Open AccessEigenstructure involving the histogram for image thresholding
Author(s) -
Ameer Salah
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.1428
Subject(s) - histogram , thresholding , mathematics , transpose , balanced histogram thresholding , matrix (chemical analysis) , image histogram , histogram matching , pattern recognition (psychology) , image (mathematics) , artificial intelligence , eigenvalues and eigenvectors , image processing , computer science , binary image , physics , materials science , composite material , quantum mechanics
The idea of the proposed image thresholding scheme is simply to consider the histogram as a 2D plot rather than a 1D function. The data can now be represented as a two‐row matrix. The first row is simply the grey levels of the image and the second row is the corresponding histogram values. Multiplying this matrix by its transpose will result in a power‐type matrix of size 2 × 2. The best threshold is the one producing a power matrix closer to that of the original image. Many combinations of the eigenvalues are suggested. To increase the correlation with the first row of the matrix, the histogram is replaced by the cumulative histogram. It is noticed that the trace of the matrix produces the best results. Comparative results show the effectiveness of the proposed schemes.