Objective
For the interpretation of visual information the recognition of objects is crucial. Object recognition is complicated by a variety of photometric variations, including changes of shadows, shading, specularities and illuminant colour. For object recognition to be successful in real-world applications it is essential that robustness with respect to these photometric variations is obtained. This will prove important in many application fields such as surveillance, robotics, defence, manufacturing industry, and image and video search engines.
Although the greater part of image data is in colour format nowadays, most object recognition systems are still based on luminance alone. Robustness with respect to undesired photometric variations can be greatly improved by extending these algorithms to colour. In the field of colour vision invariants are derived from physical models of the reflection of light on surfaces. These invariants have however three drawbacks which have to be taken into account. Firstly, the invariant features are derived from physical models, which only hold in controlled environments. Secondly, the invariants become unstable and unusable in the absence of colour. Thirdly, in the absence of colours photometric invariance is unattainable.
In this proposal we aim to incorporate the photometric invariance theory into the computation of features for object recognition. Instead of full invariance, which results in unusable features when deviations to the physical model occur or when colour is absent, we aim at photometric robustness. We intend to obtain the robustness in two steps: 1. Enrich the existing features used in object recognition with photometric invariant features accompanied by confidence measures. 2. Apply machine -learning techniques to learn the most discriminative features. The machine learning techniques enable the object recognition system to choose for each object between luminance based features and photometric invariant colour features.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences physical sciences astronomy space exploration
- engineering and technology materials engineering colors
- natural sciences computer and information sciences artificial intelligence computer vision image recognition
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering robotics
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
FP6-2004-MOBILITY-5
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Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
LE CHESNAY
France
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.