System and method for multimedia analytic processing and display
The present disclosure includes systems and methods for multimedia image analytic including automated binarization, segmentation, and enhancement using bio-inspired based visual morphology schemes. The present disclosure further includes systems and methods for biometric multimedia content authentication using extracted geometric features and one or more of the binarization, segmentation, and enhancement methods.
1. A method of binarizing acquired input multimedia content, the method comprising the steps of:
a) receiving the input multimedia content;
b) applying single window α-trim mean on one of the input multimedia content and a transformed grayscale channel of the input multimedia content;
c) applying double window α-trim mean on one of the input multimedia content and the transformed grayscale channel of the input multimedia content;
d) creating a visibility multimedia content using the single window α-trim mean and the double window α-trim mean;
e) determining a local visual threshold using the visibility multimedia content and a visual morphological thresholding method; and
f) generating a binarized multimedia content by applying the local visual threshold on the input multimedia content.
2. The method of claim 1 and further comprising, prior to step b), performing a color space transformation on the input multimedia content and selecting a channel from the transformation to create the transformed grayscale channel of the input multimedia content.
3. The method of claim 1 and further comprising, prior to step b), performing a color space transformation using an α-trim mean based principal component analysis (PCA) conversion on the input multimedia content, and selecting a channel from the transformation to create the transformed grayscale channel of the input multimedia content.
4. The method of claim 1 , wherein applying the single window α-trim mean comprises:
dividing the input multimedia content into a plurality of content blocks;
performing a local α-trim mean on each of the plurality of content blocks; and
determining the single window α-trim mean, based on the local α-trim means.
5. The method of claim 1 , wherein applying the double window α-trim mean comprises:
dividing the input multimedia content into a plurality of content blocks;
performing a local α-trim mean on each of the plurality of content blocks;
squaring a resulting α-trim mean for each of the plurality of content blocks;
dividing each squared result into a second plurality of content blocks;
performing the local α-trim mean on each of the second plurality of content blocks; and
determining a double window α-trim mean, based on the local α-trim means corresponding to the second plurality of content blocks.
6. The method of claim 1 , wherein determining a local visual threshold comprises:
dividing the visibility multimedia content into a plurality of content blocks;
determining a grey level density value for each of the plurality of content blocks;
applying an optimization algorithm to each of the plurality of content blocks; and
determining the local visual threshold.
7. The method of claim 1 , wherein the input multimedia content is at least one forensic finger print image, and the binarized multimedia content is at least one binarized forensic finger print image.
8. The method of claim 1 , wherein the input multimedia content is at least one microscopy image, and the binarized multimedia content is at least one binarized microscopy image.
9. The method of claim 1 , wherein the input multimedia content is at least one 3D image, and the binarized multimedia content is at least one binarized 3D image.
10. The method of claim 1 , wherein the input multimedia content is at least one X-ray image, and the binarized multimedia content is at least one binarized X-ray image.
11. A method of segmenting an acquired multimedia content, the method comprising the steps of:
a) receiving the multimedia content;
b) applying single window α-trim mean on one of the multimedia content and a transformed grayscale channel of the multimedia content;
c) applying double window α-trim mean on one of the multimedia content and the transformed grayscale channel of the multimedia content;
d) creating a visibility multimedia content using the single window α-trim mean and the double window α-trim mean;
e) determining a local visual threshold using the visibility multimedia content and a visual morphological thresholding method; and
f) generating a mask based on the local visual threshold; and
g) generating a foreground multimedia content and a background multimedia content by applying the mask to the multimedia content.
12. The method of claim 11 , wherein applying the single window α-trim mean comprises:
dividing the input multimedia content into a plurality of content blocks;
performing a local α-trim mean on each of the plurality of content blocks; and
determining the single window α-trim mean, based on the local α-trim means.