Non Identical Duplicate Video Detection using SIFT Method

Non-Identical Duplicate video detection is a challenging research problem. Non-Identical Duplicate video are a pair of videos that are not exactly identical but are almost similar.In this paper, we evaluate two methods - Keyframe -based and Tomography-based methods to determine the Non-Identical Duplicate videos. These two methods make use of the existing scale based shift invariant (SIFT) method to find the match between the key frames in first method, and the cross-sections through the temporal axis of the videos in second method.We provide extensive experimental results and the analysis of accuracy and efficiency of the above two methods on a data set of Non- Identical Duplicate video-pair.

A New Iterative FIR Filter for Image and Video Restoration

Image and video filtering is a key image-processing task in computer vision especially in noisy environment. In most of the cases the noise source is unknown and hence possess a major difficulty in the filtering operation. In this paper we present an error-correction based learning approach for iterative filtering. A new FIR filter is designed in which the filter coefficients are updated based on Widrow-Hoff rule. Unlike the standard filter the proposed filter has the ability to remove noise without the a priori knowledge of the noise. Experimental result shows that the proposed filter efficiently removes the noise and preserves the edges in the image. We demonstrate the capability of the proposed algorithm by testing it on standard images infected by Gaussian noise and on a real time video containing inherent noise. Experimental result shows that the proposed filter is better than some of the existing standard filters

Evaluation of 2D image reconstruction using fan-beam FBP algorithm with no backprojection weight from WLP completed truncated projection data

This paper presents the image reconstruction using the fan-beam filtered backprojection (FBP) algorithm with no backprojection weight from windowed linear prediction (WLP) completed truncated projection data. The image reconstruction from truncated projections aims to reconstruct the object accurately from the available limited projection data. Due to the incomplete projection data, the reconstructed image contains truncation artifacts which extends into the region of interest (ROI) making the reconstructed image unsuitable for further use. Data completion techniques have been shown to be effective in such situations. We use windowed linear prediction technique for projection completion and then use the fan-beam FBP algorithm with no backprojection weight for the 2-D image reconstruction. We evaluate the quality of the reconstructed image using fan-beam FBP algorithm with no backprojection weight after WLP completion.

On the Implementation of a Streaming Video over Peer to Peer network using Middleware Components

Video streaming applications have hitherto been supported by single server systems. A major drawback of such a solution is that it increases the server load. The server restricts the number of clients that can be simultaneously supported due to limitation in bandwidth. The constraints of a single server system can be overcome in video streaming if we exploit the endless resources available in a distributed and networked system. We explore a P2P system for streaming video applications. In this paper we build a P2P streaming video (SVP2P) service in which multiple peers co-operate to serve video segments for new requests, thereby reducing server load and bandwidth used. Our simulation shows the playback latency using SVP2P is roughly 1/4th of the latency incurred when the server directly streams the video. Bandwidth consumed for control messages (overhead) is as low as 1.5% of the total data transfered. The most important observation is that the capacity of the SVP2P grows dynamically.

Fuzzy inference based data preprocessing for VBR video traffic prediction

Prediction of variable bit rate compressed video traffic is critical to dynamic allocation of resources in a network. In this paper, we propose a technique for preprocessing the dataset used for training a video traffic predictor. The technique involves identifying the noisy instances in the data using a fuzzy inference system. We focus on three prediction techniques, namely, linear regression, neural network and support vector regression and analyze their performance on H.264 video traces. Our experimental results reveal that data preprocessing greatly improves the performance of linear regression and neural network, but is not effective on support vector regression.

A numerical study of variable coefficient elliptic Cauchy problem via projection method

In this paper, we investigate a numerical method for the solution of an inverse problem of recovering lacking data on some part of the boundary of a domain from the Cauchy data on other part for a variable coefficient elliptic Cauchy problem. In the process, the Cauchy problem is transformed into the problem of solving a compact linear operator equation. As a remedy to the ill-posedness of the problem, we use a projection method which allows regularization solely by discretization. The discretization level plays the role of regularization parameter in the case of projection method. The balancing principle is used for the choice of an appropriate discretization level. Several numerical examples show that the method produces a stable good approximate solution.

Monsoon circulation interaction with Western Ghats orography under changing climate - Projection by a 20-km mesh AGCM

