Research Article
Passive Framework of Sparse Region Duplication Detection from Digital Images
Algorithm 1
Algorithm of the proposed CMFD method.
ImgIn ➔ The input image matrix | ImgGray ➔ Grayscale image | ImgBlocks ➔ A two-dimensional matrix where each block is represented by pixels | ImgFeatures ➔ Matrix representing feature space | EDist ➔ Euclidean distance between neighborhood features | D ➔ Region marker based on the threshold | ImgReconst ➔ Reconstructed image | Thresh ➔ Threshold determining if the Euclidean distance between feature spaces could mark the region | Step 1: ImgGray ← Grayscale(ImgIn) //Convert the RGB image into grayscale image | Step 2: ImgBlocks ← Divide(ImgGray,8) //divide the image into [] circular blocks | Step 3: FOREACH block in ImgBlocks | ImgFeatures ← Extract LIOP features(block) //extract LIOP features from each circulate block | END | Step 4: For : Size(ImgFeatures)-1 | EDist ← Euclidean(ImgFeatures[i], ImgFeatures[i+1]) | IF(EDist <= Thresh) | D[i]=1 | ELSE | D[i]=0 | END | END | Step 5: ImgReconst ← Postprocessor(D) |
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