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Although many visual attention models have been proposed, very few saliency models investigated the impact of audio information. To develop audio-visual attention models, researchers need to have a ground truth of eye movements recorded while exploring complex natural scenes in different audio conditions. They also need tools to compare eye movements and gaze patterns between these different audio conditions.

The capability of determining the quality of target detections is important for applications using smart cameras, such as autonomous robotics and surveillance. We propose to estimate the quality of target detections by integrating the target location uncertainty over polygonal domains, which represent the fields of view of the cameras. We define a framework based on numerical integration that easily accommodates multiple models for uncertainty and fields of view.

In the recent years, we experienced the proliferation of sensors for retrieving depth information on a scene, such as LIDAR or RGBD sensors (Kinect). However, it is still a challenge to identify the meaning of a specific point cloud to recognize the underlying object. Here, we wonder if it is possible to define a global feature for an object that is robust to noise, sampling and occlusion. We propose a local measure based on curvature. We called it Principal Curvatures because rather than using the Gaussian curvature we keep the

In this paper, we propose an original methodology allowing the computation of the saliency maps for high dimensional RTI data (Reflectance Transformation Imaging). Unlike most of the classical methods, our approach aims at devising an intrinsic visual saliency of the surface, independent of the sensor (image) and the geometry of the scene (light-object-camera).

Unique encoding of the dynamics of facial actions has potential to provide a spontaneous facial expression recognition system. The most promising existing approaches rely on deep learning of facial actions. However, current approaches are often computationally intensive and require a great deal of memory/processing time, and typically the temporal aspect of facial actions are often ignored, despite the potential wealth of information available from the spatial dynamic movements and their temporal evolution over time from neutral state to apex state.

In this paper, a novel motion-information-based 3D video coding method is proposed for texture plus depth video, which addresses the issue that the traditional motion estimation methods cannot work well for all types of motion.
The global motion information is exploited to :
1. project the reference frames to the view of current-to- be-encoded frame,
2. The motion compensated virtual frame becomes more similar to the current-to-be-encoded frame.

Large scale images allow pathologists to perform reviews, using
computer workstations instead of microscopes. This trend raises
a wide range of issues related to the management of these massive
datasets. In particular, efficient solutions for data storage and processing
have to be developed in order to deliver increasingly reliable
and faster analyses. In addition, the improvement of workflows also
requires the reinforcement of visualization capabilities. In this paper,
we present a new virtual microscopy (VM) approach for interactivetime