In this talk, I will introduce a concept of "interactive
intelligent computing" which combines the statistical computing theories
with the HCI-type of human intervention.
In past decades, we have seen burgeoning of many interesting computing
paradigms such as pattern recognition, machine learning, and data mining to
name a few. What they all share is their main goal to 1) learn statistical
regularity from given data and 2) realize automation of certain computing
tasks, such as segmentation, classification, and recognition, using the
learned knowledge. Over years, we have seen that rigorous statistical and
mathematical theories and algorithms have been developed for addressing
these problems and that many have attempted to apply them to solve numerous
difficult real-world problems. Today, we are realizing that alas! this
paradigm of automation has serious limitations and many practical computing
systems are incorporating clever but heuristic user interventions to achieve
their end goals. What is really missing is a systematic theoretical and
practical treatise in addressing these user factors within the existing
computational theories for realizing reusable robust computing solutions.
This talk overviews these ideas by illustrating some examples from my
research field: visual information computing, especially medical image
analysis. My previous and current studies of one-click data segmentation and
registration, conducted at Siemens towards this goal, will be described and
demonstrated briefly. I will also describe their foundation: how
kernel-based robust statistical theories and algorithms can be applied to
and extended for realizing these solutions.
