Damaged watermarks detection in frequency domain as a primary method for video concealment

This paper deals with video transmission over lossy communication networks. The main idea is to develop video concealment method for information losses and errors correction. At the beginning, three main groups of video concealment methods, divided by encoder/decoder collaboration, are briefly described. The modified algorithm based on the detection and filtration of damaged watermark blocks encapsulated to the transmitted video was developed. Finally, the efficiency of developed algorithm is presented in experimental part of this paper

Alternating Least-Squares for Low-Rank Matrix Reconstruction

For reconstruction of low-rank matrices from undersampled measurements, we develop an iterative algorithm based on least-squares estimation. While the algorithm can be used for any low-rank matrix, it is also capable of exploiting a-priori knowledge of matrix structure. In particular, we consider linearly structured matrices, such as Hankel and Toeplitz, as well as positive semidefinite matrices. The performance of the algorithm, referred to as alternating least-squares (ALS), is evaluated by simulations and compared to the Cram\'er-Rao bounds.Comment: 4 pages, 2 figure

A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method

In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods

On the Effects of Sender-Receiver Concealment Mismatch on Multimedia Communication Optimization

A large number of performance optimization algorithms for multimedia communications, including rate-distortion optimized schemes, rely on knowing the decoder behavior in case of data loss, i.e., the decoder-side error concealment technique. However, for the specific case of video coding, standards do not specify it, thus different decoders may - and typically do - use different concealment techniques. This work investigates the impact of assuming, in the transmission optimization phase, a concealment algorithm different from the one that is actually used by the decoder, in order to determine which are the best assumptions to use at the transmitter. Firstly, we investigate the typical performance provided by ten concealment techniques belonging to three widely used algorithmic families (spatial, temporal and mixed). Then, we assess the impact that an incorrect concealment assumption causes, in terms of both packet transmission policy changes and video quality degradation, using a simple rate-distortion transmission optimization technique that targets a generic two QoS-level network. Simulation results over several standard video sequences show that the performance impact of incorrectly assuming the decoder-side concealment technique may be significant but it is limited if the two techniques belong to the same algorithmic family. Moreover, the impact on performance caused by incorrect assumptions is strongly mitigated if the decoder employs a high-performance concealment algorithm. Finally, the impact on the performance of several parameters such as the encoding pattern, the packet loss statistics (uniform and burst losses) and the amount of high-priority traffic is evaluated, showing that the conclusions can be confidently applied to actual multimedia communication scenarios

Adaptive sub-channel allocation based UEP for video transmission in space-time coded OFDM systems

In this work, we introduce the idea of adaptive sub-channel allocation based unequal error protection (ASCA-UEP) to a space-time block coded orthogonal frequency division multiplexing (STBC-OFDM) system. In such a system, UEP is realized by adaptively allocating and transmitting high-priority and low-priority video data over high-quality and low-quality sub-channels, respectively. Further, we propose two ASCA-UEP schemes in a time division duplex (TDD) system: a receiver-based scheme and a transmitter-based scheme. Analysis and simulation results demonstrate that ASCA-UEP greatly enhances the quality of video reception, and the transmitter-based scheme is more robust to uplink channel noise than the receiver-based scheme, and is thus preferred when the receiver is power-constrained and the transmitter has sufficient power.published_or_final_versio

An efficient P-KCCA algorithm for 2D-3D face recognition using SVM

In this paper, a novel face recognition system for face recognition and identification based on a combination of Principal Component Analysis and Kernel Canonical Correlation Analysis (P-KCCA) using Support Vector Machine (SVM) is proposed. First, the P-KCCA method is utilized to detect and extract the important features from the input images. This method makes it possible to match the 2D face image with enrolled 3D face data. The resulting features are then classified using the SVM method. The proposed methods were tested on TEXAS database with 200 subjects. The experimental results in the TEXAS face database produce interesting results from the point of view of recognition success, rate, and robustness of the face recognition algorithm. We compare the performance of our proposed face recognition method to other commonly-used methods. The experimental results show that the combination of P-KCCA method using SVM achieves a higher performance compared to the alone PCA, CCA and KCCA algorithms

A Novel System for Non-Invasive Method of Animal Tracking and Classification in Designated Area Using Intelligent Camera System

This paper proposed a novel system for non-invasive method of animal tracking and classification in designated area. The system is based on intelligent devices with cameras, which are situated in a designated area and a main computing unit (MCU) acting as a system master. Intelligent devices track animals and then send data to MCU to evaluation. The main purpose of this system is detection and classification of moving animals in a designated area and then creation of migration corridors of wild animals. In the intelligent devices, background subtraction method and CAMShift algorithm are used to detect and track animals in the scene. Then, visual descriptors are used to create representation of unknown objects. In order to achieve the best accuracy in classification, key frame extraction method is used to filtrate an object from detection module. Afterwards, Support Vector Machine is used to classify unknown moving animals

User-Oriented QoS in Packet Video Delivery

We focus on packet video delivery, with an emphasis on the quality of service perceived by the end-user. A video signal passes through several subsystems, such as the source coder, the network and the decoder. Each of these can impair the information, either by data loss or by introducing delay. We describe how each of the subsystems can be tuned to optimize the quality of the delivered signal, for a given available bit rate in the network. The assessment of end-user quality is not trivial. We present recent research results, which rely on a model of the human visual system