EE · Spring 2026 · Undergraduate

Image Processing

From image formation and filtering to multiview geometry, visual recognition, and learning-based vision.

Course Overview

This course introduces the principles and algorithms that enable computers to process and understand visual information. It begins with image representation, filtering, and feature extraction, then develops the geometry of cameras and multiple views. The final part connects these foundations to image classification, neural networks, and convolutional models.

Instructor
Prof. Nuri Kim
Term
Spring 2026
Meetings
Mon 09:00–10:30
Wed 09:00–09:50
Classroom
Engineering Building 7
Room 114

Learning Objectives

Image processing and geometry

  • Represent, filter, and analyze digital images in spatial and frequency domains.
  • Detect corners and local features for matching visual observations.
  • Model cameras, transformations, homographies, and two-view geometry.
  • Recover depth and 3D structure from multiple images.

Recognition and learning

  • Build image-classification pipelines using local and learned features.
  • Explain neural networks and convolutional architectures for vision.
  • Connect classical image representations to neural-network-based recognition.
  • Implement and evaluate computer-vision methods in Python.

Instruction is in Korean, with an English-language textbook and technical terminology. Familiarity with Python and introductory linear algebra is recommended.

Schedule

Part I
Images & Features
W01Course IntroductionLMS
W02Python and NumPyA0
W03Image-Processing Packages and Image FilteringA1
W04Image Pyramids and Hough TransformLMS
W05Detecting CornersQuiz 1
W06Feature Detectors and DescriptorsA2 · Q2
Part II
Visual Geometry
W072D TransformationsQuiz 3
W08Image Homographies and Camera Models IQuiz 4
W09Midterm ExamExam
W10Camera Models II and GeometryA3 · Q5
W11StereoQuiz 6
Part III
Recognition & Learning
W12Image ClassificationQuiz 7
W13Neural NetworksA4
W14Neural NetworksLMS
W15Convolutional Neural NetworksA5
W16Final ExamExam

The schedule follows the Spring 2026 course plan. Slides, code, and detailed deadlines are available to enrolled students through the JBNU LMS.

Coursework

Assignment 0

Data Interpretation & Visualization

Explore numerical image data and build reliable visualizations.

Assignment 1

Filtering & Edge Detection

Implement convolution, image filtering, and edge extraction.

Assignment 2

Panorama Stitching

Detect and match features, estimate homographies, and warp images.

Assignment 3

3D Reconstruction

Estimate epipolar geometry and reconstruct structure from multiple views.

Assignment 4

Scene Recognition

Build a visual-words representation and evaluate scene classification.

Assignment 5

Neural Networks for Recognition

Implement and train neural models for visual recognition tasks.

Assignments
30%
Midterm Exam
20%
Final Exam
40%
Attendance
10%

Generative AI may be used creatively in course activities and assignments. Students remain responsible for checking, revising, and understanding all submitted work; unreviewed AI-generated submissions may be treated as academic misconduct.

Resources

  • Course platformJBNU LMS
  • Primary textbookRichard Szeliski, Computer Vision: Algorithms and Applications, 2nd ed.
  • ReferenceRichard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision
  • ReferenceDavid Forsyth and Jean Ponce, Computer Vision: A Modern Approach
  • ReferenceRafael Gonzalez and Richard Woods, Digital Image Processing
  • Contactnuri.kim@jbnu.ac.kr