• Description

The Pattern Recognition course will introduce the student to the techniques for extracting information from data. In particular the course focuses on the recognition of patterns within the context of computer vision. Images are one of the main sources of information used by the human brain at the perceptual level to make decisions. As a consequence, the recognition of patterns within the context of computer vision is of great interest, especially nowadays that we have at our disposal a huge amount of visual data that can not be analyzed by hand. Practical applications of pattern recognition in the context of artificial vision are many. For example safety, medicine, automatic inspection, or automatic navigation.

• Prior knowledge

• Requirements: Ability to read scientific texts in English. Basic statistical knowledge (undergraduate or engineering level).
• Bibliography (course material): Richard Szeliski, 2022. "Computer Vision: Algorithms and Applications", Second Edition.

• Information prior to enrolment

• Coordinator: Dr. Carles Ventura
• Credits: 6
• Description: In the present course the student will be introduced in the set of techniques that allow us to extract information from a set of data. As an application, the subject will focus on extracting high level information from the environment, from information captured by cameras. In this case the objective is to learn to recognize objects in real environments and completely automatically. Images are one of the most important sources of information that the human brain uses perceptually to make decisions. In this context, different samples of the same object have in common a series of patterns, which must be detected, modeled and later classified for recognition. The course aims to train the student to know the probabilistic techniques and mathematical optimization related to pattern recognition.
• Requirements: ability to read scientific texts in English. Basic statistical knowledge (undergraduate or engineering level).
• Bibliography: UOC modules

• Learning objectives and results

This course is oriented to the study of fundamental concepts of computer vision, pattern recognition and other advanced topics related to the problems of analysis and automatic recognition of complex images. In particular, the learning objectives are as follows:
 
- To know how the images are formed.
- To know the main techniques of digital image processing.
- To understand how color is perceived and know the spaces of color representation.
- To know the main techniques of dimensionality reduction (selection and extraction of characteristics), both supervised and unsupervised, and know how to apply to real problems.
- To know the main techniques of automatic learning for automatic classification of data and know how to apply to real problems.

The specific competences that are covered in this course are the following:
 
A1. Ability to understand and be able to apply advanced computer knowledge and numerical or computational methods to engineering problems.

A2. Ability to apply computational, mathematical and statistical methods to model, design and develop applications, services, intelligent systems and / or systems based on knowledge.

A3. Ability to apply mathematical and computational methods to solving technological and engineering problems, particularly in research, development and innovation.

A4. Ability to model problems using a mathematical language and solve them through formal reasoning.

A5. Ability to identify mathematical theories necessary for the construction of models from problems of other disciplines.

A6. Ability to handle mathematical and statistical software.

A9. Ability to analyze and process data to generate and manage useful information in decision making.

A10. Ability to design, implement and validate algorithms using the most convenient structures.

• Content

Unit 1. Introduction to computer vision. A brief history of computer vision. Color spaces.

Unit 2. Image processing. Linear filters and non linear filters. Morphological operations.

Unit 3. Feature extraction. Feature detection and description. Feature matching. Content-based image retrieval. Segmentation.

Unit 4. Machine Learning. Supervised and unsupervised learning. Deep Neural Networks. Convolutional Neural Networks.

Unit 5. Image Classification. Feature-based methods. Convolutional Neural Networks based methods.

Unit 6. Object detection. Face detection. Person detection. Object detection.

Unit 7. Semantic segmentation. Instance segmentation. Panoptic segmentation. Pose estimation.

Unit 8. Video understanding. Action recognition. Optical flow. Object tracking. Video object segmentation.

Tema 9. Generative models. Autoencoders. Variational autoencoders. Generative Adversarial Networks (GANs).

• View the UOC learning resources used in the subject

• Additional information on support tools and learning resources

For the practical exercises, the resources than can be found on the following code repository will be used:

https://gitlab.uoclabs.uoc.es/patternrecognition/pattern-recognition/-/tree/master

• Guidelines on assessment at the UOC

The assessment process is based on the student's personal work and presupposes authenticity of authorship and originality of the exercises completed.

Lack of authenticity of authorship or originality of assessment tests, copying or plagiarism, the fraudulent attempt to obtain a better academic result, collusion to copy or concealing or abetting copying, use of unauthorized material or devices during assessment, inter alia, are offences that may lead to serious academic or other sanctions.

Firstly, you will fail the course (D/0) if you commit any of these offences when completing activities defined as assessable in the course plan, including the final tests. Offences considered to be misconduct include, among others, the use of unauthorized material or devices during the tests, such as social media or internet search engines, or the copying of text from external sources (internet, class notes, books, articles, other students' essays or tests, etc.) without including the corresponding reference.

And secondly, the UOC's academic regulations state that any misconduct during assessment, in addition to leading to the student failing the course, may also lead to disciplinary procedures and sanctions.

The UOC reserves the right to request that students identify themselves and/or provide evidence of the authorship of their work, throughout the assessment process, and by the means the UOC specifies (synchronous or asynchronous). For this purpose, the UOC may require students to use a microphone, webcam or other devices during the assessment process, and to make sure that they are working correctly.

The checking of students' knowledge to verify authorship of their work will under no circumstances constitute a second assessment.

• View the assessment model