Sharif Digital Repository

In this paper, a new method is proposed for motion vector steganalysis using the entropy value and its combination with the features of the optimized motion vector. In this method, the entropy of blocks is calculated to determine their texture and the precision of their motion vectors. Then, by using a fuzzy cluster, the blocks are clustered into the blocks with high and low texture, while the membership function of each block to a high texture class indicates the texture of that block. These membership functions are used to weight the effective features that are extracted by reconstructing the motion estimation equations. Characteristics of the results indicate that the use of entropy and... 

Motion estimation is an important field of video processing. Conventional block motion estimation methods consider all pixels equally. In this paper a new edge sensitive algorithm is proposed in which distortion of ridgelet coefficients is calculated partially and compared over all candidates. Using an edge boosting distortion function, edge pixels will have more important role since they introduce larger ridgelet coefficients. Inherent ability of ridgelet in representing edges with various directions in multi scale, makes it a powerful tool for edge pursuit. Proposed partial ridgelet distortion search (PRDS) do not reject any candidate without assessment while performs relatively fast since... 

Global motion estimation (GME) algorithms have an imperative role in object-based applications. Gradient-based GME is a well known method among these algorithms. Such algorithms require an initial value for their initialization step. Well estimation of this value plays a significant role in the accuracy of GME. This work introduces a simple but efficient technique for initial value prediction of GME. This technique employs a long-term predictor as well as global motions of previous frames. Simulations results demonstrate faster convergence and less computational complexity of the proposed method versus common presented techniques in the literature with almost same efficiency  

In this paper, we have introduced a new approach to deal with motion estimation block distortion measure (BDM) which has a great role in performance comparison of various methods. The proposed method generates lower BDM, by employing transformed matrices of original frames to compute motion vectors regardless of the algorithm employed as motion estimator. Our experimental results that use the semi DPCM predictor residual matrices as the transformation tool, justify the proposed approach. © 2005 IEEE  

Block motion estimation is an important field of video processing. This paper presents a new scheme to reach in faster block motion estimation based on motion prediction. The scheme exploits an adaptive filter to predict the block motion from its spatio-temporal motion compensated adjacent blocks, the predicted motion determines the initial candidate block for search methods with biased search center, in special case of partial distortion search, this strategy reduces search time by preventing from complete distortion calculation for more loser candidates, after comparison of three conventional adaptive filters, a normalized least mean square filter with convergence monitoring is recommended... 

Detection of changes in remotely sensed geographic images is required for a variety of applications including natural disasters. Change detection is an important process utilized for updating the geographic information system (GIS) data, monitoring natural resources and urban developments. It provides quantitative analysis of the spatial distribution of the area of interest. Different types of change detection techniques include: Multi-date visual composite (multitemporal composite), image differencing, post-classification, etc. In this work, we propose a change detection algorithm based on multiresolution analysis and motion estimation. We use multispectral satellite imagery and apply the... 

Global motion estimation (GME) is an important technique in image and video processing. Whereas the direct global motion estimation techniques boast reasonable precision they tend to suffer from high computational complexity. As with indirect methods, though presenting lower computational complexity they mostly exhibit lower accuracy than their direct counterparts. In this paper, the authors introduce a robust algorithm for GME with near identical accuracy and almost 50-times faster than MPEG-4 verification model (VM). This approach entails two stages in which, first, motion vector of sampled block is employed to obtain initial GME then Levenberg-Marquardt algorithm is applied to the... 

Modern sensors consist of arrays of detectors specially arranged to enhance precision, capabilities, and field of view (FOV). Sensor arrays can have multiple functionalities, such as the simultaneous detection of position and motion. We use a linear sensor model and develop an optimization method to design an array of photodiodes. Our objective function minimizes bias and variance estimations. We introduce a maximum likelihood technique to approximate and determine the bias caused by measurement errors, and verify our theory by statistically complete simulations. We apply our theory to design an optimal sun sensor. The sensor has a predefined conical FOV, and its accuracy is controlled by a... 

