10.1117/2.1201103.003558 Supporting experimental research in computer vision Bart Lamiroy and Daniel Lopresti A collaborative community paradigm promotes new levels of repro- ducibility, reusability, and comparison of results. The document-analysis and exploitation (DARE) paradigm 1 was sparked at Lehigh University based on observations of how current research is being conducted in computer-perception do- mains. While very significant advances have been made in these fields over the last decades, publish-or-perish reflexes have impacted reporting of results and the methods underlying research-problem definition. One can identify a recurring pattern in how problems are identified, addressed, and published. A problem is chosen, sometimes by the author. A solution is then formulated. Data is found to support the problem and solution. Testing and vali- dation is conducted. Finally, experimental results are published Figure 1. Example of the document-analysis and exploitation paradigm. and sometimes the final system is fielded. Given the tendency of defining one’s own problem, and finding data to support and prove one’s own findings, concerned research communities have adapted to preserve a research evaluation environment that is as objective as possible. This is effected by including stringent peer-review processes, creating and sharing common data sets, and organizing competitions. These practices unnecessarily constrain research domains. DARE provides a collaborative community paradigm for machine-perception research that promotes new levels of re- producibility, reusability, and comparison of results. The main goals of machine perception are to develop algorithms that approach human levels of performance for specific tasks or invent new methods of improving known techniques. Unfor- tunately, they are often tested on small, overused data sets removed from the real world. This is partially related to the fact that data-set construction is costly and burdensome. Over time, Continued on next page