Sharif Digital Repository

Error concealment is a useful method for improving the damaged video quality in the decoder side. In this paper, a dynamic method with low computational complexity is presented to improve the visual quality of videos when up to 50% of the frames are damaged. In the proposed method, temporal replacement and the improved outer boundary matching algorithm are used for dynamical error concealment in inter-frames of videos. With the use of motion vectors (MVs) which are close to the damaged macroblock (MB) the method can determine whether the motion in specific areas is either regular, irregular, or zero. Then, based on this knowledge, different methods are performed. It adaptively selects a set... 

Nowadays, the use of multimedia services such as video sequences is constantly growing. Unfortunately, due to the lack of reliable communication channels and video data sensitivity to transmission errors, the quality of received video might decrease. Therefore, decoder error concealment methods have been developed to retrieve the damaged or lost data. In this paper, a novel temporal error concealment (TEC) algorithm based on moment invariants is presented. It includes three main stages of: designation of candidate motion vectors (MVs) set, adaptive determination of block size in the current and reference frames for feature extraction, and error function calculation based on moment... 

While visual target tracking is one of the noteworthy and the most active research areas in computer vision and machine learning, many challenges are still unresolved. In this paper, an adaptive generic target tracker is proposed that includes the adaptive determination of learning parameters from spatio-temporal context model, analysis of prior targets and confidence map for accurate target localization, and modified scale estimation scheme based on confidence map. According to spatio-temporal context model, the learning parameters are adaptively determined for achieving confidence map and target scale robustly. Moreover, analysis of the confidence map helps our tracker to change context... 

In recent years, visual tracking methods that are based on discriminative correlation filters (DCFs) have been very promising. However, most of these methods suffer from a lack of robust scale estimation skills. Although a wide range of recent DCF-based methods exploit the features that are extracted from deep convolutional neural networks (CNNs) in their translation model, the scale of the visual target is still estimated by hand-crafted features. Whereas the exploitation of CNNs imposes a high computational burden, this paper exploits pre-trained lightweight CNNs models to propose two efficient scale estimation methods, which not only improve the visual tracking performance but also... 

Due to the automatic feature extraction procedure via multi-layer nonlinear transformations, the deep learning-based visual trackers have recently achieved a great success in challenging scenarios for visual tracking purposes. Although many of those trackers utilize the feature maps from pre-trained convolutional neural networks (CNNs), the effects of selecting different models and exploiting various combinations of their feature maps are still not compared completely. To the best of our knowledge, all those methods use a fixed number of convolutional feature maps without considering the scene attributes (e.g., occlusion, deformation, and fast motion) that might occur during tracking. As a... 

In this paper, the performance of the gradient method based on Score Function Difference (SFD) in the separation of i.i.d. and periodic signals will be investigated. We will see that this algorithm will separate periodic signals better than i.i.d. ones. By using this experimental result and the fact that voiced frames of speech signals are approximately periodic, a modified algorithm named VDGaradient has been proposed for separation of speech signals in synthetic convolutive mixtures. In this method, voiced frames of speech signal will be used as the input to the gradient method, then the resulting separating system will be applied to separate sources completely  

Visual target tracking is one of the most sought-after yet challenging research topics in computer vision. Given the ill-posed nature of the problem and its popularity in a broad range of real-world scenarios, a number of large-scale benchmark datasets have been established, on which considerable methods have been developed and demonstrated with significant progress in recent years - predominantly by recent deep learning (DL)-based methods. This survey aims to systematically investigate the current DL-based visual tracking methods, benchmark datasets, and evaluation metrics. It also extensively evaluates and analyzes the leading visual tracking methods. First, the fundamental... 

Visual target tracking is one of the most sought-after yet challenging research topics in computer vision. Given the ill-posed nature of the problem and its popularity in a broad range of real-world scenarios, a number of large-scale benchmark datasets have been established, on which considerable methods have been developed and demonstrated with significant progress in recent years - predominantly by recent deep learning (DL)-based methods. This survey aims to systematically investigate the current DL-based visual tracking methods, benchmark datasets, and evaluation metrics. It also extensively evaluates and analyzes the leading visual tracking methods. First, the fundamental... 

We consider the problem of tracking an unknown small target from aerial videos of medium to high altitudes. This is a challenging problem, which is even more pronounced in unavoidable scenarios of drastic camera motion and high density. To address this problem, we introduce a context-aware IoU-guided tracker (COMET) that exploits a multitask two-stream network and an offline reference proposal generation strategy. The proposed network fully exploits target-related information by multi-scale feature learning and attention modules. The proposed strategy introduces an efficient sampling strategy to generalize the network on the target and its parts without imposing extra computational... 

Visual object tracking remains an active research field in computer vision due to persisting challenges with various problem-specific factors in real-world scenes. Many existing tracking methods based on discriminative correlation filters (DCFs) employ feature extraction networks (FENs) to model the target appearance during the learning process. However, using deep feature maps extracted from FENs based on different residual neural networks (ResNets) has not previously been investigated. This paper aims to evaluate the performance of 12 state-of-the-art ResNet-based FENs in a DCF-based framework to determine the best for visual tracking purposes. First, it ranks their best feature maps and... 

