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Computer Vision

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Computer Vision

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Academic year 2023/2024

Course ID
ONC0257
Teachers
Marco Grangetto (Lecturer)
Attilio Fiandrotti (Lecturer)
Davide Cavagnino (Assistant)
Year
1st year
Teaching period
Second semester
Type
Distinctive
Credits/Recognition
6
Course disciplinary sector (SSD)
INF/01 - informatics
Delivery
Formal authority
Language
English
Attendance
Obligatory
Type of examination
Oral
Prerequisites
Lessons require the knowledge of vector calculus, matrix calculus, and analytic techniques. The experimental part requires computer programming skills.
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Sommario del corso

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Course objectives

The educational objectives are to provide mainly theoretical skills in the field of image processing. The course provides the methodological foundations in the field of natural and medical image processing in the spatial and frequency domains. Such methods enable the development of systems capable of improving and restoring image quality, compressing lossy and lossless images, and solving computer vision problems such as classification, segmentation, and image generation.

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Results of learning outcomes

Upon completion of the course, the student is able to select and parameterize algorithms suitable for improving image quality in the spatial and frequency domains, lossy and lossless image compression, and solving computer vision problems such as classification, segmentation, and image generation.

KNOWLEDGE AND UNDERSTANDING SKILLS. Acquisition of methodologies of selection and parameterization of natural and medical image processing algorithms.

ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING. Acquisition of theoretical for natural and medical image processing.

SELF-JUDGMENT. Acquisition of the basic criteria for determining how to set up proper algorithm design for natural and medical image analysis.

COMMUNICATION SKILLS. Acquisition of technical terminology in the area of natural and medical image processing.

LEARNING SKILLS. Development of autonomous learning skills for future training in the field.

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Program

This course covers the fundamentals of natural and medical image analysis and processing. The course is devoted to both the study of theoretical aspects and the scaling and application of algorithms for image analysis. The course program comprises the following topics.

 

Introduction.

  • Definition and examples of areas of use of image processing. 
  • Fundamental steps in image processing.
  • Components of an image processing system.
  • Light and the electromagnetic spectrum.
  • Image acquisition and representation.
  • Spatial and radiometric resolution.
  • An overview of mathematical tools used in image processing.
  • Compression formats for images and video.

Digital image processing in the spatial domain

  • Brightness transformations and spatial filtering with related examples.
  • Histogram processing and matching.
  • Local histogram processing.
  • Fundamentals of spatial filtering.
  • Spatial smoothing and sharpening filters.
  • Combination of space-based enhancement methods.

Digital image processing in the frequency domain.

  • The Fourier transform of functions of a continuous variable.
  • Sampling and the Fourier transform of sampled functions.
  • Extension to functions of two variables.
  • The 2D discrete Fourier transform and some of its properties.
  • Fundamentals of filtering in the frequency domain.
  • Smoothing and sharpening of images using filters in the frequency domain.

Image enhancement and restroration

  • Periodic noise reduction using frequency domain filtering.
  • Linear and non-position-dependent degradation.
  • Inverse filtering. Reconstruction of images from projections.

Processing of color images.

  • Color fundamentals.
  • Processing of pseudo-color images.
  • Transformations of colors.
  • Segmentation of color images.

Low level vision

  • Recognition of points, lines and boundaries of regions.
  • Canny's edge detector.
  • Thresholding.
  • SIFT descriptors.

Advanced computer vision.

  • Introduction to computer vision with statistical methods and neural networks.
  • Discriminative and generative models, mixture of Gaussians, Factor Analysis.
  • Fully connected artificial neural networks.
  • Convolutional and fully convolutional networks for image classification.
  • Deep architectures for image classification and related training methods.
  • Neural networks for natural and medical image segmentation.
  • Neural networks for localization and classification of objects in natural and medical images.
  • Generative models.
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Course delivery

Lectures take place both in traditional mode and through the use of multimedia tools. The student is invited tointeract with the lecturer, and some experimental activities are aimed at deepening understanding of the topicspresented in class.

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Learning assessment methods

Oral interview on topics covered in the course. The goal of the exam is to assess the student's capability to approach an image-processing task with the correct technical language and tools.

Suggested readings and bibliography



Oggetto:
Book
Title:  
Digital Image Processing, fourth ed.
Year of publication:  
2017
Publisher:  
Pearson Education
Author:  
R. C. Gonzalez, R. E. Woods
Permalink:  
Required:  
No


Oggetto:
Book
Title:  
Pattern recognition and machine learning
Year of publication:  
2006
Publisher:  
Springer
Author:  
Bishop, Christopher M.
Required:  
No


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