Method of directed pattern enhancement for flexible recognition
A directed pattern enhancement method receives a learning image and pattern enhancement directive. Pattern enhancement learning is performed using the learning image and the pattern enhancement directive to generate pattern enhancement recipe. An application image is received and a pattern enhancement application is performed using the application image and the pattern enhancement recipe to generate pattern enhanced image. A recognition thresholding is performed using the pattern enhanced image to generate recognition result. The pattern enhancement directive consists of background directive, patterns to enhance directive, and patterns to suppress directive. An update learning method performs pattern enhancement progressive update learning.
1. A directed pattern enhancement method comprising the steps of:
a) Input learning image and pattern enhancement directive;
b) Perform pattern enhancement learning using the learning image and the pattern enhancement directive having pattern enhancement recipe output;
c) Input application image;
d) Perform pattern enhancement application using the application image and the pattern enhancement recipe having pattern enhanced image output wherein the pattern enhancement application comprising the steps of:
a) Perform background matching using the background recipe and the application image having non-background or background score output:
b) Perform foreground enhancement pattern matching using foreground enhancement pattern recipe and the application image having enhancement score output;
c) Perform integration using the enhancement score and the background score having pattern enhanced image output;
d) Perform foreground suppression pattern matching using foreground suppression pattern recipe and the application image having suppression score output;
e) Perform integration using the enhancement score, the suppression score and the background score having pattern enhanced image output wherein the integration step further comprising a special integration step when both the enhancement score and suppression score have high values.
2. The special integration step of claim 1 uses the average enhancement score and average suppression score of the neighbors of the points.
3. The special integration step of claim 1 uses whole neighbor as template to create a new enhancement score and a new suppression score.