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Articles by Israa Hadi Ali
Total Records ( 4 ) for Israa Hadi Ali
  Israa Hadi Ali and Adil Abbas Majeed
  Rough Set Theory (RST) is a moderately new mathematical model to imperfect knowledge. RST concepts are utilized to analysis and classification of uncertain, imprecise, or incomplete knowledge and information. The purpose of this research is to classify group of kinds of persons’ motions numerate overlapping situations with each other similar to (bending, jacking, jumping, pjumping, running, siding, skipping, walking, waving) and the difficulty of distinguishing among these movements so we found and analyzed a set of attributes such as displacement, velocity, different in length and slope and because of the overlapping which resulted between these qualities so that we have been used RST which is used in distinguishing forms of overlapping movements types in classification process.
  Israa Hadi Ali and Qasim Jaleel
  One of the main problems with video processing is the extraction of feature. Interest points are one of the feature types in the video. There are a number of algorithms that depend on the interest points in the stitching process. The aim of this study is to compare three methods to extract interest points for video hidden target recovery. Hidden target makes the target non-clear for a reason such as security cases or to save the privacy of the target in the video. The proposed method is implemented in two stages. Firstly, determining the hidden region in the target and create a large window around the desired target to track and discover the target parts that appear. Secondly, the stage is the stitching stage of the parts using one of the best three methods namely Harris corner detection, SURF and FREAK. After selecting the best image resulting from those methods and comparing the results. Experiments have demonstrated the efficiency and robustness of the Harris algorithm compared to the SURF and the FREAK in the proposed method.
  Israa Hadi Ali and Safa S. AL-Murieb
  As a result of the evolution of the means of communication and to protect the transmission of data over the internet and communication channels, the data of the media such as a video needs for protection and security. To provide that, a watermark will be added throughout the video but it is better to be hided in different location in the frames not in fixed places nor in the background of frames. In this study a blind watermark embedding scheme was explained, by concealing different data in frames instead of embedding the same data. By embedding in most active object, another object was used as guiding to knows the bits that were hidden at each pixel of that object. Instead of hiding the same number of bits from watermark at every pixel of an active object, variation number of bits were concealed according to a homogeneity of a pixel with its neighbors. Also this study indicates how the proposed technique resists frame dropping and swapping attack, although of splitting the watermark into blocks and concealing each block in a frame.
  Israa Hadi Ali and Russell H. Al_taie
  In this study, new manner for removing noise from image using wavelet fusion method. The main aim of this research is restaurant the image based on peak signal to noise ratio measure. The key idea is compared each sub band for different levels of wavelet based on PSNR value. Initially apply discreet wavelet transform with 2level decomposition on the set of images .Then perform denoising wavelet techniques that achieved by threshold value for detail coefficient and compare it with wavelet coefficients for detail sub band. After that select sub band that has less noise from each image, sub band that contain high PSNR measure is the optimal. Finally apply IDWT process to convert the result image from frequency domain to spatial domain. The outcomes of the work exposed that the number of levels increases, PSNR of image decrease. In this study was chosen two level of decomposition to guarantee choosing several sub band for fusion process but the increasing in number of levels of wavelet will lose the essential information of image, therefore level 1 is better than level 2.
 
 
 
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