Mesh-based motion estimation is an important tool for video coding especially with low bit rate. In this paper, a new method for interpolating pixel motion from adjacent mesh nodes with the ability of omitting independent nodes is proposed. By exploiting fuzzy rules to determine the association of pixel and neighboring nodes, the proposed interpolation can detach pixels from some nodes. Consequently, it can deal with those critical patches on objects boundary which their nodes do not belong to one object. Updating the membership functions of each rule with specified strategy makes the interpolation adaptive with non-stationary conditions of image sequences and decreases sensitivity to... 

A novel approach in Terrain Following/Terrain Avoidance (TF/TA) flight is proposed. Due to existing unknown terrain during flight, an intelligent approach is used. Intelligence is implemented using the fuzzy approach. This method can be used in off-line design in trajectory planning which has wide applications in TF/TA maneuvers in unmanned aerial vehicles. A relationship between slope of terrain and aircraft height with speed of aircraft is constructed by fuzzy approach. This work is performed for a two dimensional Terrain Following Flight. This method is state of the art in literature. © 2007 IEEE  

One of the most important capabilities expected from an autonomous vehicle is avoiding collision with obstacles in its path. For this purpose, autonomous vehicle must be able to perform an emergency maneuver as soon as the obstacle is detected. This paper presents a method for designing and performing an emergency maneuver in order to avoid collision with a fixed or moving obstacle in the path. A sinusoidal or exponential trajectory, which is a function of the relative distance between vehicle and obstacle, is designed as the desired trajectory for lateral motion of the vehicle. A sliding mode controller is designed in order to guarantee that the vehicle tracks that desired trajectory. The... 

The motivation of this work is to synthesize a kicking pattern for a humanoid robot with consideration of various objectives such as retaining its balance even after the kick is done and reducing the undesired angular momentum using both hands and torso. This kick pattern is designed so that a desirable ball velocity is achieved. In this paper, the law of conservation of angular momentum is used to generate a less energy consuming trajectory. Effectiveness of the proposed method is verified using computer simulation and is tested on Sharif CEDRA humanoid robot. Copyright © 2006 by ASME  

Motion estimation and compensation is an essential part of existing video coding systems. Compared to the widely used block-matching algorithms (BMA), the mesh-based motion estimation (MME) produces better subjective and objective qualified image sequences in many cases, especially at low bitrates. However, the iterative refinement step of the MME is computationally too expensive. In this paper, we propose a fast MME scheme, which adjusts its interpolation patterns to the motion features of image sequences to achieve higher quality reconstructed results. Experimental results show notable rate-distortion improvement over both the BMA and the conventional non-iterative MME schemes, especially... 

This paper proposes a global motion estimation method to remove unintentional camera motions which degrade the visual quality of image sequences. The proposed approach is based on combination of 2D Radon transform, 1D Fourier transform and 1D Scale transform which can accurately estimate scale, rotational and translational distortions of camera motion and is robust to internal moving objects. Our experimental results with real and synthesized videos indicate the effectiveness of our proposed method  

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... 

Recent studies have demonstrated that high-level semantics in data can be captured using sparse representation. In this paper, we propose an approach to human body pose estimation in static images based on sparse representation. Given a visual input, the objective is to estimate 3D human body pose using feature space information and geometrical information of the pose space. On the assumption that each data point and its neighbors are likely to reside on a locally linear patch of the underlying manifold, our method learns the sparse representation of the new input using both feature and pose space information and then estimates the corresponding 3D pose by a linear combination of the bases... 

In this paper, we present a new method to solve tracking problem. The proposed method combines sparse representation and motion estimation to track an object. Recently. sparse representation has gained much attention in signal processing and computer vision. Sparse representation can be used as a classifier but has high time complexity. Here, we utilize motion information in order to reduce this computation time by not calculating sparse codes for all the frames. Experimental results demonstrates that the achieved result are accurate enough and have much less computation time than using just a sparse classifier  

Fully automatic algorithm for estimating the 3D human pose from multiple uncalibrated cameras is presented. Unlike the state-of-the-art methods which use the estimated pose of previous frames to restrict the candidates of current frame, the proposed method uses the viewpoint of previous frame in order to obtain an accurate pose. This paper also introduces a method to incorporate pose estimation results of several cameras without using the calibration information. The algorithm employs a rich descriptor for matching purposes. The performance of the proposed method is evaluated on a soccer database which is captured by multiple cameras. The dataset of silhouettes, in which the related 3D...