Hand Posture Recognition with standard webcam for Natural Interaction

This paper presents an experimental prototype designed for natural human-computer interaction in an environmental intelligence system. Using computer vision resources, it analyzes the images captured by a webcam to recognize a person’s hand movements. There is now a strong trend in interpreting these hand and body movements in general, with computer vision, which is a very attractive field of research. In this study, a mechanism for natural interaction was implemented by analyzing images captured by a webcam based on hand geometry and posture, to show its movements in our model. A camera is installed in such a manner that it can discriminate the movements a person makes using Background Subtraction. Then hands are searched for assisted by segmentation by skin color detection and a series of classifiers. Finally, the geometric characteristics of the hands are extracted to distinguish defined control action positions

Deep Placental Vessel Segmentation for Fetoscopic Mosaicking

During fetoscopic laser photocoagulation, a treatment for twin-to-twin transfusion syndrome (TTTS), the clinician first identifies abnormal placental vascular connections and laser ablates them to regulate blood flow in both fetuses. The procedure is challenging due to the mobility of the environment, poor visibility in amniotic fluid, occasional bleeding, and limitations in the fetoscopic field-of-view and image quality. Ideally, anastomotic placental vessels would be automatically identified, segmented and registered to create expanded vessel maps to guide laser ablation, however, such methods have yet to be clinically adopted. We propose a solution utilising the U-Net architecture for performing placental vessel segmentation in fetoscopic videos. The obtained vessel probability maps provide sufficient cues for mosaicking alignment by registering consecutive vessel maps using the direct intensity-based technique. Experiments on 6 different in vivo fetoscopic videos demonstrate that the vessel intensity-based registration outperformed image intensity-based registration approaches showing better robustness in qualitative and quantitative comparison. We additionally reduce drift accumulation to negligible even for sequences with up to 400 frames and we incorporate a scheme for quantifying drift error in the absence of the ground-truth. Our paper provides a benchmark for fetoscopy placental vessel segmentation and registration by contributing the first in vivo vessel segmentation and fetoscopic videos dataset.Comment: Accepted at MICCAI 202

Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies

Globally aligned photomosaic of the Lucky Strike hydrothermal vent field (Mid-Atlantic Ridge, 37°18.5′N) : release of georeferenced data, mosaic construction, and viewing software

Author Posting. © American Geophysical Union, 2008. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geochemistry Geophysics Geosystems 9 (2008): Q12009, doi:10.1029/2008GC002204.We present a georeferenced photomosaic of the Lucky Strike hydrothermal vent field (Mid-Atlantic Ridge, 37°18′N). The photomosaic was generated from digital photographs acquired using the ARGO II seafloor imaging system during the 1996 LUSTRE cruise, which surveyed a ∼1 km2 zone and provided a coverage of ∼20% of the seafloor. The photomosaic has a pixel resolution of 15 mm and encloses the areas with known active hydrothermal venting. The final mosaic is generated after an optimization that includes the automatic detection of the same benthic features across different images (feature-matching), followed by a global alignment of images based on the vehicle navigation. We also provide software to construct mosaics from large sets of images for which georeferencing information exists (location, attitude, and altitude per image), to visualize them, and to extract data. Georeferencing information can be provided by the raw navigation data (collected during the survey) or result from the optimization obtained from image matching. Mosaics based solely on navigation can be readily generated by any user but the optimization and global alignment of the mosaic requires a case-by-case approach for which no universally software is available. The Lucky Strike photomosaics (optimized and navigated-only) are publicly available through the Marine Geoscience Data System (MGDS, http://www.marine-geo.org). The mosaic-generating and viewing software is available through the Computer Vision and Robotics Group Web page at the University of Girona (http://eia.udg.es/∼rafa/mosaicviewer.html).This work has been supported by the EU Marie Curie RTNs MOMARNet (OD, RG, JE, LN, JF, NG) and FREESUBNet (RG, NG, XC), the Spanish Ministry of Science and Innovation (grant CTM2007–64751; RG, JE), CNRS and ANR (grant ANR NT05–3_42212, JE), ICREA (LN), and by the Generalitat de Catalunya (JE, RG). JF has been funded by MICINN under FPI grant BES-2006-12733 and NG has been supported by MICINN under the ‘‘Ramon y Cajal’’ program

Measurement of Creep Deformation across Welds in 316H Stainless Steel Using Digital Image Correlation

Spatially resolved measurement of creep deformation across weldments at high temperature cannot be achieved using standard extensometry approaches. In this investigation, a Digital Image Correlation (DIC) based system has been developed for long-term high-temperature creep strain measurement in order to characterise the material deformation behaviour of separate regions of a multi-pass weld. The optical system was sufficiently stable to allow a sequence of photographs to be taken suitable for DIC analysis of creep specimens tested at a temperature of 545 °C for over 2000 h. The images were analysed to produce local creep deformation curves from two cross-weld samples cut from contrasting regions of a multi-pass V-groove weld joining thick-section AISI Type 316H austenitic stainless steel. It is shown that for this weld, the root pass is the weakest region of the structure in creep, most likely due to the large number of thermal cycles it has experienced during the fabrication process. The DIC based measurement method offers improved spatial resolution over conventional methods and greatly reduces the amount of material required for creep characterisation of weldments

Modelling human musculoskeletal functional movements using ultrasound imaging

<p>Abstract</p> <p>Background</p> <p>A widespread and fundamental assumption in the health sciences is that muscle functions are related to a wide variety of conditions, for example pain, ischemic and neurological disorder, exercise and injury. It is therefore highly desirable to study musculoskeletal contributions in clinical applications such as the treatment of muscle injuries, post-surgery evaluations, monitoring of progressive degeneration in neuromuscular disorders, and so on.</p> <p>The spatial image resolution in ultrasound systems has improved tremendously in the last few years and nowadays provides detailed information about tissue characteristics. It is now possible to study skeletal muscles in real-time during activity.</p> <p>Methods</p> <p>The ultrasound images are transformed to be congruent and are effectively compressed and stacked in order to be analysed with multivariate techniques. The method is applied to a relevant clinical orthopaedic research field, namely to describe the dynamics in the Achilles tendon and the calf during real-time movements.</p> <p>Results</p> <p>This study introduces a novel method to medical applications that can be used to examine ultrasound image sequences and to detect, visualise and quantify skeletal muscle dynamics and functions.</p> <p>Conclusions</p> <p>This new objective method is a powerful tool to use when visualising tissue activity and dynamics of musculoskeletal ultrasound registrations.</p

Recognising Complex Activities with Histograms of Relative Tracklets

One approach to the recognition of complex human activities is to use feature descriptors that encode visual inter-actions by describing properties of local visual features with respect to trajectories of tracked objects. We explore an example of such an approach in which dense tracklets are described relative to multiple reference trajectories, providing a rich representation of complex interactions between objects of which only a subset can be tracked. Specifically, we report experiments in which reference trajectories are provided by tracking inertial sensors in a food preparation sce-nario. Additionally, we provide baseline results for HOG, HOF and MBH, and combine these features with others for multi-modal recognition. The proposed histograms of relative tracklets (RETLETS) showed better activity recognition performance than dense tracklets, HOG, HOF, MBH, or their combination. Our comparative evaluation of features from accelerometers and video highlighted a performance gap between visual and accelerometer-based motion features and showed a substantial performance gain when combining features from these sensor modalities. A considerable further performance gain was observed in combination with RETLETS and reference tracklet features