] Optimizing/learning a linear classifier
[interactive video 10-4] Dense Network for Digit Recognition
[interactive video 10-5] Convolutional Networks
[video 10-6] Popular CNN Architectures
[interactive video 10-7] Training Strategies and Limitations
Exam - Neural Networks for Image Classification
Specification for Assignment 01
Test Cases for Assignment 01
Submission for Assignment 01
Specification for Assignment 02
Test Cases for Assignment 02
Submission for Assignment 02
Course Presentation ►