This paper presents a novel LDP based image descriptor which is more robust to temporal face changes. LDP is a framework to encode directional pattern based on local derivative variations, hence LDP is highly directional. However texture based features extracted globally tend to average over the image area. Hence this paper proposes to divide the face image into multiple regions and perform LDP...

In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has ri...

We present a new functional magnetic resonance imaging (fMRI) analysis method that incorporates both spatial and temporal dynamics of blood-oxygen-level dependent (BOLD) signals within a region of interest (ROI). 3D moment descriptors are used to characterize the spatial changes in BOLD signals over time. The method is tested on fMRI data collected from eight healthy subjects performing a bulb-...

The brain can be thought of as a collective ensemble ranging in the spatial domain from microscopic elements (molecules, receptors, ionic channels, synapses) to macroscopic entities (layers, nuclei, cortical areas, neural networks) (Figure 1). The same multi-scale analysis can be replicated in the temporal domain, when decomposing brain activity in a multitude of dynamic processes with time con...

This work presents an evaluation of using time-causal scalespace filters as primitives for video analysis. For this purpose, we present a new family of video descriptors based on regional statistics of spatiotemporal scale-space filter responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms ...

Geographical Information Science is challenged by an unprecedented increase in the availability of tracking data related to human and animal movement, typically captured through location-aware portable devices such as GPS receivers. Capture of trajectory data at fine temporal and spatial granularities has allowed with the representation of detailed geospatial lifelines, opening new options for ...

The current main trend in paralinguistic information recognition is the so-called static classification. In this kind of classification the low level descriptors are pooled together by means of statistical functionals and all, or almost all, information about the temporal structure and evolution of speech is lost. Although this approach represents the state-ofthe-art, we believe that dynamic cl...

Human action recognition has drawn much attention in the field of video analysis. In this paper, we develop a human action detection and recognition process based on the tracking of Interest Points (IP) trajectory. A pre-processing step that performs spatio-temporal action detection is proposed. This step uses optical flow along with dense speed-up-robust-features (SURF) in order to detect and ...

Encoding is one of the key factors for building an effective video representation. In the recent works, super vector-based encoding approaches are highlighted as one of the most powerful representation generators. Vector of Locally Aggregated Descriptors (VLAD) is one of the most widely used super vector methods. However, one of the limitations of VLAD encoding is the lack of spatial informatio...

Spatio-temporal oriented energy features have been proved to be an efficient feature for action recognition. It has satisfied performance on most of public databases. However, the oriented energy features were used as holistic action features for template matching in many literatures. In the paper, we proposed an action representation based on local spatio-temporal oriented energy features, and...