Spontaneous Subtle Expression Detection and Recognition based on Facial Strain

Optical strain is an extension of optical flow that is capable of quantifying subtle changes on faces and representing the minute facial motion intensities at the pixel level. This is computationally essential for the relatively new field of spontaneous micro-expression, where subtle expressions can be technically challenging to pinpoint. In this paper, we present a novel method for detecting and recognizing micro-expressions by utilizing facial optical strain magnitudes to construct optical strain features and optical strain weighted features. The two sets of features are then concatenated to form the resultant feature histogram. Experiments were performed on the CASME II and SMIC databases. We demonstrate on both databases, the usefulness of optical strain information and more importantly, that our best approaches are able to outperform the original baseline results for both detection and recognition tasks. A comparison of the proposed method with other existing spatio-temporal feature extraction approaches is also presented.Comment: 21 pages (including references), single column format, accepted to Signal Processing: Image Communication journa

Detection of deformable objects in a non-stationary scene

Image registration is the process of determining a mapping between points of interest on separate images to achieve a correspondence. This is a fundamental area of many problems in computer vision including object recognition and motion tracking. This research focuses on applying image registration to identify differences between frames in non-stationary video scenes for the purpose of motion tracking. The major stages for the image registration process include point detection, image correspondence, and an affine transformation. After applying image registration to spatially align the image frames and detect areas of motion segmentation is applied to segment the moving deformable objects in the non-stationary scenes. In this paper, specific techniques are reviewed to implement image registration. First, I will present other work related to image registration for feature point extraction, image correspondence, and spatial transformations. Then I will discuss deformable object recognition. This will be followed by a detailed description on the methods developed for this research and implementation. Included is a discussion on the Harris Corner Detection operator that allows the identification of key points on separate frames based on detecting areas in frames with strong contrasts in intensity values that can be identified as corners. These corners are the feature points that are comparable between frames. Then there will be an explanation on finding point correspondences between two separate video frames using ordinal and orientation measures. When a correspondence is made, the data acquired from the image correspondence calculations will be used to apply translation to align the video frames. With these methods, two frames of video can be properly aligned and then subtracted to detect deformable objects. Finally, areas of motions are segmented using histograms in the HSV color space. The algorithms are implemented using INTEL?s open computer vision library called OpenCV. The results demonstrate that this approach is successful at detecting deformable objects in non-stationary scenes

Pedestrian Flow Simulation Validation and Verification Techniques

For the verification and validation of microscopic simulation models of pedestrian flow, we have performed experiments for different kind of facilities and sites where most conflicts and congestion happens e.g. corridors, narrow passages, and crosswalks. The validity of the model should compare the experimental conditions and simulation results with video recording carried out in the same condition like in real life e.g. pedestrian flux and density distributions. The strategy in this technique is to achieve a certain amount of accuracy required in the simulation model. This method is good at detecting the critical points in the pedestrians walking areas. For the calibration of suitable models we use the results obtained from analyzing the video recordings in Hajj 2009 and these results can be used to check the design sections of pedestrian facilities and exits. As practical examples, we present the simulation of pilgrim streams on the Jamarat bridge. The objectives of this study are twofold: first, to show through verification and validation that simulation tools can be used to reproduce realistic scenarios, and second, gather data for accurate predictions for designers and decision makers.Comment: 19 pages, 10 figure

Visual Clutter Study for Pedestrian Using Large Scale Naturalistic Driving Data

Some of the pedestrian crashes are due to driver’s late or difficult perception of pedestrian’s appearance. Recognition of pedestrians during driving is a complex cognitive activity. Visual clutter analysis can be used to study the factors that affect human visual search efficiency and help design advanced driver assistant system for better decision making and user experience. In this thesis, we propose the pedestrian perception evaluation model which can quantitatively analyze the pedestrian perception difficulty using naturalistic driving data. An efficient detection framework was developed to locate pedestrians within large scale naturalistic driving data. Visual clutter analysis was used to study the factors that may affect the driver’s ability to perceive pedestrian appearance. The candidate factors were explored by the designed exploratory study using naturalistic driving data and a bottom-up image-based pedestrian clutter metric was proposed to quantify the pedestrian perception difficulty in naturalistic driving data. Based on the proposed bottom-up clutter metrics and top-down pedestrian appearance based estimator, a Bayesian probabilistic pedestrian perception evaluation model was further constructed to simulate the pedestrian perception process

Deep neutral hydrogen observations of Leo T with the Westerbork Synthesis Radio Telescope

Leo T is the lowest mass gas-rich galaxy currently known and studies of its gas content help us understand how such marginal galaxies survive and form stars. We present deep neutral hydrogen (HI) observations from the Westerbork Synthesis Radio Telescope in order to understand its HI distribution and potential for star formation. We find a larger HI line flux than the previously accepted value, resulting in a 50% larger HI mass of 4.1 x 10^5 Msun. The additional HI flux is from low surface brightness emission that was previously missed; with careful masking this emission can be recovered even in shallower data. We perform a Gaussian spectral decomposition to find a cool neutral medium component (CNM) with a mass of 3.7 x 10^4 Msun, or almost 10% of the total HI mass. Leo T has no HI emission extending from the main HI body, but there is evidence of interaction with the Milky Way circumgalactic medium in both a potential truncation of the HI body and the offset of the peak HI distribution from the optical center. The CNM component of Leo T is large when compared to other dwarf galaxies, even though Leo T is not currently forming stars and has a lower star formation efficiency than other gas-rich dwarf galaxies. However, the HI column density associated with the CNM component in Leo T is low. One possible explanation is the large CNM component is not related to star formation potential but rather a recent, transient phenomenon related to the interaction of Leo T with the Milky Way circumgalactic medium.Comment: accepted for publication in A&A; 12 pages, 13 figure

Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection

Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds on infrared images. Recently, deep learning based methods have achieved promising performance on SIRST detection, but at the cost of a large amount of training data with expensive pixel-level annotations. To reduce the annotation burden, we propose the first method to achieve SIRST detection with single-point supervision. The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters. To handle this issue, we introduce randomness to the clustering process by adding noise to the input images, and then obtain much more reliable pseudo masks by averaging the clustered results. Thanks to this "Monte Carlo" clustering approach, our method can accurately recover pseudo masks and thus turn arbitrary fully supervised SIRST detection networks into weakly supervised ones with only single point annotation. Experiments on four datasets demonstrate that our method can be applied to existing SIRST detection networks to achieve comparable performance with their fully supervised counterparts, which reveals that single-point supervision is strong enough for SIRST detection. Our code will be available at: https://github.com/YeRen123455/SIRST-Single-Point-Supervision