Grid Loss: Detecting Occluded Faces

Detection of partially occluded objects is a challenging computer vision problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of the detection window are occluded, since not every sub-part of the window is discriminative on its own. To address this issue, we propose a novel loss layer for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a convolution layer independently rather than over the whole feature map. This results in parts being more discriminative on their own, enabling the detector to recover if the detection window is partially occluded. By mapping our loss layer back to a regular fully connected layer, no additional computational cost is incurred at runtime compared to standard CNNs. We demonstrate our method for face detection on several public face detection benchmarks and show that our method outperforms regular CNNs, is suitable for realtime applications and achieves state-of-the-art performance.Comment: accepted to ECCV 201

Improving Global Multi-target Tracking with Local Updates

Conference dates: September 6-7 & 12, 2014We propose a scheme to explicitly detect and resolve ambiguous situations in multiple target tracking. During periods of uncertainty, our method applies multiple local single target trackers to hypothesise short term tracks. These tracks are combined with the tracks obtained by a global multi-target tracker, if they result in a reduction in the global cost function. Since tracking failures typically arise when targets become occluded, we propose a local data association scheme to maintain the target identities in these situations. We demonstrate a reduction of up to 50% in the global cost function, which in turn leads to superior performance on several challenging benchmark sequences. Additionally, we show tracking results in sports videos where poor video quality and frequent and severe occlusions between multiple players pose difficulties for state-of-the-art trackers.Anton Milan, Rikke Gade, Anthony Dick, Thomas B. Moeslund, and Ian Rei

Incorporating corrosion measurement in hip wear simulators: An added complication or a necessity?

Corrosion is not routinely considered in the assessment of the degradation or the lifetime of total hip replacement bearing surfaces. Biomechanical simulations are becoming ever more complex and are taking into account motion cycles that represent activities beyond a simple walking gait at 1 Hz, marking a departure from the standard ISO BS 14242. However, the degradation is still very often referred to as wear, even though the material loss occurs due to a combination of tribological and corrosion processes and their interactions. This article evaluates how, by incorporating real-time corrosion measurements in total hip replacement simulations, pre-clinical evaluations and research studies can both yield much more information and accelerate the process towards improved implants

Volumetric Attention for 3D Medical Image Segmentation and Detection

A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction, and allows the use of pretrained 2D detection models when training data is limited, as is often the case for medical applications. Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge winner by 3.9 points and achieving top performance on the LiTS leader board at the time of paper submission. Detection experiments on the DeepLesion dataset also show that the addition of VA to existing object detectors enables a 69.1 sensitivity at 0.5 false positive per image, outperforming the best published results by 6.6 points.Comment: Accepted by MICCAI 201

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Search for direct pair production of the top squark in all-hadronic final states in proton-proton collisions at s√=8 TeV with the ATLAS detector

The results of a search for direct pair production of the scalar partner to the top quark using an integrated luminosity of 20.1fb−1 of proton–proton collision data at √s = 8 TeV recorded with the ATLAS detector at the LHC are reported. The top squark is assumed to decay via t˜→tχ˜01 or t˜→ bχ˜±1 →bW(∗)χ˜01 , where χ˜01 (χ˜±1 ) denotes the lightest neutralino (chargino) in supersymmetric models. The search targets a fully-hadronic final state in events with four or more jets and large missing transverse momentum. No significant excess over the Standard Model background prediction is observed, and exclusion limits are reported in terms of the top squark and neutralino masses and as a function of the branching fraction of t˜ → tχ˜01 . For a branching fraction of 100%, top squark masses in the range 270–645 GeV are excluded for χ˜01 masses below 30 GeV. For a branching fraction of 50% to either t˜ → tχ˜01 or t˜ → bχ˜±1 , and assuming the χ˜±1 mass to be twice the χ˜01 mass, top squark masses in the range 250–550 GeV are excluded for χ˜01 masses below 60 GeV

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Functional analysis of Ectodysplasin-A mutations causing selective tooth agenesis.

Mutations of the Ectodysplasin-A (EDA) gene are generally associated with the syndrome hypohidrotic ectodermal dysplasia (MIM 305100), but they can also manifest as selective, non-syndromic tooth agenesis (MIM300606). We have performed an in vitro functional analysis of six selective tooth agenesis-causing EDA mutations (one novel and five known) that are located in the C-terminal tumor necrosis factor homology domain of the protein. Our study reveals that expression, receptor binding or signaling capability of the mutant EDA1 proteins is only impaired in contrast to syndrome-causing mutations, which we have previously shown to abolish EDA1 expression, receptor binding or signaling. Our results support a model in which the development of the human dentition, especially of anterior teeth, requires the highest level of EDA-receptor signaling, whereas other ectodermal appendages, including posterior teeth, have less stringent requirements and form normally in response to EDA mutations with reduced activity

A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions

Abstract Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400–2500 nm in this case, giving detailed information about the spectral reflectance of the plant. Although this technology has been used in laboratory-based controlled lighting conditions for early detection of plant disease, the transfer of such technology to imaging plants in field conditions presents a number of challenges. These include problems caused by varying light levels and difficulties of separating the target plant from its background. Here we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection. Graph theory was used to minimise a cost function to detect the continuous boundary between uninteresting plants and the area of interest. The method includes automatic detection of a known reflectance tile which was kept constantly within the field of view for all image scans. A method to split images containing rows of multiple raspberry plants into individual plants was also developed. Validation was carried out by comparison of plant height and density measurements with manually scored values. A reasonable correlation was found between these manual scores and measurements taken from the images (r2 = 0.75 for plant height). These preliminary steps are an essential requirement before detailed spectral analysis of the plants can be achieved