Sharif Digital Repository

This article presents an innovative application of the frequency domain decomposition method based on an acoustic and vibration response. Frequency domain decomposition method has been frequently used for operational modal analysis testing in the last decade to identify modal parameters for in-situ case studies. For these studies, the outputs of the vibration response through accelerometers have been employed in the analysis. In this article, the frequency domain decomposition method is employed, for the first time, to analyze both acoustic and vibration response of the building which is a novel application in building vibration response. As a case study, a cylindrical shaped seven-story... 

In transition from concept design to detail design, designers and engineers need to find specific materials to optimize performance of the systems. The large number of materials and the wide range of manufacturing processes cause engineers always to seek new materials selection methods. In this research work a new method so called QFD method, which employs materials indices, is introduced. The method enjoys a combination of Ashby's materials selection concepts with the Quality Function Deployment (QFD) tool. The QFD method, which is modified here to fit the materials selection field, provides the designer with the required weighting factors for the material indices. Incorporation of House of... 

Automatic analysis in soccer scenes is still a difficult task in the absence of soccer field information. The first and most important step in almost all analysis, is soccer field line recognition and homography extraction. The aim of this paper is introducing a novel approach for automatic detection and recognition of soccer field lines and arcs by minimal information. A simple camera model and perspective map is assumed to reduce unknown parameters. An accurate method is utilized for detecting line pixels. The side of playfield area is determined based on the orientation of lines and arcs. Based on the detected playfield area side, an initial perspective map is obtained. An optimization... 

A Bayesian framework for 3-D human pose estimation from monocular images based on sparse representation (SR) is introduced. Our probabilistic approach aims at simultaneously learning two overcomplete dictionaries (one for the visual input space and the other for the pose space) with a shared sparse representation. Existing SR-based pose estimation approaches only offer a point estimation of the dictionary and the sparse codes. Therefore, they might be unreliable when the number of training examples is small. Our Bayesian framework estimates a posterior distribution for the sparse codes and the dictionaries from labeled training data. Hence, it is robust to overfitting on small-size training... 

This paper presents a novel method for cooperative educational dissemination systems. Taking into consideration the inherent characteristics of distance learning video streams (existence of a few slow moving objects in a classroom), we have proposed a novel content-based video coding method that is very efficient at low bitrate channels. On the encoding side, we have applied a background subtraction algorithm for motion segmentation using a novel statistical background modeling approach. At each frame, the moving objects are extrapolated with a rectangular model and tracked frame by frame (which forms the only data needed to be sent over the channel). On the decoding side, we have used a new... 

This paper presents a novel video encoding method for cooperative Educational Dissemination Systems. Taking into consideration the inherent characteristics of distance learning video streams, existing a few moving objects in the scene and objects having slow motions, we propose a novel content-based video encoding method which is very efficient on low bandwidth channels. In the encoding process, we apply a background subtraction algorithm for motion segmentation with a novel statistical background modeling. In each frame, the moving objects are extrapolated with rectangular bounding boxes which are the only data send over the low bandwidth channel. In the decoding process, we propose a new... 

Due to fast growth of wireless mobile networks, video transmission over wireless media has been widely studied. As wireless networks are error prone, there is a high possibility of loss in sent packets. Since time limitations in real-time video applications should be met, the delay-related to resending packets is not acceptable and the error should to be concealed at receiver side. With respect to different concealment methods, two new methods for temporal error concealment are proposed. In the first method, an optimized set of motion vectors is formed using motion vectors in surrounding blocks of the lost macroblock, and then this set is searched for the best motion vector. For extending... 

Object tracking is a difficult and primary task in many video processing applications. Because of the diversity of various video processing tasks, there exists no optimum method that can perform properly for all applications. Histogram-based particle filtering is one of the most successfu1 object tracking methods. However, for dealing with visual tracking in real world conditions (such as changes in illumination and pose) is still a challenging task. In this paper, we have proposed a color-based adaptive histogram particle filtering method that can update the target model. We have used the Bhattacharyya coefficients to measure the likelihood between two color histograms. Our experimental... 

An adaptive tracker to address the problem of tracking objects which undergo abrupt and significant motion changes is introduced. Abrupt motion of objects is an issue which makes tracking a challenging task. To address this problem, a new adaptive motion model is proposed. The model is integrated into the sequential importance resampling particle filter (SIR PF), which is the most popular probabilistic tracking framework. In this model, in each time step, if necessary, the particles' configurations are updated by using feedback information from the observation likelihood. In order to overcome the local-trap problem, local search algorithm with best improvement strategy is used to update... 

