Realtime Video Classification Using Dense HOF/HOG

ABSTRACT The current state-of-the-art in Video Classification is based on Bag-of-Words using local visual descriptors. Most commonly these are Histogram of Oriented Gradient (HOG) and Histogram of Optical Flow (HOF) descriptors. While such system is very powerful for classification, it is also computationally expensive. This paper addresses the problem of computational efficiency. Specifically: (1) We propose several speed-ups for densely sampled HOG and HOF descriptors and release Matlab code. (2) We investigate the trade-off between accuracy and computational efficiency of descriptors in terms of frame sampling rate and type of Optical Flow method. (3) We investigate the trade-off between accuracy and computational efficiency for the video representation, using either a k-means or hierarchical k-means based visual vocabulary, a Random Forest based vocabulary or the Fisher kernel

The ALICE experiment at the CERN LHC

ALICE (A Large Ion Collider Experiment) is a general-purpose, heavy-ion detector at the CERN LHC which focuses on QCD, the strong-interaction sector of the Standard Model. It is designed to address the physics of strongly interacting matter and the quark-gluon plasma at extreme values of energy density and temperature in nucleus-nucleus collisions. Besides running with Pb ions, the physics programme includes collisions with lighter ions, lower energy running and dedicated proton-nucleus runs. ALICE will also take data with proton beams at the top LHC energy to collect reference data for the heavy-ion programme and to address several QCD topics for which ALICE is complementary to the other LHC detectors. The ALICE detector has been built by a collaboration including currently over 1000 physicists and engineers from 105 Institutes in 30 countries. Its overall dimensions are 161626 m3 with a total weight of approximately 10 000 t. The experiment consists of 18 different detector systems each with its own specific technology choice and design constraints, driven both by the physics requirements and the experimental conditions expected at LHC. The most stringent design constraint is to cope with the extreme particle multiplicity anticipated in central Pb-Pb collisions. The different subsystems were optimized to provide high-momentum resolution as well as excellent Particle Identification (PID) over a broad range in momentum, up to the highest multiplicities predicted for LHC. This will allow for comprehensive studies of hadrons, electrons, muons, and photons produced in the collision of heavy nuclei. Most detector systems are scheduled to be installed and ready for data taking by mid-2008 when the LHC is scheduled to start operation, with the exception of parts of the Photon Spectrometer (PHOS), Transition Radiation Detector (TRD) and Electro Magnetic Calorimeter (EMCal). These detectors will be completed for the high-luminosity ion run expected in 2010. This paper describes in detail the detector components as installed for the first data taking in the summer of 2008

The Impact Of Introducing Virtual Slides As A Replacement For Powerpoint Presentations In The Students’ Microscopy Labs

INTRODUCTION / BACKGROUND: The medical school students in Timisoara, Romania have been studying pathology slides in microscopy labs according to a protocol which uses classical PowerrPoint presentations as guides for understanding the microscopic features of diseases, followed by individual examination of the glass slides under the microscope. AIMS: We aimed to assess the impact of replacing those presentations with virtual slides (VS). METHODS: In the middle of the semester, for the benign tumors microscopy lab, which is presented over the course of 2 weeks, we used 3 VS, while the other 3 slides were presented in the classical PowerPoint manner. All attending students from the 3rd year of the Medical School of the University of Medicine and Pharmacy “Victor Babes” Timisoara were asked to fill out an anonymous questionnaire at the end of the lab, in which they graded the difficulty in identifying lesions, chose the best/least understood lesion and pointed out the best manner of presentation. RESULTS: 431 valid questionnaires were collected. 52.9% of the students indicated one of the 3 VS as the best understood lesion, while 59.62% chose a different VS as a least understood one. One VS was also the top best (113/332 votes) while another the least understood (34/126 votes) lesion. 74.01% students agreed that VS helped them understand the microscopic criteria better, while 74.71% would like VS to be used in the labs to come. CONCLUSION: VS were appreciated by the students as a novelty and a more impressing way of studying pathology slides, but did not dramatically improve the easiness with which they identify and understand the lesions