Amany Sarhan

A Proposed Intelligent Denoising Technique for Spatial Video Denoising for Real-Time Applications

With the widespread use of videos in many fields of our lives, it becomes very important to develop new techniques for video denoising. Spatial video denoising using wavelet transform has been the focus of the current research, as it requires less computation and more suitable for real-time applications. Two specific techniques for spatial video denoising using wavelet transform are considered in this work: 2D Discrete Wavelet Transform 2D DWT and 2D Dual Tree Complex Wavelet Transform 2D DTCWT. We performed an analytical analysis to investigate the performance of each of these techniques. From this analysis, we found out that each of these techniques has its advantages and disadvantages. The first technique gives less quality at high levels of noise but consumes less time, whereas the second gives high quality video while consuming a large amount of time. In this work, we introduce an intelligent denoising system that makes a tradeoff between the quality of the denoised video and the time required for denoising. The system first estimates the noise level in the video frame then chooses the proper denoising technique to apply on the frame. The simulation results show that the proposed system is more suitable for real-time applications where time is critical, while still giving high quality videos at low to moderate levels of noise.

Peer-to-Peer Desktop Grids Based on an Adaptive Decentralized Scheduling Mechanism

This article proposes an adaptive fuzzy logic based decentralized scheduling mechanism that will be suitable for dynamic computing environment in which matchmaking is achieved between resource requirements of outstanding tasks and resource capabilities of available workers. Feasibility of the proposed method is done via real time system. Experimental results show that implementing the proposed fuzzy matchmaking based scheduling mechanism maximized the resource utilization of executing workers without exceeding the maximum execution time of the task. It is concluded that the efficiency of FMA-based decentralized scheduling, in the case of parallel execution, is reduced by increasing the number of subtasks.

Fuzzy watershed segmentation algorithm: an enhanced algorithm for 2D gel electrophoresis image segmentation

Detection and quantification of protein spots is an important issue in the analysis of two-dimensional electrophoresis images. However, there is a main challenge in the segmentation of 2DGE images which is to separate overlapping protein spots correctly and to find the weak protein spots. In this paper, we describe a new robust technique to segment and model the different spots present in the gels. The watershed segmentation algorithm is modified to handle the problem of over-segmentation by initially partitioning the image to mosaic regions using the composition of fuzzy relations. The experimental results showed the effectiveness of the proposed algorithm to overcome the over segmentation problem associated with the available algorithm. We also use a wavelet denoising function to enhance the quality of the segmented image. The results of using a denoising function before the proposed fuzzy watershed segmentation algorithm is promising as they are better than those without denoising.

A Wavelet Relational Fuzzy C-Means Algorithm for 2D Gel Image Segmentation

One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM) and the Wavelet Fuzzy C-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.

A reliable event-driven strategy for real-time multiple object tracking using static cameras

Recently, because of its importance in computer vision and surveillance systems, object tracking has progressed rapidly over the last two decades. Researches on such systems still face several theoretical and technical problems that badly impact not only the accuracy of position measurements but also the continuity of tracking. In this paper, a novel strategy for tracking multiple objects using static cameras is introduced, which can be used to grant a cheap, easy installation and robust tracking system. The proposed tracking strategy is based on scenes captured by a number of static video cameras. Each camera is attached to a workstation that analyzes its stream. All workstations are connected directly to the tracking server, which harmonizes the system, collects the data, and creates the output spatial-tempo database. Our contribution comes in two issues. The first is to present a newmethodology for transforming the image coordinates of an object to its real coordinates. The second is to offer a flexible event-based object tracking strategy. The proposed tracking strategy has been tested over a CAD of soccer game environment. Preliminary experimental results show the robust performance of the proposed tracking strategy.

A Bidder Behavior Learning Intelligent System for Trust Measurement

With the spread of e-commerce fields over the world especially an online auctions segment, the problems of internet crimes increased in the last years. The online auctions face a serious problem of trust among the participants where users have no information about the others, and have no relations among them except the commercial transactions; this allows fraud to occur by malicious users. We proposed a framework depend on “Bidder Behavior” consist of three algorithms the first “ Bidder Increase Price Behavior”(BIPB), the second “Search In Stored Data Base”(SSDB) and the third “Person Recursive Search” (PRS) to improve the performance of trust measurement

Spatio-Temporal Error Concealment Algorithms for Encoded Video Streams for Real-Time Applications

Abstract Multimedia is one of the most important aspects of the information era. With the advent of the multimedia age and the spread of Internet, video storage and streaming has been gaining a lot of popularity. When transmitting compressed video over a data network channel errors affect the decoding process. Video transmission puts constraints on data rate, computational complexity and delay. Unreliable channels add more challenges to this task as compressed video is extremely vulnerable to transmission errors. Problems arising from imperfect transmission of block coded video frame sequence result in lost blocks. This paper presents and implements a set of decoder side spatio-temporal algorithms that aim to enhance the speed and quality of the error concealment process. The results obtained by these algorithms are much faster and achieve better performance compared to the existing algorithms, which make them more suitable for real-time applications.

Resolving the ambiguity of real-time multiple object tracking using static cameras

Recently, because of its importance in computer vision and surveillance systems, object tracking has progressed rapidly. In this paper a novel strategy for tracking multiple objects using static cameras is introduced, which can be used to grant a cheap, easy installation, and robust tracking system. The proposed tracking strategy is based on scenes captured by static video cameras. Each camera is attached to a workstation that analyses its stream. All workstations are connected to a tracking server, which harmonises the system, collects the data, and creates the output spatial-tempo database. Our contribution comes in two issues. The first is to present a new methodology for transforming the image coordinates of an object to its real coordinates. The second is to offer a flexible event-based object tracking strategy, which has been tested over a CAD of soccer game environment. Preliminary experimental results show the robust performance of the proposed tracking strategy.