Announcements

  1. (Feb 7) Course page is online.
  2. (Feb 7) Syllabus is available.
  3. (Feb 7) Slides for introduction are available.
  4. (Feb 13) Slides for digital image fundamentals are available.
  5. (Feb 16) Slides for binary image analysis are available.
  6. (Feb 23) Homework assignment 1 is available.
  7. (Feb 28) Slides for linear filtering are available.
  8. (Mar 6) Slides for edge detection are available.
  9. (Mar 13) Slides for local feature detectors are available.
  10. (Mar 19) Homework assignment 2 is available.
  11. (Mar 19) Slides for color image processing are available.
  12. (Mar 19) Slides for texture analysis are available.
  13. (Mar 27) Slides for image segmentation are available.
  14. (Apr 2) Slides for representation and description are available.
  15. (Apr 10) Slides for pattern recognition are available.
  16. (Apr 12) Homework assignment 3 is available.
  17. (May 5) Project description is available.
  18. (May 7) Slides for image classification and object recognition are available.
  19. (May 7) Slides for image retrieval are available.

Personnel

Instructor: Selim Aksoy (Office: EA 422, Email: )
TA: Hüseyin Gökhan Akçay (Office: EA 130, Email: akcay[at]cs.bilkent.edu.tr)

Course Information

Schedule: Tue 13:40-15:30, Thu 15:40-17:30 (EB 104)
Office hours: Selim Aksoy (TBD)
Catalog description: Image acquisition, sampling and quantization. Spatial domain processing. Image enhancement. Texture analysis. Edge detection. Frequency domain processing. Color image processing. Mathematical morphology. Image segmentation and region representations. Statistical and structural scene descriptions. Applications.
Course emphasis and goals: This course provides an introduction to image analysis and computer vision for undergraduates. We will start with low-level vision (early processing) techniques such as binary image analysis, filtering, edge detection and texture analysis. Then, we will cover mid-level vision topics such as image segmentation and feature extraction in detail. Finally, we will do case studies on several applications such as image retrieval, image classification, and object recognition. The emphasis will be on feature extraction and image representations for recognition.
Prerequisites: Good programming background, data structures, linear algebra, vector calculus, basics of signal processing. No prior knowledge of image processing or computer vision is assumed.
Syllabus: Make sure you read the syllabus for course details.

Texts

Lecture Schedule

Chapters

Contents

Introduction

[ Slides: pps | pdf ]

(Feb 7, 9)

Topics:
  • Overview
  • Example applications

Digital Image Fundamentals

[ Slides: pps | pdf ]

(Feb 14, 16)

Topics:
  • Acquisition, sampling, quantization
  • Image enhancement
  • Image formats
  • Linear algebra and MATLAB review
Readings:
  • SS Ch 1, 2
  • GW Ch 1, 2, 3.1-3.4
References:
Software:

Binary Image Analysis

[ Slides: pps | pdf ]

(Feb 21, 23)

Topics:
  • Pixels and neighborhoods
  • Mathematical morphology
  • Connected components analysis
  • Automatic thresholding
Readings:
  • SS Ch 3.1-3.5, 3.8
  • GW Ch 2.5, 9.1-9.5, 10.3
References:
Software:

Linear Filtering

[ Slides: Part 1 (pps | pdf) | Part 2 (pps | pdf) ]

(Feb 28, Mar 1)

Topics:
  • Spatial domain filtering
  • Frequency domain filtering
  • Image enhancement
Readings:
  • SS Ch 5.1-5.5, 5.10-5.11
  • GW Ch 3.5-3.8, 4
Software:

Edge Detection

[ Slides: pps | pdf ]

(Mar 6, 8)

Topics:
  • Edges, lines, arcs
  • Hough transform
Readings:
  • SS Ch 5.6-5.8, 10.3-10.4
  • GW Ch 10.1-10.2
References:
Software:

Local Feature Detectors

[ Slides: pps | pdf ]

(Mar 13, 15)

Topics:
  • Corners and other interest points
  • Invariants
References:
Software:

Color Image Processing

[ Slides: pps | pdf ]

(Mar 20)

Topics:
  • Color spaces and conversions
Readings:
  • SS Ch 6.1-6.5
  • GW Ch 6

Texture Analysis

[ Slides: pps | pdf ]

(Mar 22, 27)

Topics:
  • Statistical approaches
  • Structural approaches
Readings:
  • SS Ch 7
  • GW Sec 11.3.3

Image Segmentation

[ Slides: pps | pdf ]

(Mar 29, Apr 3)

Topics:
  • Histogram-based approaches
  • Clustering-based approaches
  • Region growing
  • Split-and-merge
  • Morphological approaches
  • Graph-based approaches
Readings:
  • SS Ch 10.1
  • GW Ch 10.4-10.5
References:
Software:

Representation and Description

[ Slides: pps | pdf ]

(Apr 5, 10)

Topics:
  • Image representations and descriptors
  • Region representations and descriptors
Readings:
  • SS Ch 10.2, 3.7
  • GW Ch 11
References:

Pattern Recognition Overview

[ Slides: Part 1 | Part 2 ]

(Apr 12, 17)

Topics:
  • Brief introduction to pattern recognition
Readings:
  • SS Ch 4
  • GW Ch 12.1-12.2
References:
Software:

Case Studies

[ Slides: Part 1 (pps | pdf) ] | Part 2 (pps | pdf) ]

(May 3, 8, 10, 15)

Topics:
  • Image retrieval
  • Image classification
  • Object recognition
References:

Exams

Homework

  1. Homework assignment 1: description | data (Due: March 14, 2012 as online submission)
  2. Homework assignment 2: description | software (Due: April 8, 2012 as online submission)
  3. Homework assignment 3: description | data (Due: May 2, 2012 as online submission)

Please make sure you fully understand the late submission policy and the honor code for assignments in the syllabus. Cheating and plagiarism on homework assignments will be punished according to the regulations of the University as described in the Bilkent University Policy on Academic Honesty / Öğrenci Disiplin İlke ve Kuralları.

Project

The goal of the project is to develop an object recognition system that finds matches of known objects in new images.

Grading Policy

Homework:35%
Quiz:10%
Exam:25%
Project:25%
Class participation:5%

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