REAL-TIME ARBITRARY VIEW RENDERING ON GPU FROM STEREO VIDEO AND TIME-OF-FLIGHT CAMERA

Generating in-between images from multiple views of a scene is a crucial task for both computer vision and computer graphics fields. Photorealistic rendering, 3DTV and robot navigation are some of many applications which benefit from arbitrary view synthesis, if it is achieved in real-time. GPUs excel in achieving high computation power by processing arrays of data in parallel, which make them ideal for real-time computer vision applications. This paper proposes an arbitrary view rendering algorithm by using two high resolution color cameras along with a single low resolution time-of-flight depth camera and utilizing GPUs to achieve realtime processing rates. The presented ideas are examined in an experimental framework and based on the experimental results, it could be concluded that it is possible to realize content production and display stages of a free-viewpoint system in real-time by using only low-cost commodity computing devices

Comparison of Quantization Index Modulation and Forbidden Zone Data Hiding for Compressed Domain Video Data Hiding

Data hiding is now a part of daily life through various applications. In this work, we apply two data hiding methods, Quantization Index Modulation and Forbidden Zone Data Hiding, to video applications. We place these methods into a general video data hiding scheme and compare their performance against compression attacks. The results of the experiments with typical TV content indicate the superiority of FZDH, specifically for powerful attacks

Boundary Matching Based Translucent TV Logo Detection

Increasing trend in the usage of translucent television logos by broadcast channels renders opaque logo detection algorithms inadequate. Important applications such as identification of broadcast types make the detection of translucent logos a significant requirement. This paper presents a method for detecting translucent television logos in video streams. Firstly, boundary information of the logo, which will be searched in broadcast stream, is extracted manually. This search is carried out by comparing the edge map of the luminance channel of the interest region with inner and outer contours of the logo using different metrics. Performance is increased by utilization of temporal redundancies and solutions to special problematic cases. Furthermore, traces of the logo boundaries are examined in chrominance channels of video frames in order to eliminate false alarms caused by opaque logos with the same boundaries. Promising results indicate the applicability of the method in real life

A fast method for animated TV logo detection

As a recent trend some TV stations prefer to use animated logos, therefore the detection of the presence of an animated TV logo emerges as a new requirement for certain applications. In this paper we present a novel method for the detection of animated television logos in real-time. The main idea is to handle all frames of the animated logo in a unified manner. For this purpose a unified logo boundary representation is utilized. In the training stage, the boundaries of the animated logo from each frame are placed in a single set. During detection, a voting-based decision scheme is performed in order to determine the presence of the trained logo. Furthermore robustness of the method is improved by incorporating negative clues regarding the existence of the animated logo obtained from the region of interest. Aforementioned clues are unified in order to reach a final decision by using effective combination rules. Finally, time windowing is used for eliminating false positives with short durations. The proposed method is examined through typical broadcast data and promising results are obtained

Content Based Video Copy Detection with Coarse Features

Content Based Copy Detection is an alternative approach to invisible watermarking for tracking duplicate data. Primary stages are creating a database using the features belonging to the original data and searching query data in terms of its features in this database. Features must be robust against targeted attacks and discriminative enough to distinguish different content. In this work, we propose reducing the precision of feature values to attain robustness and increasing the number and dimension of features to attain discriminativity. To this end, we create a feature database using different features, which correspond to different information sources, together. We detect the original sources of the query videos in this database. which is composed of coarse features, by feature comparison. Effectiveness of the proposed method against various attacks is observed through experiments

SPEECH DETECTION ON BROADCAST AUDIO

Speech boundary detection contributes to performance of speech based applications such as speech recognition and speaker recognition. Speech boundary detector implemented in this study works on broadcast audio as a pre-processor module of a keyword spotter. Speech boundary detection is handled in 3 steps. At first step, audio data is segmented into homogeneous regions in an unsupervised manner. After an ACTIVITY/NON-ACTIVITY decision is made for each region, ACTIVITY regions are classified as Speech/Non-speech via Gaussian Mixture Model (GMM) based classification. GMM's are trained using a novel feature, Spectral Flow Direction (SFD), and an improved multi-band harmonicity feature in addition to widely used Mel Frequency Cepstral Coefficients (MFCC's)

