Course Title:
 DIGITAL IMAGE PROCESSING AND ANALYSIS
Code:
 BME-A20
Semester: 2nd
Weekly teaching hours CREDITS (ECTS)
Lecture: 2 5

SYLLABUS
Theoretical classes: Introductory concepts for Image Processing & Analysis and their applications. Basic elements of 2-D signal processing and image transforms. Image acquisition systems and different types of degradation. Image enhancement methods. Image restoration methods. Techniques for lossless and lossy image compression. Elements of color theory and color image processing basics. Reconstruction of 3D objects based on 2D projections. Edge detection and linking. Image segmentation. Shape description and representation. Object recognition. Basic structure of an image analysis system. Basic principles of machine learning for image processing & analysis. Elements of deep neural networks (DNN) theory and architectures. Emphasis on DNN architectures suitable for image processing & analysis.

Practical classes: Design and implement algorithms that perform basic image processing (e.g., noise removal, image enhancement); Design and implement algorithms for advanced image analysis (e.g., image compression, image segmentation & image representation).

Learning outcomes
The learning outcomes for the course “Digital Image Processing and Analysis”:

  • Understanding of Image Processing Concepts: Gain a solid understanding of fundamental concepts, methodologies, and algorithms in digital image processing and analysis.
  • Image Enhancement: Acquire knowledge and skills in techniques for enhancing digital images to improve their visual quality and interpretability.
  • Image Restoration: Develop the ability to restore degraded digital images using appropriate restoration algorithms, reducing noise and artifacts for better image quality.
  • Image Analysis: Learn to analyze and interpret digital images using various image analysis techniques, such as edge detection, segmentation, and shape description.
  • Object Recognition: Gain familiarity with object recognition methods and algorithms to identify and classify objects of interest within digital images.
  • Image Compression: Understand the principles and techniques of image compression for efficient storage and transmission of digital images, including lossless and lossy compression algorithms.
  • Color Image Processing: Acquire knowledge of color theory and techniques for processing color images, including color enhancement, color correction, and color-based analysis.
  • 3D Reconstruction: Learn about the principles and algorithms for reconstructing three-dimensional objects from two-dimensional projections, enabling visualization and analysis of 3D structures.
  • Machine Learning in Image Processing: Understand the basic principles of machine learning applied to image processing and analysis, including the use of deep neural networks for tasks such as image classification and segmentation.

General Competences
  • Analytical Skills: Acquire the ability to analyze and interpret digital images, extract meaningful information, and make informed decisions based on the results.
  • Creativity: Foster creativity in finding innovative solutions and approaches for image processing and analysis tasks, considering unique requirements and constraints.
  • Attention to Detail: Develop meticulousness in processing and analyzing digital images, paying attention to details and ensuring accuracy in results.
  • Communication Skills: Effectively communicate technical concepts and findings related to digital image processing and analysis to both technical and non-technical audiences.
  • Collaboration and Teamwork: Work effectively in teams, collaborating with peers to solve complex problems and achieve common objectives in digital image processing projects.
  • Ethical Awareness: Understand and adhere to ethical guidelines and principles when working with digital images, ensuring privacy, data integrity, and responsible use of technology.