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)

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