3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform

Significant geometric structures can be compactly described by global wireframes in the estimation of 3D room layout from a single panoramic image. Based on this observation, we present an alternative approach to estimate the walls in 3D space by modeling long-range geometric patterns in a learnable Hough Transform block. We transform the image feature from a cubemap tile to the Hough space of a Manhattan world and directly map the feature to the geometric output. The convolutional layers not only learn the local gradient-like line features, but also utilize the global information to successfully predict occluded walls with a simple network structure. Unlike most previous work, the predictions are performed individually on each cubemap tile, and then assembled to get the layout estimation. Experimental results show that we achieve comparable results with recent state-of-the-art in prediction accuracy and performance. Code is available at https://github.com/Starrah/DMH-Net.Comment: Accepted by ECCV 202

First Evaluation of the CPU, GPGPU and MIC Architectures for Real Time Particle Tracking based on Hough Transform at the LHC

Recent innovations focused around {\em parallel} processing, either through systems containing multiple processors or processors containing multiple cores, hold great promise for enhancing the performance of the trigger at the LHC and extending its physics program. The flexibility of the CMS/ATLAS trigger system allows for easy integration of computational accelerators, such as NVIDIA's Tesla Graphics Processing Unit (GPU) or Intel's \xphi, in the High Level Trigger. These accelerators have the potential to provide faster or more energy efficient event selection, thus opening up possibilities for new complex triggers that were not previously feasible. At the same time, it is crucial to explore the performance limits achievable on the latest generation multicore CPUs with the use of the best software optimization methods. In this article, a new tracking algorithm based on the Hough transform will be evaluated for the first time on a multi-core Intel Xeon E5-2697v2 CPU, an NVIDIA Tesla K20c GPU, and an Intel \xphi\ 7120 coprocessor. Preliminary time performance will be presented.Comment: 13 pages, 4 figures, Accepted to JINS

Massively Parallel Computing and the Search for Jets and Black Holes at the LHC

Massively parallel computing at the LHC could be the next leap necessary to reach an era of new discoveries at the LHC after the Higgs discovery. Scientific computing is a critical component of the LHC experiment, including operation, trigger, LHC computing GRID, simulation, and analysis. One way to improve the physics reach of the LHC is to take advantage of the flexibility of the trigger system by integrating coprocessors based on Graphics Processing Units (GPUs) or the Many Integrated Core (MIC) architecture into its server farm. This cutting edge technology provides not only the means to accelerate existing algorithms, but also the opportunity to develop new algorithms that select events in the trigger that previously would have evaded detection. In this article we describe new algorithms that would allow to select in the trigger new topological signatures that include non-prompt jet and black hole--like objects in the silicon tracker.Comment: 15 pages, 11 figures, submitted to NIM

A Masked Bounding-Box Selection Based ResNet Predictor for Text Rotation Prediction

The existing Optical Character Recognition (OCR) systems are capable of recognizing images with horizontal texts. However, when the rotation of the texts increases, it becomes harder to recognizing these texts. The performance of the OCR systems decreases. Thus predicting the rotations of the texts and correcting the images are important. Previous work mainly uses traditional Computer Vision methods like Hough Transform and Deep Learning methods like Convolutional Neural Network. However, all of these methods are prone to background noises commonly existing in general images with texts. To tackle this problem, in this work, we introduce a new masked bounding-box selection method, that incorporating the bounding box information into the system. By training a ResNet predictor to focus on the bounding box as the region of interest (ROI), the predictor learns to overlook the background noises. Evaluations on the text rotation prediction tasks show that our method improves the performance by a large margin