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![1 The 2D Cartesian coordinate space of an M×N image [3].](profile/Abdullah-Moussa/publication/337149913/figure/fig1/AS:823526118412288@1573355335622/The-2D-Cartesian-coordinate-space-of-an-MN-image-3.jpg)
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![Figure 2.1 The 2D Cartesian coordinate space of an M×N image [3].](profile/Abdullah-Moussa/publication/337149913/figure/fig1/AS:823526118412288@1573355335622/The-2D-Cartesian-coordinate-space-of-an-MN-image-3_Q320.jpg)
![Figure 2.2 An example of the exact matching problem [7].](profile/Abdullah-Moussa/publication/337149913/figure/fig2/AS:823526118416385@1573355335654/An-example-of-the-exact-matching-problem-7_Q320.jpg)
![Figure 2.4 Some examples of changing in brightness (a) [11] and...](profile/Abdullah-Moussa/publication/337149913/figure/fig3/AS:823526118395905@1573355335684/Some-examples-of-changing-in-brightness-a-11-and-contrast-b-12_Q320.jpg)
![Figure 2.5 Checking candidate windows in FS template matching [13].](profile/Abdullah-Moussa/publication/337149913/figure/fig4/AS:823526118408192@1573355335761/Checking-candidate-windows-in-FS-template-matching-13_Q320.jpg)
Template Matching is one of the most basic techniques in computer vision, where the algorithm should search for a template image T in an image to analyze I. It is heavily used in signal, image and video processing. A lot of applications are based on template matching in object detection, superresolution, image denoising and image compression. In th...
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Recent years have witnessed significant progress in video object detection.A video sequence is a much richer source of visual information than a still image. This is primarily because of the capture of motion. Although a single image provides a snapshot of the scene, a sequence of images registers the dynamics in it. The discussion of motion in thi...
Citations
... So, with the help of a sliding window, the sum of the pixel intensities within each sub-block is calculated and the resulting nine values are stored as a feature vector for that block. Using this feature vector is inspired by FRoTeMa template matching algorithm [17][18]. At the end of this procedure, we should have a nine values vector for each pixel in SI. ...
In recent years, and with the presence of many efficient image processing tools, digital image forgery has become a serious social issue. Copy-move forgery is one of the most widely used methods for image forgeries in which a part of the image is copied and then pasted to another location in the same image. This procedure is usually used to add or cover a critical part of the image. In this paper, we propose a new fast and accurate algorithm for copy-move forgery detection in digital images. In the proposed algorithm, the image to analyze is segmented into overlapping square blocks with a predefined side length, each one of the blocks is split into equally spaced k sub-blocks. The sum of pixel intensities of each sub-block is used to form a k-dimensional vector with the help of sliding window and such vector is used as a feature for each block. The resulting features of all blocks are stored in a KD-tree. The block corresponding to each node in the KD-tree is checked with the block corresponding to the nearest neighbor of this node. If the correlation between such blocks is above a prespecified threshold, the two blocks are considered as clones. Experimental results and comparisons with a state of the art method show that the proposed algorithm is fast and accurate.
