In this course we will analyze the paradigm of automatic object recognition from a Computer Vision and Machine Learning points of view. We will review past and recent challenges in object recognition, such as multi-modal, multi-part, multi-scale, multi-view, multi-class, multi-label, and large scale object recognition, including recent deep learning architectures. We will also review current trends for a particular and complex kind of objects: people in visual data'. We will deal with the problem of human pose recovery and automatic behavior analysis, describing potential applications as well as future lines of research in the field.
Person in charge
Simone Balocco (
Sergio Escalera (
Generic Technical Competences
CG2 - Capability to lead, plan and supervise multidisciplinary teams.
Technical Competences of each Specialization
CEA6 - Capability to understand the basic operation principles of Computational Vision main techniques, and to know how to use in the environment of an intelligent system or service.
CEA14 - Capability to understand the advanced techniques of Vision, Perception and Robotics, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
CEP6 - Capability to assimilate and integrate the changing economic, social and technological environment to the objectives and procedures of informatic work in intelligent systems.
CEP8 - Capability to respect the surrounding environment and design and develop sustainable intelligent systems.
Appropiate attitude towards work
CT5 - Capability to be motivated for professional development, to meet new challenges and for continuous improvement. Capability to work in situations with lack of information.
CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
Introduction to object and human recognition
Multi-modal object recognition
Multi-part object recognition
Multi-scale object recognition
Multi-view object recognition
Multi-class object recognition
Multi-label object recognition
Multi-ple data: deep-learning for large scale object recognition
Object Recognition in context: scene understanding and grammars
Human Pose Recovery
Human Behavior Analysis
T Each week it will be a 1.5h theoretical topic exposition class.
P Each week it will be a 1h practical session.
The rest of the course are devoted to autonomous lectures, programming, and studying.
The course will follow a continuous evaluation consisting in four practical reports (PR) and two in-class presentations (PS). At the end of the course a test exam will be performed (TS). The final score (FS) will be computed as follows:
FS = 0.5 * PR + 0.3 * PS + 0.2 * TS
A minimum score of 3 over 10 points is required for each part PR, PS, and TS in order to compute the final score FS.
Computer Vision: A Modern Approach -
David A. Forsyth, Jean Ponce, ,
Human Behavior Analysis from Depth Maps -
Sergio Escalera, AMDO ,
Object detection with discriminatively trained part-based models -
P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, PAMI ,
2010, vol. 32, num. 9.
Multi-scale stacked sequential learning -
C. Gatta, E. Puertas, and O. Pujol, Pattern Recognition ,
2011, vol. 44, issue 10-11, pp. 2414-2426.
Multiple View Geometry Vision -
R. Hartley and A. Zisserman, ,
On the Decoding Process in Ternary Error-Correcting Output Codes -
Sergio Escalera, Oriol Pujol, and Petia Radeva, IEEE PAMI ,
2010, vol. 32, issue 1, pp. 120-134.
ISBN: 0162-8828 http://cataleg.upc.edu/record=b1000619~S1*cat
Subclass Problem-dependent Design of Error-Correcting Output Codes -
Sergio Escalera, David Tax, Oriol Pujol, Petia Radeva, and Robert Duin, IEEE Transactions in Pattern Analysis and Machine Intelligence ,
2008, vol. 30, issue 6, pp. 1041-1054.
An extensive experimental comparison of methods for multi-label learning -
Gjorgji Madjarov, Dragi Kocev, Author Vitae, Dejan Gjorgjevikj, Sao Deroski, Pattern Recognition ,
Sub-linear Indexing for Large Scale Object Recognitiom -
Stepan Obdrzalek and Jiri Matas, BMVC ,
80 million tiny images: a large dataset for non-parametric object and scene recognition -
A. Torralba, R. Fergus, W. T. Freeman, PAMI ,
The role of context in object recognition -
Oliva, A. Torralba, Trends in Cognitive Sciences ,
Objects in Context -
Rabinovich, A. Vedaldi, C. Galleguillos, E. Wiewiora and S. Belongie, ICCV ,
A Stochastic Grammar of Images -
S.C. Zhu and D. Mumford, Foundations and Trends in Computer Graphics and Vision ,
Articulated pose estimation with flexible mixtures-of-parts -
Y. Yang, D. Ramanan, Computer Vision and Pattern Recognition (CVPR) ,
2011, pp. 13851392.
Real-time American Sign Language Recognition using desk and wearable computer based video -
T. Starner, J. Weaver, and A. Pentland, IEEE TPAMI ,
1998, vol. 20, issue 1, pp. 1371-1375.
A tutorial on Hidden Markov Models and selected applications -
L. Rabiner, IEEE Speech recognition ,
1989, vol.2, pp.257-286.
A probabilistic framework for matching temporal trajectories: Condensation-based recognition of gestures and expressions -
M. Black and A. Jepson, LNCS ,
1998, vol. 1406, pp. 909-924.
Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM -
S. Calinon, A. Billard, ICML ,
Deep Learning -
Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press ,
Deep learning -
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton, Nature ,
521 no. 7553 (2015): 436-444.
Where we are
B6 Building Campus Nord
C/Jordi Girona Salgado,1-3
08034 BARCELONA Spain
Tel: (+34) 93 401 70 00