Introduction
(updated 2025.2).
Image Processing with Deep Learning
Deep learning innovations are driving breakthroughs in the field of computer vision for automotive, robotics, and science.
Thus, the aim of this course is to introduce students of computer vision starting from basics of digital image processing and then turning to modern deep learning models, specifically convolutional neural networks(CNN), for image enhancement and analysis applications on Energy/Environment, Autonomous Vehicle/Robots, and Machine Vision.
Students will learn how to build CNN(Convolutional Neural Network) based object detection model for vehicle, pedestrians and other target objects. Also, they will learn how to apply pre-processing and post-processing for CNN model input/output, such as calibration, filtering, image scaling, image augmentation using fundamental methods of computer vision.
This course is composed of two parts:
(1) Part1 : Digital Image Processing (8 weeks) Basics of digital image processing for pre/post-processing of CNN models will be covered including cameral calibration, spatial filtering, feature recognition, color image processing, image scaling/rotating and more. Students will learn how to apply image processing methods to build their own defect inspection and road lane detection system.
(2) Part 2: Convolutional Neural network for Object Detection (8 weeks) Basics and useful technique in building a CNN model will be covered including backpropagation, evaluation metric, regularization and more. Also, the state-of-the-art CNN models such as Inception and Yolo v3 will be introduced. Students will learn how to use their own customized dataset to train and test a CNN object detection model.
After taking this course, students will be able to build their own program for
Road lane detection
Vehicle/Pedestrian detection
Face recognition
Defect Inspection
and more
Lecture Syllabus
DLIP- 2025
03월 04일
1
T
Course Overview Introduction to Image Processing
TU: OpenCV Basics
Tutorial: Installation for OpenCV C++
03월 07일
F
TU: OpenCV Basics
03월 11일
2
T
Camera Optics/ Spatial Filter
TU: Filter (1 week)
03월 14일
F
TU: Filter
03월 18일
3
T
Histogram/ Threshold & Morphology
TU: Thresholding_Morphology
03월 21일
F
Quiz1 - written LAB: GrayScale Image Segmentation
LAB1: Grayscale image (2week)
03월 25일
4
T
LAB: GrayScale Image Segmentation
03월 28일
F
Edge,Line,Corner Detection
TU: Line, Edge detection(1 week)
04월 01일
5
T
TU: Line, Edge detection
04월 04일
F
Camera Modeling and Calibration
TU: Calibration
04월 08일
6
T
Color Image Processing
TU: Color Image Processing
LAB: Color Image Processing
LAB2: Color Image (2week)
04월 11일
F
LAB: Color Image Processing
04월 15일
7
T
Quiz2 - written
Test1-Programming
Installation Guide for Deep Learning 2024
04월 18일
F
TU: OpenCV-Python
LAB: Image Processing in Python
04월 22일
8
T
LAB: Image Processing in Python
LAB3: Image Processing in Python (2 weeks)
04월 25일
F
MLP Introduction
04월 29일
9
T
Optimization, Loss Function BackPropagation
TU: Pytorch Exercise (MLP)
05월 02일
F
Overview of CNN, Case Study: LeNet
TU: Pytorch (LeNet-5) // T2-1 TU: Pytorch (LeNet-5) // T2-2 , T2-3
05월 06일
10
T
(휴일)
05월 09일
F
CNN (loss function & regualization, evaluation)
TU: Pytorch(VGG, MobileNet) T3-1
05월 13일
11
T
TU: Pytorch (VGG, MobileNet) T3-2, T3-3 TU: Classification (Cat/Dog)
Quiz 3- Written
05월 16일
F
Object Detection CNN models
TU: YOLOv5 in Pytorch (Install/Test) T4-2,T4-3 TU: Object Detection
LAB4: Object Detection(2weeks)
05월 20일
12
T
Special Topics in Deep Learning
Final Lab Proposal
05월 23일
F
Test 2: Programming
05월 27일
13
T
Final Lab
LAB5: Final Lab ( Report by 16th week)
05월 30일
F
Final Lab
06월 03일
14
T
Progressive Presentation
06월 06일
F
(휴일)
Final Lab
06월 10일
15
T
Final Lab
06월 13일
F
Demonstration
06월 17일
16
T
Report Due
06월 20일
F
Part 1
Introduction to Image Processing
Optics / Calibration
Tutorial: OpenCV, Calibration
Spatial Filtering
Histogram/Threshold
Morphology
Edge,Line,Corner Detection
Color Image Processing
Part 2
Inro. Deep Neural Network: MLP, Activation Function, Loss Function
Inro. Deep Neural Network: Back Propagation, CaseStudy(LeNet)
Intro. Convolutional Neural Network: Convolution, Evaluation Metric
Intro. Convolutional Neural Network:Strategy for CNN design, Case Study(AlexNet)
Popular CNN models: VGGNet, ResNet, Inception
Object Detection: from R-CNN to YOLO
Recent trend in CNN
Tutorial
Tutorial: OpenCV, Calibration
Tutorial: Image Spatial Filtering
Tutorial: Morphology
Tutorial: Edge & Line Detection
Tutorial: Color Image Processing
Tutorial: PyTorch
Tutorial: LeNet
Tutorial: AlexNet
Tutorial: AlexNet with Customized Dataset
Tutorial: YOLOv8
LAB
LAB: Object Segmentation in Grayscale Image
LAB: Object Segmentation in Color Image
LAB: Industrial Problem
LAB: CNN Object Detection (Vehicle /Pedestrian)
LAB: CNN Object Detection (Custom dataset, Free Topic)
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