Applied Optics Feature Announcement
Convergence in Optical and Digital Pattern Recognition
Submission Deadline: October 1, 2009
Optical and digital pattern recognition techniques have provided a variety of important solutions to applications in both military and commercial domains. These techniques have also been driving forces for advancement in various related research areas such as novel devices, digital and optical algorithms, hybrid processors, and optical materials and architectures. The pioneering work of Vander Lugt more than forty years ago initiated the field of optical pattern recognition with much enthusiasm. The past forty years of optical and digital pattern recognition has experienced intense research and many successes. More recently, the field of computational sensing has had a dramatic impact on both optical and digital techniques for pattern recognition. The convergence of these once separate domains offers the potential for dramatically improved performance by means of the joint design of optical and digital degrees of freedom. In this special Applied Optics issue, we hope to address the many recent successes of optical and digital pattern recognition along with their growing convergence in the form of computational imaging. This special issue will answer some key questions to help researchers review the progress achieved until now and to identify new directions in these important domains.
Some of the specific questions we are interested in addressing in this special feature issue are as follows. Is pattern recognition more suitable to analog computing, digital computing, or a hybrid? Within a hybrid, computational imaging solution, how best do we allocate resources to the optical and digital subsystems? In what applications have these optical, digital, and hybrid pattern recognition techniques been applied successfully? What is the nature of these applications that make them successful? What are the various challenges of applying optical pattern recognition? What are the device requirements to further improve performance? Is optical alignment a problem in optical pattern recognition devices? What has been done to solve some of these problems? Is success at all possible or are the linear shift-invariant properties of optical processing insufficient to solve these problems? We believe that a special issue on Convergence in Optical and Digital Pattern Recognition in Applied Optics will help the community to answer many of the above questions and lead the research in new directions.
Topics of interest include but are not limited to the following topics:
- Successful applications of pattern recognition, e.g., face recognition, optical computing, motion detection, spectral estimation, and tracking.
- Optical, digital, and hybrid pattern recognition systems.
- Computational imaging applications to pattern recognition.
- Task-specific information for pattern recognition.
- Importance of optical phase in recognition, training, and learning.
- Performance evaluation of optical and digital pattern recognition algorithms, devices, systems, and architectures.
- Challenges for the optical implementation of pattern recognition.
- Digital, optical, and hybrid pattern recognition hardware.
- Noise modeling in optical and digital pattern recognition.
- Application of pattern recognition in beam alignment.
- Theoretical analysis of limitations of various pattern recognition techniques.
- Hybrid electro-optic pattern recognition devices.
- Review papers on interrelationship and convergence of optical and digital pattern recognition.
Manuscripts must be prepared according to the usual standards for submission to Applied Optics; see the Information for Contributors in any printed issue or the OSA Style Guide: http://www.opticsinfobase.org/ao/submit/style/jrnls_style.cfm.
Manuscripts must also be uploaded through OSA's electronic submission system: http://www.opticsinfobase.org/ao/journal/ao/author.cfm. All submissions must be submitted to the Information Processing Division. Please specify that the manuscript is for the Convergence in Optical and Digital Pattern Recognition feature (choose from the feature issue drop-down menu).
Feature Editors
Abdul Awwal
Lawrence Livermore National Laboratory
Livermore, California
USA
awwal1@llnl.gov
Khan Iftekharuddin
University of Memphis
Memphis, Tennessee
USA
iftekhar@memphis.edu
Mohammad Karim
Old Dominion University
Norfolk, Virginia
USA
mkarim@odu.edu
Mark Neifeld
University of Arizona
Tucson, Arizona
USA
mark@ece.arizona.edu
David Stork
Ricoh Innovations, Inc.
Menlo Park, California
USA
stork@rii.ricoh.com
