Natural images contain many effects caused by the interaction between light and the objects in the scene. These effects include, amongst others, shading, shadows, and pecularities. In general, computer vision algorithms are hindered by such effects.
Identifying and isolating these effects and also other physical elements of the scene which participate in the generation of these effects, such as 3D shape, depth and the color of the light source, can be very useful to improve the performance of many algorithms. However, the estimation of these intrinsic properties from a single image is a challenging problem which has received much attention in the last years.
This project aims at the definition of deep network architectures that allows to decompose images into some of their photometric properties such as reflectance, lighting, shading (including self-shadows and cast shadows) or peculiarities jointly with the corresponding 3D shape properties
The fellow is expected to contribute in the current lines of research of the hosting group, and do research tasks as:
- Reviewing previous works on the topics of the project (i.e. intrinsic image estimation and deep learning).
- Designing Convolutional Neural Networks to learn the best features based on the large dataset of lighting effects that the hosting group is building.
- Evaluating the defined models by performing experiments on standard datasets and/or specific datasets of intrinsic properties.
PROJECT SUPERVISOR & HOSTING GROUP
Prof. Maria Vanrell will supervise the Fellow with the support of the hosting team (Color in Context: www.cic.uab.cat), which is widely recognized for the contribution in color representation for Computer Vision.
The hosting group is collaborating with a number of institutions world-wide. The research fellow will have the opportunity to participate actively in these collaborations, including through research stays. The activities of the team are supported by multiple research and technology transfer projects.
CANDIDATE ’S PROFILE
A PhD in computer science or a related field is required.
The applicants must have experience in computer vision, pattern recognition and machine learning techniques, and be able to demonstrate strong analytical and programming skills.
the applicants are expected to be fluent in both oral and written communication in English. They should work well in a team, while demonstrating initiative and independence, and willing to supervise PhD students.
THE COMPUTER VISION CENTER
The selected candidate will work in the Computer Vision Centre (CVC), Barcelona, a research institute comprising more than 100 researchers and support staff, dedicated to computer vision research and knowledge transfer. With a strong international projection and links to the industry, the Computer Vision Centre offers an exciting environment for scientific career development.
The Computer Vision Centre has a plan for expansion of its permanent research staff base, and has received the “HR Excellence in Research” award as a provider and supporter of a stimulating and favourable working environment.
(+34) 93 581 24 15
Conditions, deadlines and applications at www.uab.cat/psphere.
Call open from 9th of September until 9th of December.
OTHER P-SPHERE POSITIONS AT CVC
- HUMAN POSE RECOVERY AND BEHAVIOR ANALYSIS
- PERCEPTION BASED SELF DRIVING SYSTEM FOR URBAN SCENARIOS
- DEEP LEARNING FOR MULTI-MODAL DATA REPRESENTATIONS
- SCENE TEXT UNDERSTANDING
- INFORMATION EXTRACTION FROM HISTORICAL DOCUMENT IMAGES