Publication Date: 2007
IEEE Transactions on Consumer Electronics (00983063)53(3)pp. 1117-1124
A new motion compensated deinterlacing method using forward and backward motion estimation is proposed in this paper. Bi-directional motion estimation is performed using two previous and two subsequent fields. The motion estimator uses pre-filtering prior to motion estimation for the current and the subsequent two fields. The motion estimator finds a single optimal matching block in the same or opposite parity reference fields. Motion compensation is done according to the amount of vertical motion within the reference fields to achieve the highest vertical resolution improvement. A novel technique to prevent the appearance of visual artifacts in the presence of fast-moving objects is proposed. Experimental results show that the proposed method performs better than the conventional deinterlacing methods, based on objective and subjective criteria. © 2007 IEEE.
Block matching has been widely used for block motion estimation; however most of the block matching algorithms impose heavy computational load to the system, and require much time for execution. This problem prevents using them in time critical applications. In this paper, a new approach to block matching technique is presented, which has small computational complexity as well as high accuracy. The main assumption of the algorithm is that, all the pixels of a block move equally by a linear motion. Experimental results show the feasibility and effectiveness of the proposed algorithm. © 2008 IEEE.
In this paper, a new robust digital image watermarking algorithm based on Joint DWT-DCT Transformation is proposed. The imperceptibility is provided as well as higher robustness against common signal processing attacks. A binary watermarked image is embedded in certain sub-bands of a 3-level DWT transformed of a host image. Then, DCT transform of each selected DWT sub-band is computed and the PN-sequences of the watermark bits are embedded in the coefficients of the corresponding DCT middle frequencies. In extraction stages, the watermarked image, which maybe attacked, is first preprocessed by sharpening and Laplassian of Gaussian filters. Then, the same approach as the embedding process is used to extract the DCT middle frequencies of each sub-band. Finally, correlation between mid-band coefficients and PN-sequences is calculated to determine watermarked bits. Experimental results show that the proposed method improved the performance of the watermarking algorithms which are based on the joint of DWT-DCT. © 2008 IEEE.
This paper presents a new robust digital image watermarking technique based on Discrete Cosine Transform (DCT) and neural network. The neural network is Full Counter propagation Neural Network (FCNN). FCNN has been used to simulate the perceptual and visual characteristics of the original image. The perceptual features of the original image have been used to determine the highest changeable threshold values of DCT coefficients. The highest changeable threshold values have been used to embed the watermark in DCT coefficients of the original image. The watermark is a binary image. The pixel values of this image are inserted as zero and one values in the DCT coefficients of the image. The implementation results have shown that this watermarking algorithm has an acceptable robustness versus different kinds of watermarking attacks. © 2008 IEEE.
Background estimation is one of the most challenging phases in extracting foreground objects from video sequences. In this paper we present a background modeling approach that uses the similarity of frames to extract background areas from the video sequence. We use a window over the frames history and compute the similarity between the selected frames of this window as a similarity window. The properties of similarity window depend on the characteristics of the scene and can be adjusted parametrically. Our primary results show that if proper parameters are chosen, this method can give a good approximation of the background model. ©2008 IEEE.
Publication Date: 2008
Eurasip Journal on Advances in Signal Processing (16876172)2008
An electrocardiogram (ECG) beat classification scheme based on multiple signal classification (MUSIC) algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP) neural network and a probabilistic neural network (PNN), are employed. The experimental results achieved a promising accuracy of 99.03 for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100 accuracy and never misclassifies any other classes as NORMAL.
