|
||||||||||||||||||||||||||||||
View general information Description Prior knowledge Information prior to enrolment Learning objectives and results Content View the UOC learning resources used in the subject Additional information on support tools and learning resources Guidelines on assessment at the UOC View the assessment model | ||||||||||||||||||||||||||||||
This is the course plan for the first semester of the academic year 2024/2025. To check whether the course is being run this semester, go to the Virtual Campus section More UOC / The University / Programmes of study section on Campus. Once teaching starts, you'll be able to find it in the classroom. The course plan may be subject to change. | ||||||||||||||||||||||||||||||
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. |
||||||||||||||||||||||||||||||
|
||||||||||||||||||||||||||||||
|
||||||||||||||||||||||||||||||
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: The specific competences that are covered in this course are the following: 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. |
||||||||||||||||||||||||||||||
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). |
||||||||||||||||||||||||||||||
|
||||||||||||||||||||||||||||||
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 |
||||||||||||||||||||||||||||||
Assessment at the UOC is, in general, online, structured around the continuous assessment activities, the final assessment tests and exams, and the programme's final project. Assessment activities and tests can be written texts and/or video recordings, use random questions, and synchronous or asynchronous oral tests, etc., as decided by each teaching team. The final project marks the end of the learning process and consists of an original and tutored piece of work to demonstrate that students have acquired the competencies worked on during the programme. To verify students' identity and authorship in the assessment tests, the UOC reserves the right to use identity recognition and plagiarism detection systems. For these purposes, the UOC may make video recordings or use supervision methods or techniques while students carry out any of their academic activities. The UOC may also require students to use electronic devices (microphones, webcams or other tools) or specific software during assessments. It is the student's responsibility to ensure that these devices work properly. The assessment process is based on students' individual efforts, and the assumption that the student is the author of the work submitted for academic activities and that this work is original. The UOC's website on academic integrity and plagiarism has more information on this. Submitting work that is not one's own or not original for assessment tests; copying or plagiarism; impersonation; accepting or obtaining any assignments, whether for compensation or otherwise; collaboration, cover-up or encouragement to copy; and using materials, software or devices not authorized in the course plan or instructions for the activity, including artificial intelligence and machine translation, among others, are examples of misconduct in assessments that may have serious academic and disciplinary consequences. If students are found to be engaging in any such misconduct, they may receive a Fail (D/0) for the graded activities in the course plan (including final tests) or for the final grade for the course. This could be because they have used unauthorized materials, software or devices (such as artificial intelligence when it is not permitted, social media or internet search engines) during the tests; copied fragments of text from an external source (the internet, notes, books, articles, other students' work or tests, etc.) without the corresponding citation; purchased or sold assignments, or undertaken any other form of misconduct. Likewise and in accordance with the UOC's academic regulations, misconduct during assessment may also be grounds for disciplinary proceedings and, where appropriate, the corresponding disciplinary measures, as established in the regulations governing the UOC community (Normativa de convivència). In its assessment process, the UOC reserves the right to:
Artificial intelligence in assessments The UOC understands the value and potential of artificial intelligence (AI) in education, but it also understands the risks involved if it is not used ethically, critically and responsibly. So, in each assessment activity, students will be told which AI tools and resources can be used and under what conditions. In turn, students must agree to follow the guidelines set by the UOC when it comes to completing the assessment activities and citing the tools used. Specifically, they must identify any texts or images generated by AI systems and they must not present them as their own work. In terms of using AI, or not, to complete an activity, the instructions for assessment activities indicate the restrictions on the use of these tools. Bear in mind that using them inappropriately, such as using them in activities where they are not allowed or not citing them in activities where they are, may be considered misconduct. If in doubt, we recommend getting in touch with the course instructor and asking them before you submit your work. |
||||||||||||||||||||||||||||||
|