2019 Image Sensing

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Undergraduate major in Systems and Control Engineering
Instructor(s)
Okutomi Masatoshi 
Course component(s)
Lecture
Day/Period(Room No.)
Tue5-6(S422)  Fri5-6(S422)  
Group
-
Course number
SCE.I301
Credits
2
Academic year
2019
Offered quarter
1Q
Syllabus updated
2019/3/18
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course will provide a comprehensive overview of fundamental image processing and image sensing. Topics covered in this course will include the following: imaging model, pixel-by-pixel image processing, area-based image processing, frequency-domain image processing, image restoration, geometric transform, pattern matching, feature extraction, feature description and matching, and pattern recognition.

Images can be regarded as high-dimensional signal and contain rich information. They are also very useful as external sensors and have a variety of applications. In this course, basic theories and algorithms which are essential for image processing and sensing will be explained.

Student learning outcomes

By the end of this course, students will be able to:
1. Understand how geometric information in space is recorded in an image and its mathematical modeling.
2. Understand pixel-by-pixel intensity transform and area-based intensity transform (spatial filtering).
3. Understand the relationship between spatial filtering and frequency filtering.
4. Understand mathematical descriptions of various geometric transforms, how to transform actual images, and image mosaicing as a typical application.
5. Understand methods for detecting specific patterns and figures, and image segmentation.
6. Understand methods for feature extraction and description, and pattern recognition.

Keywords

imaging model, spacial filtering, Fourier transform, frequency filtering, geometric transform, pattern detection, feature detection and matching, pattern recognition

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Basic knowledge, theories and algorithms required for image processing and sensing will be explained step by step, classifying diverse topics related to image processing with their characteristics.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Camera structure and imaging model Understanding the structure of digital cameras and the model of imagimg by cameras.
Class 2 Parameters in capturing images by a camera Understanding various camera parameters and their influence on the generated image.
Class 3 Image digitization and color space Understanding sampling theorem, the influence of sampling and quantization, the method to capture calor information, and color transform.
Class 4 Pixel-by-pixel intensity transform Understanding the intensity transform using a tone curve and its effect on the image.
Class 5 Area-based intensity transform (spacial filtering) Understanding various types of spacial filtering and their effects on the image.
Class 6 Non-linear filtering and Fourier transform of images Understanding non-linear filtering and their effects, and Fourier transform of images.
Class 7 Frequency filtering Understanding the relationship between spacial filtering and frequency filtering, and some types of frequency filtering.
Class 8 Image restoration and generation Understanding image degradation models, image restoration, and some other methods for image restoration/generation.
Class 9 Geometric transform Understanding the mathematical description of various geometric transforms.
Class 10 Geometric transform of images and image mosaicing Understanding the method of geometric transform of an image and image mosaicing as its typical application.
Class 11 Binary image processing Understanding image binarization and binary image processing
Class 12 Image segmentation Understanding the methods for image segmentation
Class 13 Detection of patterns and Figures Understanding pattern detection methods including template matching and figure detection by Hough transform.
Class 14 Feature detection and matching Understanding detection and description of features, and feature matching.
Class 15 Pattern recognition Understanding fundamentals of pattern recognition and some typical recognition methods.

Textbook(s)

Digital Image Processing: Computer Graphics Arts Society (CG-ARTS)

Reference books, course materials, etc.

None required

Assessment criteria and methods

The level of understanding about the contents presented in the course and the ability to apply them to problems will be assessed.

Related courses

  • SCE.I201 : Introduction to Measurement Engineering
  • SCE.I202 : Random Signal Processing
  • SCE.I203 : Digital Signal Processing

Prerequisites (i.e., required knowledge, skills, courses, etc.)

no prior conditions

Page Top