Rolling Shutter Stereo

A huge fraction of cameras used nowadays is based on CMOS sensors with a rolling shutter that exposes the image line by line. For dynamic scenes/cameras this introduces undesired effects like stretch, shear and wobble. It has been shown earlier that rotational shake induced rolling shutter effects in hand-held cell phone capture can be compensated based on an estimate of the camera rotation. In contrast, we analyse the case of significant camera motion, e.g. where a bypassing streetlevel capture vehicle uses a rolling shutter camera in a 3D reconstruction framework. The introduced error is depth dependent and cannot be compensated based on camera motion/rotation alone, invalidating also rectification for stereo camera systems. On top, significant lens distortion as often present in wide angle cameras intertwines with rolling shutter effects as it changes the time at which a certain 3D point is seen. We show that naive 3D reconstructions (assuming global shutter) will deliver biased geometry already for very mild assumptions on vehicle speed and resolution. We then develop rolling shutter dense multiview stereo algorithms that solve for time of exposure and depth at the same time, even in the presence of lens distortion and perform an evaluation on ground truth laser scan models as well as on real street-level data

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

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

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

Plexin-B2 Negatively Regulates Macrophage Motility, Rac, and Cdc42 Activation

Plexins are cell surface receptors widely studied in the nervous system, where they mediate migration and morphogenesis though the Rho family of small GTPases. More recently, plexins have been implicated in immune processes including cell-cell interaction, immune activation, migration, and cytokine production. Plexin-B2 facilitates ligand induced cell guidance and migration in the nervous system, and induces cytoskeletal changes in overexpression assays through RhoGTPase. The function of Plexin-B2 in the immune system is unknown. This report shows that Plexin-B2 is highly expressed on cells of the innate immune system in the mouse, including macrophages, conventional dendritic cells, and plasmacytoid dendritic cells. However, Plexin-B2 does not appear to regulate the production of proinflammatory cytokines, phagocytosis of a variety of targets, or directional migration towards chemoattractants or extracellular matrix in mouse macrophages. Instead, Plxnb2−/− macrophages have greater cellular motility than wild type in the unstimulated state that is accompanied by more active, GTP-bound Rac and Cdc42. Additionally, Plxnb2−/− macrophages demonstrate faster in vitro wound closure activity. Studies have shown that a closely related family member, Plexin-B1, binds to active Rac and sequesters it from downstream signaling. The interaction of Plexin-B2 with Rac has only been previously confirmed in yeast and bacterial overexpression assays. The data presented here show that Plexin-B2 functions in mouse macrophages as a negative regulator of the GTPases Rac and Cdc42 and as a negative regulator of basal cell motility and wound healing