Research Article
Detection of Image Seam Carving Using a Novel Pattern
Algorithm 1
The LNMOP descriptor construction.
| (1) | Input: Grayscale image (I) | | (2) | Output: Descriptor (LNMOP) | | (3) | Initial: t ⟵ 0, tol ⟵ [], r ⟵ 1 | | (4) | Begin | | (5) | [m, n] ⟵ size(I) | | (6) | k ⟵ ⌈ log2(2r + 1)2⌉ | | (7) | For i ⟵ −r: r | | (8) | For j ⟵ −r: r | | (9) | t = t + 1 | | (10) | temp (1: m, 1: n) = P (r + i + 1: m − r + i, r + j + 1: n − r + j) | | (11) | if (i ! = 0) || (j ! = 0) then | | (12) | mag(1: (2r + 1)2 − 1) = intensity (P (r + i + 1, r + j + 1))- intensity (P(r + 1, r + 1)) | | (13) | end if | | (14) | end | | (15) | end | | (16) | λ ⟵ 2r + 1 | | (17) | l(1: (2r + 1)2− 1) = level (max (mag), min (mag), λ, mag) //Determination of the magnitude level l | | (18) | For l ⟵ 1: λ | | (19) | if temp = = l then | | (20) | temp1 ⟵ l | | (21) | end if | | (22) | temp2 ⟵ sum (temp1) //The number of occurrences for the magnitude level l | | (23) | temp3 ⟵ dec_to_bin (temp2, k) //Decimal to binary with k bits | | (24) | tol ⟵ [tol, temp3] //Concatenation | | (25) | end | | (26) | LNMOP ⟵ tol/sum(tol) //Normalization | | (27) | return LNMOP | | (28) | end |
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