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

일자	주차	Day	Lecture	Tutorial	LAB	과제 공지
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)