Spotting method for classification of real world data

This paper makes the case for a spotting computation scheme which gives ris e to a new classification methodology for processing real world data by sur veying algorithms developed under the Real World Computing (RWC) program an d related work in Japan. A spotting function has the segmentation-free char acteristic which ignores gracefully most real world input data which do not belong to a task domain. Some members of the family of spotting methods ha ve been developed under the RWC program. This paper shows how some spotting methods rise to the challenge of the case made for them. The common comput ational structure amongst spotting methods suggests an architecture for spo tting computation.