Deeply optimized Hough transform: Application to action segmentation

Conference of 17th International Conference on Image Analysis and Processing, ICIAP 2013 ; Conference Date: 9 September 2013 Through 13 September 2013; Conference Code:99647International audienceHough-like methods like Implicit Shape Model (ISM) and Hough forest have been successfully applied in multiple computer vision fields like object detection, tracking, skeleton extraction or human action detection. However, these methods are known to generate false positives. To handle this issue, several works like Max-Margin Hough Transform (MMHT) or Implicit Shape Kernel (ISK) have reported significant performance improvements by adding discriminative parameters to the generative ones introduced by ISM. In this paper, we offer to use only discriminative parameters that are globally optimized according to all the variables of the Hough transform. To this end, we abstract the common vote process of all Hough methods into linear equations, leading to a training formulation that can be solved using linear programming solvers. Our new Hough Transform significantly outperforms the previous ones on HoneyBee and TUM datasets, two public databases of action and behaviour segmentation

Decoding the Temporal Dynamics of Covert Spatial Attention Using Multivariate EEG Analysis: Contributions of Raw Amplitude and Alpha Power

Attention can be oriented in space covertly without the need of eye movements. We used multivariate pattern classification analyses (MVPA) to investigate whether the time course of the deployment of covert spatial attention leading up to the observer's perceptual decision can be decoded from both EEG alpha power and raw activity traces. Decoding attention from these signals can help determine whether raw EEG signals and alpha power reflect the same or distinct features of attentional selection. Using a classical cueing task, we showed that the orientation of covert spatial attention can be decoded by both signals. However, raw activity and alpha power may reflect different features of spatial attention, with alpha power more associated with the orientation of covert attention in space and raw activity with the influence of attention on perceptual processes

Skeleton point trajectories for human daily activity recognition

Conference of 8th International Conference on Computer Vision Theory and Applications, VISAPP 2013 ; Conference Date: 21 February 2013 Through 24 February 2013; Conference Code:97053International audienceAutomatic human action annotation is a challenging problem, which overlaps with many computer vision fields such as video-surveillance, human-computer interaction or video mining. In this work, we offer a skeleton based algorithm to classify segmented human-action sequences. Our contribution is twofold. First, we offer and evaluate different trajectory descriptors on skeleton datasets. Six short term trajectory features based on position, speed or acceleration are first introduced. The last descriptor is the most original since it extends the well-known bag-of-words approach to the bag-of-gestures ones for 3D position of articulations. All these descriptors are evaluated on two public databases with state-of-the art machine learning algorithms. The second contribution is to measure the influence of missing data on algorithms based on skeleton. Indeed skeleton extraction algorithms commonly fail on real sequences, with side or back views and very complex postures. Thus on these real data, we offer to compare recognition methods based on image and those based on skeleton with many missing data

A region driven and contextualized pedestrian detector

Conference of 8th International Conference on Computer Vision Theory and Applications, VISAPP 2013 ; Conference Date: 21 February 2013 Through 24 February 2013; Conference Code:97053International audienceThis paper tackles the real-time pedestrian detection problem using a stationary calibrated camera. Problems frequently encountered are: a generic classifier can not be adjusted to each situation and the perspective deformations of the camera can profoundly change the appearance of a person. To avoid these drawbacks we contextualized a detector with information coming directly from the scene. Our method comprises three distinct parts. First an oracle gathers examples from the scene. Then, the scene is split in different regions and one classifier is trained for each one. Finally each detector are automatically tuned to achieve the best performances. Designed for making camera network installation procedure easier, our method is completely automatic and does not need any knowledge about the scene

The Implementation Gap: Environmental Rhetoric Versus Reality in Lao Cai, Vietnam

International audienceThis study draws upon a case study of Lao Cai, a province recognized as one of the most important ecological regions in Vietnam, but also one of the most vulnerable to climate hazards. The province has recently adopted an action plan for climate change adaptation. However, the national authorities intend to promote Lao Cai as a major secondary city on the main route from China to Hanoi. In a context of rapid, strategic, state-driven urban development, I identify three main obstacles to effective implementation of environmental and climate change policies: (1) the pre-eminence of economic growth over any environmental goal, (2) the under- enforcement of existing regulations, and (3) a failure of environmental governance. Environmental risk management is mainly based on the reinforcement of defensive infrastructures (such as the river embankment) and the displacement of exposed people. These actions are likely inefficient in a context of increased major hazards that might put great pressure on displaced residents’ livelihoods. In other words, there is a wide gap between discourse and implementation

Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor

The 2002–3 pandemic caused by severe acute respiratory syndrome coronavirus (SARS-CoV) was one of the most significant public health events in recent history1. An ongoing outbreak of Middle East respiratory syndrome coronavirus2 suggests that this group of viruses remains a key threat and that their distribution is wider than previously recognized. Although bats have been suggested to be the natural reservoirs of both viruses3,4,5, attempts to isolate the progenitor virus of SARS-CoV from bats have been unsuccessful. Diverse SARS-like coronaviruses (SL-CoVs) have now been reported from bats in China, Europe and Africa5,6,7,8, but none is considered a direct progenitor of SARS-CoV because of their phylogenetic disparity from this virus and the inability of their spike proteins to use the SARS-CoV cellular receptor molecule, the human angiotensin converting enzyme II (ACE2)9,10. Here we report whole-genome sequences of two novel bat coronaviruses from Chinese horseshoe bats (family: Rhinolophidae) in Yunnan, China: RsSHC014 and Rs3367. These viruses are far more closely related to SARS-CoV than any previously identified bat coronaviruses, particularly in the receptor binding domain of the spike protein. Most importantly, we report the first recorded isolation of a live SL-CoV (bat SL-CoV-WIV1) from bat faecal samples in Vero E6 cells, which has typical coronavirus morphology, 99.9% sequence identity to Rs3367 and uses ACE2 from humans, civets and Chinese horseshoe bats for cell entry. Preliminary in vitro testing indicates that WIV1 also has a broad species tropism. Our results provide the strongest evidence to date that Chinese horseshoe bats are natural reservoirs of SARS-CoV, and that intermediate hosts may not be necessary for direct human infection by some bat SL-CoVs. They also highlight the importance of pathogen-discovery programs targeting high-risk wildlife groups in emerging disease hotspots as a strategy for pandemic preparedness