In this study, the authors have investigated the likely future changes in the summer monsoon over the Western Ghats (WG) orographic region of India in response to global warming, using time-slice simulations of an ultra high-resolution global climate model and climate datasets of recent past. The model with approximately 20-km mesh horizontal resolution resolves orographic features on finer spatial scales leading to a quasi-realistic simulation of the spatial distribution of the present-day summer monsoon rainfall over India and trends in monsoon rainfall over the west coast of India. As a result, a higher degree of confidence appears to emerge in many aspects of the 20-km model simulation, and therefore, we can have better confidence in the validity of the model prediction of future changes in the climate over WG mountains. Our analysis suggests that the summer mean rainfall and the vertical velocities over the orographic regions of Western Ghats have significantly weakened during the recent past and the model simulates these features realistically in the present-day climate simulation. Under future climate scenario, by the end of the twenty-first century, the model projects reduced orographic precipitation over the narrow Western Ghats south of 16A degrees N that is found to be associated with drastic reduction in the southwesterly winds and moisture transport into the region, weakening of the summer mean meridional circulation and diminished vertical velocities. We show that this is due to larger upper tropospheric warming relative to the surface and lower levels, which decreases the lapse rate causing an increase in vertical moist static stability (which in turn inhibits vertical ascent) in response to global warming. Increased stability that weakens vertical velocities leads to reduction in large-scale precipitation which is found to be the major contributor to summer mean rainfall over WG orographic region. This is further corroborated by a significant decrease in the frequency of moderate-to-heavy rainfall days over WG which is a typical manifestation of the decrease in large-scale precipitation over this region. Thus, the drastic reduction of vertical ascent and weakening of circulation due to `upper tropospheric warming effect' predominates over the `moisture build-up effect' in reducing the rainfall over this narrow orographic region. This analysis illustrates that monsoon rainfall over mountainous regions is strongly controlled by processes and parameterized physics which need to be resolved with adequately high resolution for accurate assessment of local and regional-scale climate change.

GFT projection NMR for efficient H-1/C-13 sugar spin system identification in nucleic acids

A newly implemented G-matrix Fourier transform (GFT) (4,3)D HC(C)CH experiment is presented in conjunction with (4,3)D HCCH to efficiently identify H-1/C-13 sugar spin systems in C-13 labeled nucleic acids. This experiment enables rapid collection of highly resolved relay 4D HC(C)CH spectral information, that is, shift correlations of C-13-H-1 groups separated by two carbon bonds. For RNA, (4,3)D HC(C)CH takes advantage of the comparably favorable 1'- and 3'-CH signal dispersion for complete spin system identification including 5'-CH. The (4,3)D HC(C)CH/HCCH based strategy is exemplified for the 30-nucleotide 3'-untranslated region of the pre-mRNA of human U1A protein.

USHA: unified software and hardware architecture for video decoding

Video decoders used in emerging applications need to be flexible to handle a large variety of video formats and deliver scalable performance to handle wide variations in workloads. In this paper we propose a unified software and hardware architecture for video decoding to achieve scalable performance with flexibility. The light weight processor tiles and the reconfigurable hardware tiles in our architecture enable software and hardware implementations to co-exist, while a programmable interconnect enables dynamic interconnection of the tiles. Our process network oriented compilation flow achieves realization agnostic application partitioning and enables seamless migration across uniprocessor, multi-processor, semi hardware and full hardware implementations of a video decoder. An application quality of service aware scheduler monitors and controls the operation of the entire system. We prove the concept through a prototype of the architecture on an off-the-shelf FPGA. The FPGA prototype shows a scaling in performance from QCIF to 1080p resolutions in four discrete steps. We also demonstrate that the reconfiguration time is short enough to allow migration from one configuration to the other without any frame loss.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

Projection-based atom selection in orthogonal matching pursuit for compressive sensing

For compressive sensing, we endeavor to improve the atom selection strategy of the existing orthogonal matching pursuit (OMP) algorithm. To achieve a better estimate of the underlying support set progressively through iterations, we use a least squares solution based atom selection method. From a set of promising atoms, the choice of an atom is performed through a new method that uses orthogonal projection along-with a standard matched filter. Through experimental evaluations, the effect of projection based atom selection strategy is shown to provide a significant improvement for the support set recovery performance, in turn, the compressive sensing recovery.

CAVVA: Computational Affective Video-in-Video Advertising

Advertising is ubiquitous in the online community and more so in the ever-growing and popular online video delivery websites (e. g., YouTube). Video advertising is becoming increasingly popular on these websites. In addition to the existing pre-roll/post-roll advertising and contextual advertising, this paper proposes an in-stream video advertising strategy-Computational Affective Video-in-Video Advertising (CAVVA). Humans being emotional creatures are driven by emotions as well as rational thought. We believe that emotions play a major role in influencing the buying behavior of users and hence propose a video advertising strategy which takes into account the emotional impact of the videos as well as advertisements. Given a video and a set of advertisements, we identify candidate advertisement insertion points (step 1) and also identify the suitable advertisements (step 2) according to theories from marketing and consumer psychology. We formulate this two part problem as a single optimization function in a non-linear 0-1 integer programming framework and provide a genetic algorithm based solution. We evaluate CAVVA using a subjective user-study and eye-tracking experiment. Through these experiments, we demonstrate that CAVVA achieves a good balance between the following seemingly conflicting goals of (a) minimizing the user disturbance because of advertisement insertion while (b) enhancing the user engagement with the advertising content. We compare our method with existing advertising strategies and show that CAVVA can enhance the user's experience and also help increase the monetization potential of the advertising content.