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International audience; We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstained images.

International audience; Learn how to use GPU for accelerating 3D registration with Kinect or similar devices in order to capture highly-detailed facial performance in real time or at an interactive speed. We describe the energy-based approach that we borrowed from Hao Li et al. paper published at SGP 2008. We also explain why we can benefit from GPU computation power and achieve higher quality and more detail at interactive speeds. Finally, we elaborate on how real-time performance can be achieved by improving our CUDA-based implementation

International audience; Proceedings of the 6th international conference on Intelligent Human Computer Interaction, IHCI 2014, that was held in Evry, France, on December 8-10, 2014. IHCI is a forum for the presentation of technological advances and research results at the crossroads of human-computer interaction, artificial intelligence, signal processing and computer vision. It brings together engineers and scientists from around the world

International audience; Haar-like features are ubiquitous in computer vision, e.g. for Viola and Jones face detection or local descriptors such as Speeded-Up-Robust-Features. They are classically computed in one pass over integral image by reading the values at the feature corners. Here we present a new, general, parsing formalism for convolving them more efficiently. Our method is fully automatic and applicable to an arbitrary set of Haar-like features. The parser reduces the number of memory accesses which are the main computational bottleneck during convolution on modern computer architectures. It first splits the features into simpler kernels. Then it aligns and reuses them where applicable forming an ensemble of recursive convolution trees, which can be computed faster. This is illustrated with experiments, which show a significant speed-up over the classic approach.

International audience; Our goal is to capture style from real human motion so it can be rendered with a virtual agent that represents this human user. We used expressivity parameters to describe motion style. As a first contribution, we propose an approach to estimate a subset of expressivity parameters defined in the literature (namely spatial extent and temporal extent) from captured motion trajectories. Second, we capture the expressivity of real users and then output it to the Greta engine that animates a virtual agent representing the user. We experimentally demonstrate that expressivity can be another clue for identifiable virtual clones of real humans.

A challenge for 3D motion capture by monocular vision is 3D-2D projection ambiguities that may bring incorrect poses during tracking. In this paper, we propose improving 3D motion capture by learning human gesture models from a library of gestures with variants. This library has been created with virtual human animations. Gestures are described as Gaussian Process Dynamic Models (GPDM) and are used as constraints for motion tracking. Given the raw input poses from the tracker, the gesture model helps to correct ambiguous poses. The benefit of the proposed method is demonstrated with results.

International audience; In this paper we present how work toward building a library of communicative gestures with the aim to use it to train the tracking algorithm. We describe also how we encompass user's communicative distinctiveness in our library

International audience; Virtual worlds are developing rapidly over the Internet. They are visited by avatars and staffed with Embodied Conversational Agents (ECAs). An avatar is a representation of a physical person. Each person controls one or several avatars and usually receives feedback from the virtual world on an audio-visual display. Ideally, all senses should be used to feel fully embedded in a virtual world. Sound, vision and sometimes touch are the available modalities. This paper reviews the technological developments which enable audio-visual interactions in virtual and augmented reality worlds. Emphasis is placed on speech and gesture interfaces, including talking face analysis and synthesis

International audience; This paper presents briefly describes the state of the art of accelerating image processing with graphics hardware (GPU) and discusses some of its caveats. Then it describes GpuCV, an open source multi-platform library for GPU-accelerated image processing and Computer Vision operators and applications. It is meant for computer vision scientist not familiar with GPU technologies. GpuCV is designed to be compatible with the popular OpenCV library by offering GPU-accelerated operators that can be integrated into native OpenCV applications. The GpuCV framework transparently manages hardware capabilities, data synchronization, activation of low level GLSL and CUDA programs, on-the-fly benchmarking and switching to the most efficient implementation and finally offers a set of image processing operators with GPU acceleration available

International audience; Home Healthcare application based on vital and actimetrics parameters telemonitoring : " Télévigilance ". Gesture-based communication in networked virtual environments. Speech Recognition Technologies

International audience; This paper presents a state of the art report on using graphics hardware for image processing and computer vision. Then we describe GPUCV, an open library for easily developing GPU accelerated image processing and analysis operators and applications.