2022 Image Sensing

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Academic unit or major
Undergraduate major in Systems and Control Engineering
Okutomi Masatoshi 
Class Format
Lecture    (Livestream)
Media-enhanced courses
Day/Period(Room No.)
Tue5-6(W521)  Fri5-6(W521)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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.

Course taught by instructors with work experience

Applicable How instructors' work experience benefits the course
In this class, not only the theoretical aspects but also the actual applications will be explained by a teacher who used to be involved in industry.


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

Competencies that will be developed

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

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.


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

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