Sébastien Jodogne

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This paper considers finite-automata based algorithms for handling linear arithmetic with both real and integer variables. Previous work has shown that this theory can be dealt with by using finite automata on infinite words, but this involves some difficult and delicate to implement algorithms. The contribution of this paper is to show, using topological(More)
This article considers finite-automata-based algorithms for handling linear arithmetic with both real and integer variables. Previous work has shown that this theory can be dealt with by using finite automata on infinite words, but this involves some difficult and delicate to implement algorithms. The contribution of this article is to show, using(More)
This paper addresses the problem of computing an exact and effective representation of the set of reachable configurations of a linear hybrid automaton. Our solution is based on accelerating the state-space exploration by computing symbolically the repeated effect of control cycles. The computed sets of configurations are represented by Real Vector Automata(More)
In this dissertation, I introduce a general, flexible framework for learning direct mappings from images to actions in an agent that interacts with its surrounding environment. This work is motivated by the paradigm of purposive vision. The original contributions consist in the design of reinforcement learning algorithms that are applicable to visual(More)
We discuss vision as a sensory modality for systems that effect actions in response to perceptions. While the internal representations informed by vision may be arbitrarily complex, we argue that in many cases it is advantageous to link them rather directly to action via learned mappings. These arguments are illustrated by two examples of our own work.(More)
With 18F-FDG PET/CT, tumor uptake intensity and heterogeneity have been associated with outcome in several cancers. This study aimed at investigating whether 18F-FDG uptake intensity, volume or heterogeneity could predict the outcome in patients with non-small cell lung cancers (NSCLC) treated by stereotactic body radiation therapy (SBRT). Sixty-three(More)
Solving a visual, interactive task can often be thought of as building a mapping from visual stimuli to appropriate actions. Clearly, the extracted visual characteristics that index into the repertoire of actions must be sufficiently rich to distinguish situations that demand distinct actions. Spatial combinations of local features permit, in principle, the(More)
We introduce flexible algorithms that can automatically learn mappings from images to actions by interacting with their environment. They work by introducing an image classifier in front of a Reinforcement Learning algorithm. The classifier partitions the visual space according to the presence or absence of highly informative local descriptors. The image(More)
Cephalometric analysis is an essential clinical and research tool in orthodontics for the orthodontic analysis and treatment planning. This paper presents the evaluation of the methods submitted to the Automatic Cephalometric X-Ray Landmark Detection Challenge, held at the IEEE International Symposium on Biomedical Imaging 2014 with an on-site competition.(More)