Adaptive image analysis for object recognition. II. Redundant object resolution

We construct an automatic and adaptive image analysis method for machine vision after the Marr “primal sketch” model for human visual perception. This method consists of four steps: image acquisition, object location, object validation, and parameter extraction coupled with object categorization. Any method that uses multiple threshold conditions can result in redundant objects that represent individual image features. Our method uses this redundancy to provide a “best” representation of each image feature. This object validation and categorization consists of the following steps: prescreening objects based on minimum validity criteria; constructing an object hierarchy based on “containedness”; reducing redundancy in this hierarchy but retaining multiple contained objects, thus performing a declumping process; identifying and separating heterogeneous objects from containing objects; and extracting parameters for final object categorization. We describe the application of our methods as an integrated image analysis solution to a variety of image processing problems. Qualitative and quantitative results are presented