Content Based Copy Detection with Coarse Audio-Visual Fingerprints

Content based copy detection (CBCD) emerges as a viable choice against active detection methodology of watermarking. The very first reason is that the media already under circulation cannot be marked and secondly, CBCD inherently can endure various severe attacks, which watermarking cannot. Although in general, media content is handled independently as visual and audio in this work both information sources are utilized in a unified framework, in which coarse representation of fundamental features are employed. From the copy detection perspective, number of attacks on audio content is limited with respect to visual case. Therefore audio, if present, is an indispensable part of a robust video copy detection system. In this study, the validity of this statement is presented through various experiments on a large data set

Multimodal concept detection in broadcast media: KavTan

Concept detection stands as an important problem for efficient indexing and retrieval in large video archives. In this work, the KavTan System, which performs high-level semantic classification in one of the largest TV archives of Turkey, is presented. In this system, concept detection is performed using generalized visual and audio concept detection modules that are supported by video text detection, audio keyword spotting and specialized audio-visual semantic detection components. The performance of the presented framework was assessed objectively over a wide range of semantic concepts (5 high-level, 14 visual, 9 audio, 2 supplementary) by using a significant amount of precisely labeled ground truth data. KavTan System achieves successful high-level concept detection performance in unconstrained TV broadcast by efficiently utilizing multimodal information that is systematically extracted from both spatial and temporal extent of multimedia data

Infections, antibiotic treatment and mortality in patients admitted to ICUs in countries considered to have high levels of antibiotic resistance compared to those with low levels

Background: Antimicrobial resistance is an increasing concern in ICUs worldwide. Infection with an antibiotic resistant (ABR) strain of an organism is associated with greater mortality than infection with the non-resistant strain, but there are few data assessing whether being admitted to an intensive care unit (ICU) with high levels of antimicrobial resistance is associated with a worse outcome than being admitted to an ICU with low rates of resistance. The aim of this study was, therefore, to compare the characteristics of infections and antibiotic treatments and patient outcomes in patients admitted to ICUs in countries considered as having high levels of antibiotic resistance and those admitted to ICUs in countries considered as having low levels of antibiotic resistance. Methods: Data from the large, international EPIC II one-day point prevalence study on infections in patients hospitalized in ICUs were used. For the current study, we compared the data obtained from patients from two groups of countries: countries with reported MRSA rates of greater than= 25% (highABR: Greece, Israel, Italy, Malta, Portugal, Spain, and Turkey) and countries with MRSA rates of less than 5% (lowABR: Denmark, Finland, Netherlands, Norway, and Sweden). Results: On the study day, 1187/2204 (53.9%) patients in the HighABR ICUs were infected and 255/558 (45.7%) in the LowABR ICUs (P less than 0.01). Patients in the HighABR ICUs were more severely ill than those in the LowABR ICUs, as reflected by a higher SAPS II score (35.6 vs 32.7, P less than 0.05) and had longer median ICU (12 days vs 5 days) and hospital (24 days vs 16 days) lengths of stay. They also had higher crude ICU (20.0% vs 15.4%) and hospital (27.0% vs 21.5%) mortality rates (both P less than 0.05). However, after multivariable adjustment and matched pair analysis there were no differences in ICU or hospital mortality rates between High or LowABR ICU patients overall or among those with infections. Conclusions: Being hospitalized in an ICU in a region with high levels of antimicrobial resistance is not associated per se with a worse outcome

Infections, antibiotic treatment and mortality in patients admitted to ICUs in countries considered to have high levels of antibiotic resistance compared to those with low levels

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