Neural network for detection and correction of local boundary misalignments between images

A neural network for correction of boundary misalignments between a monochrome reference image and a monochrome transformed image that is related to the reference image by an unknown transformation, the reference and transformed images further being defined within a given pixel space wherein, for each pixel in the given pixel space, the neural network comprises:

• an input layer having a plurality of input laver sections, each of the input layer section containing a plurality of input nodes encompassing at least one pixel within the given pixel space divided along a predetermined orientation into first and second sections of a cell;
• each of said input nodes having, processor means for detecting a unique contrast gradient defined by a digital state comparison between the pixels of the cell sections selected from a group consisting of;
• i) the first and second sections viewed with respect to only the reference image, ii) the first and second sections viewed with respect to only the transformed image, iii) the first sections viewed with respect to the reference and transformed images, and iv) the second sections viewed with respect to the reference and transformed images, wherein each of said input nodes outputs a first signal defining one of a presence or absence of the detected contrast gradient as measured by the selected cell sections;
• a second layer having a plurality of second layer sections respective associated with one of said input layer sections and containing a plurality of second layer nodes respectively responsive to the outputs of a predetermined combination of said input nodes to output a second signal defining a local boundary misalignment between the reference and transformed images when said first signal from each of said input nodes defines the presence of the measured contrast gradient;
• a third layer having a plurality of third layer nodes respectively associated with one of said second layer sections and responsive to the outputs thereof for weighting and combining the outputs of said associated second layer nodes to output a local correctional signal defining a direction to shift the transformed image perpendicular to the predetermined orientation associated with said one of said input layer sections;
• and means weighting and averaging the local correctional signal outputs of said third layer for calculating a fourth signal defining global misalignment between the reference and transformed images of the given pixel space.