Computer Vision

• Teachers
• Weekly hours
• Competences
• Objectives
• Contents
• Activities
• Teaching methodology
• Evaluation methodology
• Bibliography
• Previous capacities

Teachers

Person in charge

• Joan Climent Vilaró ( )

Others

• Isiah Zaplana Agut ( )

Weekly hours

Competences

Transversal Competences

Transversals

• CT6 [Avaluable] - Autonomous Learning. Detect deficiencies in one's own knowledge and overcome them through critical reflection and the choice of the best action to extend this knowledge.

Basic

• CB1 - That students have demonstrated to possess and understand knowledge in an area of ??study that starts from the base of general secondary education, and is usually found at a level that, although supported by advanced textbooks, also includes some aspects that imply Knowledge from the vanguard of their field of study.
• CB2 - That the students know how to apply their knowledge to their work or vocation in a professional way and possess the skills that are usually demonstrated through the elaboration and defense of arguments and problem solving within their area of ??study.
• CB5 - That the students have developed those learning skills necessary to undertake later studies with a high degree of autonomy

Technical Competences

Especifics

• CE01 - To be able to solve the mathematical problems that may arise in the field of artificial intelligence. Apply knowledge from: algebra, differential and integral calculus and numerical methods; statistics and optimization.
• CE02 - To master the basic concepts of discrete mathematics, logic, algorithmic and computational complexity, and its application to the automatic processing of information through computer systems . To be able to apply all these for solving problems.
• CE03 - To identify and apply the basic algorithmic procedures of computer technologies to design solutions to problems by analyzing the suitability and complexity of the proposed algorithms.
• CE04 - To design and use efficiently the most appropriate data types and structures to solve a problem.
• CE13 - To evaluate the computational complexity of a problem, identify algorithmic strategies that can lead to its resolution and recommend, develop and implement the one that guarantees the best performance in accordance with the established requirements.
• CE14 - To master the foundations, paradigms and techniques of intelligent systems and to analyze, designing and build computer systems, services and applications that use these techniques in any field of application, including robotics.
• CE15 - To acquire, formalize and represent human knowledge in a computable form for solving problems through a computer system in any field of application, particularly those related to aspects of computing, perception and performance in intelligent environments or environments.
• CE18 - To acquire and develop computational learning techniques and to design and implement applications and systems that use them, including those dedicated to the automatic extraction of information and knowledge from large volumes of data.
• CE19 - To use current computer systems, including high-performance systems, for the processing of large volumes of data from the knowledge of its structure, operation and particularities.
• CE26 - To design and apply techniques for processing and analyzing images and computer vision techniques in the area of artificial intelligence and robotics

Generic Technical Competences

Generic

• CG2 - To use the fundamental knowledge and solid work methodologies acquired during the studies to adapt to the new technological scenarios of the future.
• CG4 - Reasoning, analyzing reality and designing algorithms and formulations that model it. To identify problems and construct valid algorithmic or mathematical solutions, eventually new, integrating the necessary multidisciplinary knowledge, evaluating different alternatives with a critical spirit, justifying the decisions taken, interpreting and synthesizing the results in the context of the application domain and establishing methodological generalizations based on specific applications.
• CG6 - To identify opportunities for innovative applications of artificial intelligence and robotics in constantly evolving technological environments.
• CG8 - Perform an ethical exercise of the profession in all its facets, applying ethical criteria in the design of systems, algorithms, experiments, use of data, in accordance with the ethical systems recommended by national and international organizations, with special emphasis on security, robustness , privacy, transparency, traceability, prevention of bias (race, gender, religion, territory, etc.) and respect for human rights.
• CG9 - To face new challenges with a broad vision of the possibilities of a professional career in the field of Artificial Intelligence. Develop the activity applying quality criteria and continuous improvement, and act rigorously in professional development. Adapt to organizational or technological changes. Work in situations of lack of information and / or with time and / or resource restrictions.

Objectives

1. Define and quantify the characteristics of an image
   Related competences: CB1, CE04,
2. Compare and select the most appropriate image processing tools based on the problem to be solved.
   Related competences: CE26, CB1, CE01, CE02, CE03, CE04, CE15, CE19,
3. To segment and label image regions
   Related competences: CE26, CG2, CG4, CB5, CE01, CE03, CE04, CE13, CE15,
4. Find the most significant descriptors to characterize regions or points of interest of an object
   Related competences: CE26, CG2, CG4, CG8, CG9, CT6, CB5, CE14, CE15, CE18,
5. Detect and recognize the presence of certain items in an image
   Related competences: CE26, CG2, CG4, CG6, CG8, CG9, CT6, CE14, CE15, CE18, CE19,
6. Correctly carry out experiments to evaluate the proposed methods, their limitations and weak points, based on objective results.
   Related competences: CG2, CG4, CG8, CB2, CE01, CE02, CE03, CE14,

Contents

1. Fundamentals of digital imaging
   The digital image, properties and characteristics. Discretization and quantification. Color spaces
2. Image processing
   Lineal processing. Image filtering. Morphological image processing. Scales space
3. Image segmentation
   Edge detection. Clustering. Morphological segmentation
4. Descriptors
   Feature space. Shape descriptors. Appearance based descriptors. Keypoint detection
5. Recognition
   Local matching. Global matching. Object models. distance measures and error quantification
6. Deep learning based solutions
   Categories recognition. Semantic segmentation. Interpreting CNNs. Architectures

Activities

Activity Evaluation act

Teaching methodology

Evaluation methodology

Bibliography

Basic:

• Concise computer vision: an introduction into theory and algorithms - Klette, R, Springer, 2014. ISBN: 1447163192
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma99100516467790671&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Computer vision: algorithms and applications - Szeliski, R, Springer Nature Switzerland, 2022. ISBN: 9783030343712
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991005130575906711&context=L&vid=34CSUC_UPC:VU1&lang=ca

Previous capacities