Template Adaptation for Face Verification and Identification

Face recognition performance evaluation has traditionally focused on one-to-one verification, popularized by the Labeled Faces in the Wild dataset for imagery and the YouTubeFaces dataset for videos. In contrast, the newly released IJB-A face recognition dataset unifies evaluation of one-to-many face identification with one-to-one face verification over templates, or sets of imagery and videos for a subject. In this paper, we study the problem of template adaptation, a form of transfer learning to the set of media in a template. Extensive performance evaluations on IJB-A show a surprising result, that perhaps the simplest method of template adaptation, combining deep convolutional network features with template specific linear SVMs, outperforms the state-of-the-art by a wide margin. We study the effects of template size, negative set construction and classifier fusion on performance, then compare template adaptation to convolutional networks with metric learning, 2D and 3D alignment. Our unexpected conclusion is that these other methods, when combined with template adaptation, all achieve nearly the same top performance on IJB-A for template-based face verification and identification

Learning Object-Independent Modes of Variation with Feature Flow Fields

We present a unifying framework in which "object-independent" modes of variation are learned from continuous-time data such as video sequences. These modes of variation can be used as "generators" to produce a manifold of images of a new object from a single example of that object. We develop the framework in the context of a well-known example: analyzing the modes of spatial deformations of a scene under camera movement. Our method learns a close approximation to the standard affine deformations that are expected from the geometry of the situation, and does so in a completely unsupervised (i.e. ignorant of the geometry of the situation) fashion. We stress that it is learning a "parameterization", not just the parameter values, of the data. We then demonstrate how we have used the same framework to derive a novel data-driven model of joint color change in images due to common lighting variations. The model is superior to previous models of color change in describing non-linear color changes due to lighting

Effect of practical layered dielectric loads on SAR patterns from dual concentric conductor microstrip antennas

Radiation patterns of 2 and 4cm square Dual Concentric Conductor (DCC) microstrip antennas were studied theoretically with Finite Difference Time Domain (FDTD) analysis and compared with experimental measurements of power deposition (SAR) in layered lossy dielectric loads. Single and array configurations were investigated with 915 MHz excitation applied across either one, two or four sides, or four corners of the square apertures. FDTD simulations were carried out for realistic models of a muscle tissue load coupled to the DCC antennas with a 5 mm thick bolus of either distilled water or low loss Silicone Oil. This study characterizes the effect on SAR of adding three additional thin dielectric layers which are necessary for clinical use of the applicator. These layers consist of a 0.1 mm thick dielectric coating on the array surface to provide electrical isolation of DCC apertures, and 0.15 mm thick plastic layers above and below the bolus to contain the liquid. Experimental measurements of SAR in a plane 1 cm deep in muscle phantom agree well with theoretical FDTD simulations in the multi-layered tissue models. These studies reveal significant changes in SAR for applicator configurations involving low dielectric constant (Er) layers on either side of a high Er water bolus layer. Prominent changes include a broadening and centring of the SAR under each aperture as well as increased SAR penetration in muscle. No significant differences are noted between the simple and complete load configurations for the low Er Silicone Oil bolus. Both theoretical and measured data demonstrate relatively uniform SAR distributions with50% of maximum SAR extending to the perimeter of single and multi-aperture array configurations of DCC applicators when using a thin 5 mm water or Silicone Oil bolus

Finding the optimal background subtraction algorithm for EuroHockey 2015 video

Background subtraction is a classic step in a vision-based localization and tracking workflow. Previous studies have compared background subtraction algorithms on publicly available datasets; however comparisons were made only with manually optimized parameters. The aim of this research was to identify the optimal background subtraction algorithm for a set of field hockey videos captured at EuroHockey 2015. Particle Swarm Optimization was applied to find the optimal background subtraction algorithm. The objective function was the F-score, i.e. the harmonic mean of precision and recall. The precision and recall were calculated using the output of the background subtraction algorithm and gold standard labeled images. The training dataset consisted of 15 x 13 second field hockey video segments. The test data consisted of 5 x 13 second field hockey video segments. The video segments were chosen to be representative of the teams present at the tournament, the times of day the matches were played and the weather conditions experienced. Each segment was 960 pixels x 540 pixels and had 10 ground truth labeled frames. Eight commonly used background subtraction algorithms were considered. Results suggest that a background subtraction algorithm must use optimized parameters for a valid comparison of performance. Particle Swarm Optimization is an appropriate method to undertake this optimization. The optimal algorithm, Temporal Median, achieved an F-score of 0.791 on the test dataset, suggesting it generalizes to the rest of the video footage captured at EuroHockey 2015

The Lantern Vol. 63, No. 1, Fall 1995

• The Birthday Celebration • Surprise! Surprise! • Oregold • Future of Parenthood #2 • Seeds • How I Spent My Summer Vacation • Random Scenes From 1/2 Hour at Work • Life in the Coal Mines • Driveway • Midnight in the Court of Kings • The Black Quadrilateral • People I Hate to See, But Refuse to Dismiss • Metropolized • Poetry in Motion • Dream #3 • Rhythms • Mercykilling • Untitled • Lupine Lord • At the Bottom of the Cup • House of Commons • Poetry I Can\u27t Standhttps://digitalcommons.ursinus.edu/lantern/1147/thumbnail.jp