We propose a method that performs anomaly detection and localisation within heterogeneous data using a pairwise undirected mixed graphical model. The data are a mixture of categorical and quantitative variables, and the model is learned over a dataset that is supposed not to contain any anomaly. We then use the model over temporal data, potentially a data stream, using a version of the two-side...

Species distributional estimates are an essential tool to improve and implement effective conservation management measures. Nevertheless, obtaining accurate remains a challenge in many cases, especially when looking at the marine environment, mainly due species mobility habitat dynamism. Ecosystems surrounding oceanic islands highly dynamic constitute key actor on pelagic habitats, congregating...

This paper presents a performance evaluation of shape similarity metrics for 3D video sequences of people with unknown temporal correspondence. Previous evaluation focuses on non-skeletal similarity metrics. Since the human motion is essentially an articulated motion, it is also interesting to investigate skeletal-based similarity metrics. In this paper, we extend previous evaluation of non-ske...

Diverse scene stitching is a challenging task in aerial video surveillance. This paper presents a hybrid stitching method based on the observation that aerial videos captured in real surveillance settings are neither totally ordered nor completely unordered. Often, human operators apply continuous monitoring of the drone to revisit the same area of interest. This monitoring mechanism yields to ...

Objectives: The objective of this study is to increase the spatial and temporal resolution of dynamic 3D MR imaging of lung volumes and diaphragm motion. To achieve this goal, we evaluate the utility of the proposed blind compressed sensing (BCS) algorithm to recover data from highly undersampled measurements. Materials and Methods: We evaluated the performance of the BCS scheme to recover dyna...

Generative models in vision have seen rapid progress due to algorithmic improvements and the availability of high-quality image datasets. In this paper, we offer contributions in both these areas to enable similar progress in audio modeling. First, we detail a powerful new WaveNet-style autoencoder model that conditions an autoregressive decoder on temporal codes learned from the raw audio wave...

We assessed the relationship between temporal lobe metabolism measured quantitatively and qualitatively with PET using [18F]-fluorodeoxyglucose (FDG) and postoperative seizure frequency after anterior temporal lobectomy. Forty-three patients with refractory partial epilepsy had anterior temporal lobectomy and preoperative assessment with PET-FDG. Qualitative PET analysis was performed visually ...

Recently, the popularity of depth-sensors such as Kinect has made depth videos easily available while its advantages have not been fully exploited. This paper investigates, for gesture recognition, to explore the spatial and temporal information complementarily embedded in RGB and depth sequences. We propose a convolutional twostream consensus voting network (2SCVN) which explicitly models both...

Structural health monitoring (SHM) is challenged by massive data storage pressure and structural fault location. In response to these issues, a bio-inspired memory model that is embedded with a causality reasoning function is proposed for fault location. First, the SHM data for processing are divided into three temporal memory areas to control data volume reasonably. Second, the inherent potent...

For the effective operation of intelligent assistive systems working in real-world human environments, it is important to be able to recognise human activities and their intentions. In this paper we propose a novel approach to activity recognition from visual data. Our approach is based on qualitative and quantitative spatio-temporal features which encode the interactions between human subjects...