Visual object tracking remains an active research field in computer vision due to persisting challenges with various problem-specific factors in real-world scenes. Many existing tracking methods based on discriminative correlation filters (DCFs) employ feature extraction networks (FENs) to model the target appearance during the learning process. However, using deep feature maps extracted from FENs based on different residual neural networks (ResNets) has not previously been investigated. This paper aims to evaluate the performance of 12 state-of-the-art ResNet-based FENs in a DCF-based framework to determine the best for visual tracking purposes. First, it ranks their best feature maps and... 

Transcranial Doppler ultrasound can be used to detect circulating cerebral eraboli. Embolie signals have characteristic transient chirps suitable for wavelet analysis. We have implemented and evaluated the first online selective selective wavelet transient enhancement filter to amplify embolic signals in a preprocessing system. Our approach is similar to wavelet de-noising for signal enhancement, but, in order to retain blood flow information, we do not use traditional threshold methods. The selective wavelet amplifier uses the matched filter properties of wavelets to enhance embolic signals significantly and improve classification performance using a novel noise tolerant approach. Even the... 

In this paper a new geometrical approach for separating speech signals is presented. This approach can be directly applied to separate more than two speech signals. It is based on clustering the observation points, and then fitting a line (hyper-plane) onto each cluster. The algorithm quality is shown to be improved by using DCT coefficients of speech signals, as opposed to using speech samples. © Springer-Verlag 2004  

We explore the dictionary learning problem for sparse representation when the signals are dependent. In this paper, a first-order Markovian model is considered for dependency of the signals, that has many applications especially in medical signals. It is shown that the considered dependency among the signals can degrade the performance of the existing dictionary learning algorithms. Hence, we propose a method using the Maximum Log-likelihood Estimator (MLE) and the Expectation Minimization (EM) algorithm to learn the dictionary from the signals generated under the first-order Markovian model. Simulation results show the efficiency of the proposed method in comparison with the... 

In this paper, an improved multiplicative image watermarking system is presented. Since human visual system is less sensitive to the image edges, watermarking is applied in the contourlet domain, which represents image edges sparsely. In the presented scheme, watermark data is embedded in directional subband with the highest energy. By modeling the contourlet coefficients with General Gaussian Distribution (GGD), the distribution of watermarked noisy coefficients is analytically calculated. The tradeoff between the transparency and robustness of the watermark data is solved in a novel fashion. At the receiver, based on the Maximum Likelihood (ML) decision rule, an optimal detector by the aid... 

This paper present a simple watermarking approach based on the rotation of low frequency components of image blocks. The rotation process is performed with less distortion by projection of the samples on specific lines according to message bit. To have optimal detection Maximum Likelihood criteria has been used. Thus, by computing the distribution of rotated noisy samples the optimum decoder is presented and its performance is analytically investigated. The privilege of this proposed algorithm is its inherent robustness against gain attack as well as its simplicity. Experimental results confirm the validity of the analytical derivations and also high robustness against common attacks. ©... 

Localization and quantification of structural damage and estimating the failure probability are key outputs in the reliability assessment of structures. In this study, an Artificial Neural Network (ANN) is used to reduce the computational effort required for reliability analysis and damage detection. Toward this end, one demonstrative structure is modeled and then several damage scenarios are defined. These scenarios are considered as training data sets for establishing an ANN model. In this regard, the relationship between structural response (input) and structural stiffness (output) is established using ANN models. The established ANN is more economical and achieves reasonable accuracy in... 

Tight unmanned aerial vehicle (UAV) autonomous missions such as formation flight and aerial refueling (AR) requires an active controller that works in conjunction with a precise vision-based sensor that is able to extract In-front aircraft relative position and orientation from captured images. A key point in implementing such a sensor is its robustness in the presence of noises and other uncertainties. In this paper, a new vision-based algorithm that uses neural networks to estimate the In-front aircraft relative orientation and position is developed. The accuracy and robustness of the proposed algorithm has been validated via a detailed modeling and a complete virtual environment based on... 

Image matching plays an important role in many aspects of computer vision. Our proposed method is based on Scale Invariant Feature Transform (SIFT) which is one of the popular image matching methods. The main ideas behind our method are removing the excess keypoints, adding oriented patterns to descriptor, and decreasing the size of the descriptors. By doing these changes to SIFT, we would have oriented patterns of keypoints. In addition, the numbers of keypoints have been reduced and the places of keypoints would be selected more accurately, and also the size of the descriptors has been reduced  

Developing internet usage and services urged play strong role for social network. Social networks are environment which uses internet as interface to provide relations between people, in the other word to interchange data and information between persons. Email and Instant Messengers are popular examples of them. Whereas these environments are continuously and instantly developing, revising and viewing by humans, they are good places for mining. In this paper, the topic of exchanged information between users in this type of networks will be our target. Our method is to use a hierarchical dictionary of semantically related topics and words that is mapped to a graph. Then extracted keywords...