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MAI5021 - Image Analysis and Representation (2023)
Home
Courses
2023
ICMC
MAI
MAI5021-2023
Assignment 02: morphology
Test Cases for Assignment 02
Test Cases for Assignment 02
Click
tests_and_images.zip
link to download the file.
◄ Specification for Assignment 02
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Git Repository with the Notebooks used on lectures
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
Submission for Assignment 02
Submission for Assignment 02 ►