Segmentation (informatique)

Résumé

En informatique, la segmentation est une technique de découpage de la mémoire. Elle est gérée par l'unité de segmentation de l'unité de gestion mémoire (dite MMU, Memory Management Unit), utilisée sur les systèmes d'exploitation modernes, qui divise la mémoire physique (dans le cas de la segmentation pure) ou la mémoire virtuelle (dans le cas de la segmentation avec pagination) en segments caractérisés par leur adresse de début et leur taille (décalage). La segmentation permet la séparation des données et du programme (entre autres segments) dans des espaces logiquement indépendants facilitant alors la programmation, l'édition de liens et le partage interprocessus. La segmentation permet également d'offrir une plus grande protection grâce au niveau de privilège de chaque segment (voir Descripteur de segment). Fonctionnement Lorsque l’unité de gestion mémoire (MMU pour Memory Management Unit) doit traduire une adresse logique en adresse linéaire, l'unité de segmentation d

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A Modular Approach to Learning Manipulation Strategies from Human Demonstration

Aude Billard, Ravin Luis De Souza, Miao Li

Object manipulation is a challenging task for robotics, as the physics involved in object interaction is com- plex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly we record a human perform- ing a task that requires an adaptive control strategy in differ- ent conditions, i.e. different task contexts. We then perform modular decomposition of the control strategy, using phases of the recorded actions to guide segmentation. Each mod- ule represents a part of the strategy, encoded as a pair of forward and inverse models. All modules contribute to the final control policy; their recommendations are integrated via a system of weighting based on their own estimated er- ror in the current task context. We validate our approach by demonstrating it, both in a simulation for clarity, and on a real robot platform to demonstrate robustness and capacity to generalise. The robot task is opening bottle caps. We show that our approach can modularize an adaptive control strategy and generate appropriate motor commands for the robot to accomplish the complete task, even for novel bottles.

Springer Verlag2015

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A modular approach to learning manipulation strategies from human demonstration

Aude Billard, Ravin Luis De Souza, Miao Li

Object manipulation is a challenging task for robotics, as the physics involved in object interaction is complex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly we record a human performing a task that requires an adaptive control strategy in different conditions, i.e. different task contexts. We then perform modular decomposition of the control strategy, using phases of the recorded actions to guide segmentation. Each module represents a part of the strategy, encoded as a pair of forward and inverse models. All modules contribute to the final control policy; their recommendations are integrated via a system of weighting based on their own estimated error in the current task context. We validate our approach by demonstrating it, both in a simulation for clarity, and on a real robot platform to demonstrate robustness and capacity to generalise. The robot task is opening bottle caps. We show that our approach can modularize an adaptive control strategy and generate appropriate motor commands for the robot to accomplish the complete task, even for novel bottles.

Springer Verlag2016

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Large training datasets have played a vital role in the success of modern deep learning methods in computer vision. But, obtaining sufficient amount of training data is challenging, specially when annotating volumetric images. This is because fully annotating a single image volume require annotating a whole stack of images which is costly. Moreover, majority of volumetric images originate from niche domains that require expert knowledge to annotate and it further increase the associated cost. Therefore, designing models for volumetric image segmentation that can work with small training datasets is of great importance. One way to address this challenge is to use prior knowledge in segmentation model design. There are many forms of prior knowledge and in this thesis, we focus on using object priors which defines prior knowledge related to the properties of the object being segmented. We begin by proposing an end-to-end differentiable architecture that produce surface meshes from input image volumes which enable easy integration of object priors related to topology and surface properties. With that we demonstrate how the priors help achieve better accuracy and quality in predictions compared to state-of-the-art methods that do not use them. Then we focus on surface priors and propose a methodology based on Active Surface models to achieve better trade-offs between the accuracy of the surfaces produced by the segmentation models and their quality. Afterwards, we apply the Active Surface model proposed earlier to develop an interactive annotation tool that significantly reduce the effort necessary to annotate image volumes. Along with that, a volumetric image segmentation model that best utilize annotations produced with the tool is also proposed. Finally we focus on object priors related to overall shape. We use Probabilistic Atlases (PAs) to introduce this prior knowledge into the segmentation model and demonstrate its ability to improve the accuracy in predictions. Overall, we use object prior to improve the accuracy, quality and the speed of annotation in volumetric image segmentation. To demonstrate their effectiveness, we use volumetric images produced by Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Electron Microscopy (EMs).

EPFL2022

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