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Image Processing and Analysis (2023)
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Courses
2023
ICMC
SCC
IPA-1-2023
Neural Networks for Images (Topic 10)
DIP_10_CNNs
DIP_10_CNNs
Click
DIP_10_CNNs.pdf
link to view the file.
◄ Exam 10
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Git Repository with Notebooks used during classes
Course Presentation
Live: Course Presentation (in Portuguese) - access only with USP email
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
Exam 1
DIP_02_ImageEnhancement_PointOp-Filtering
[interactive video 2-1] Definitions and pixel-based transformation functions
[interactive video 2-2] Common point operators
[interactive video 2-3] Coding point operators in Python
[interactive video 2-4] histogram-based image processing
[interactive video 2-5] Coding histogram equalization
Exam 2
[interactive video 3-1] filtering and 1D convolutions
[interactive video 3-2] 2D convolution
[interactive video 3-3] Cross-correlation and smoothing filters
[interactive video 3-4] Differential filters and sharpening filters
[interactive video 3-5] coding 2d convolutions for image filtering
[interactive video 3-6] examples of filters in Python
[video 3-7] Bonus content: using gitlab/git for code maintenance
Exam 3
DIP_04_FourierTransform_part1
DIP_04_FourierTransform_part2_2022
[interactive video 4-1] Fundamentals: representations of functions
[interactive video 4-2] Fourier Transform: History, Periodicity and Sinusoids
[interactive video 4-3] Fourier Series and Complex Exponentials
[interactive video 4-4] Frequency Analysis with Fourier Series
[interactive video 4-5] Introduction to Frequency analysis with numpy and python
[interactive video 4-6] Discrete Fourier Transform 1D and 2D with source code
[interactive video 4-7] 2D-DFT and its properties
[interactive video 4-8] Processing images in the frequency domain
[interactive video 4-9] Fast Fourier Transform Algorithm
[interactive video 4-10] Fast Fourier Transform implementation in Python
Exam 4
DIP_05_Restoration
[interactive video 5-1] Image formation model and common sources of noise
[interactive video 5-2] Noise simulation, filters and adaptive filters
[interactive video 5-3] Implementing noise simulation and denoising filters
[interactive video 5-4] Blur effect and least squares filters
[interactive video 5-5] Deconvolution / de-blurring with (pseudo)-inverse filter in python
Exam 5
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 6
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 7
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 8
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 9
[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 10
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
Specification for Assignment 03
Test Cases for Assignment 03
Submission for Assignment 03
Specification for Assignment 04
Test cases for Assignment 04
Submission for Assignment 04
Specification for Assignment 01 ►