This paper presents a new segmentation method for color images. It relies on soft and hard segmentation processes. In the soft segmentation process, a cellular learning automata analyzes the input image and closes together the pixels that are enclosed in each region to generate a soft segmented image. Adjacency and texture information are encountered in the soft segmentation stage. Soft segmented image is then fed to the hard segmentation process to generate the final segmentation result. As the proposed method is based on CLA it can adapt to its environment after some iterations. This adaptive behavior leads to a semi content-based segmentation process that performs well even in presence of... 

In recent years, processing the images that contain human faces has been a growing research interest because of establishment and development of automatic methods especially in security applications, compression, and perceptual user interface. In this paper, a new method has been proposed for multiple face detection and tracking in video frames. The proposed method uses skin color, edge and shape information, face detection, and dynamic movement analysis of faces for more accurate real-time multiple face detection and tracking purposes. One of the main advantages of the proposed method is its robustness against usual challenges in face tracking such as scaling, rotation, scene changes, fast... 

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin... 

This paper presents a novel video encoding method for cooperative educational dissemination systems. Taking into consideration the inherent characteristics of stereo cameras framework in our educational videos and the ability of determining objects in different depths in a scene, we have proposed a novel object-based video encoding based on "sprite coding" that supports the MPEG-4 Version 1 Main profile in order to transfer distance learning videos across narrow-band transmission links such as the Internet. This paper proposes a multi-layer video object layer generation scheme with foreground moving object extraction and background sprite generation using stereo camera property. The... 

This paper presents a novel and fast homography-based tracking method to segment and track the instructor placed in front of a class board using two synchronized views. In the proposed method we estimate the instructor as a planar object and track its planar parameters. At first, the homography of board and fundamental geometry of two views are extracted using six point correspondences. The homography transformation matrix of object is calculated by obtaining three point correspondences in parallax region introduced with homography of the board. The parameters to be tracked by Kalman filter are obtained frame by frame by object's homography decomposition. Our proposed method is robust... 

Although there is an increasing interest in employing the depth data in computer vision applications, the spatial resolution of depth maps is still limited compared with typical visible-light images. A novel method is proposed to synthetically improve the spatial resolution of a single depth image. It integrates the higher-order terms into the Markov random field (MRF) formulation of example-based methods in order to improve the representational power of those methods. The inference is performed by approximately minimising the higher-order multi-label MRF energies. In addition, to improve the efficiency of the inference algorithm, a hierarchical scheme on the number of MRF states is... 

A novel class-dependent joint weighting method is proposed to mine the key skeletal joints for human action recognition. Existing deep learning methods or those based on hand-crafted features may not adequately capture the relevant joints of different actions which are important to recognize the actions. In the proposed method, for each class of human actions, each joint is weighted according to its temporal variations and its inherent ability in extension or flexion. These weights can be used as a prior knowledge in skeletal joints-based methods. Here, a novel human action recognition algorithm is also proposed in order to use these weights in two different ways. First, for each frame of a... 

A novel method is proposed in the scope of image segmentation that solves this problem by breaking it into two main blocks. The first block's functionality is a method to anticipate the color basis of each segment in segmented images. One of the challenges of image segmentation is the inappropriate distribution of colors in the RGB color space. To determine the color of each segment, after mapping the input image onto the HSI color space, the image colors are classified into some clusters by exploiting the K-Means. Then, the list of cluster centers is winnowed down to a short list of colors based on a set of criteria. The second block of the proposed method defines how each pixel of the... 

A probabilistic video content analysis method called extended histogram (EH) is proposed for modelling temporal evolutions of a set of histograms extracted from video frames. In EH, the number of counts for each histogram bin is considered as a random variable (instead of a single value) to account for bin variations. This representation is especially suitable for modelling the dynamic behaviour of a tracked video content of interest in a general manner. The pitfall of such a modelling is its negligence of the temporal order of observations in the collection. To overcome that problem, a hierarchical approach called hierarchical extended histogram (HEH) is proposed for extracting EHs in... 

In this paper, we present the most general form for error control coding using finite field multi-rate filters. This method shows how different types of codes can easily be generated by multi-rate filters and filter banks. In all previous works, codes and syndromes were generated using prefilters. Here we present simple multi-rate structures for encoding and generating syndrome. We show that all kinds of arbitrary rate K/L, circulant linear codes can be generated by these structures. Then we claim that a similar simple structure for syndrome generation in all presented cases exist  

The 3D point clouds are increasingly being used in various application including safety-critical fields. It has recently been demonstrated that deep neural networks can successfully process 3D point clouds. However, these deep networks can be misclassified via 3D adversarial attacks intentionality designed to perturb some point cloud's features. These misclassifications may be due to the network's overreliance on features with unnecessary information in training sets. As such, identifying the features used by deep classifiers and removing features with unnecessary information from the training data can improve network's robustness against adversarial attacks. In this paper, the LPF-Defense...