Dr. Steven F. Barrett Profile

Dr. Steven F. Barrett

Assistant Professor at Univ of Wyoming

SPIE Involvement:

Conference Program Committee | Author

Publications (29)

PROCEEDINGS ARTICLE | May 13, 2015

Biomimetic optical sensor for aerospace applications

Proc. SPIE. 9480, Fiber Optic Sensors and Applications XII

KEYWORDS: Superposition, Eye, Optical sensors, Aerospace engineering, Visualization, Sensors, Fiber optics sensors, Biomimetics, Head, Diodes

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PROCEEDINGS ARTICLE | March 8, 2014

Bio-mimetic optical sensor for structural deflection measurement

Proc. SPIE. 9055, Bioinspiration, Biomimetics, and Bioreplication 2014

KEYWORDS: Safety, Optical sensors, Fiber optics, Sensors, Fiber optics sensors, Measurement devices, Aerodynamics, Fiber optics tests, Global Positioning System

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PROCEEDINGS ARTICLE | March 23, 2011

Bioinspired vision sensors with hyperacuity

Proc. SPIE. 7975, Bioinspiration, Biomimetics, and Bioreplication

KEYWORDS: Visual process modeling, Imaging systems, Sensors, Image processing, Sensor performance, Biomimetics, Electrophysiology, Analog electronics, Motion models, 3D image processing

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SPIE Journal Paper | October 1, 2006

Segmentation method for three-dimensional visualization of microscopic objects imaged with a confocal laser scanning microscope

Jeffrey Anderson, Steven Barrett, Michael Wilcox

JEI Vol. 15 Issue 04

KEYWORDS: Image segmentation, Image processing, 3D image processing, Visualization, Image processing algorithms and systems, 3D image reconstruction, Optical spheres, Neurons, Confocal microscopy, Image enhancement

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SPIE Journal Paper | July 1, 2006

Design and development of a computer-assisted retinal laser surgery system

Cameron Wright, Steven Barrett, Ashley Welch

JBO Vol. 11 Issue 04

KEYWORDS: Eye, Retina, Computing systems, Laser therapeutics, Reflectivity, Reflectometry, Laser systems engineering, Analog electronics, Argon ion lasers, Confocal microscopy

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PROCEEDINGS ARTICLE | February 6, 2006

Quantitative and qualitative performance comparison of a biomimetic vision sensor with commercial CCD camera sensors

Proc. SPIE. 6068, Sensors, Cameras, and Systems for Scientific/Industrial Applications VII

KEYWORDS: CMOS sensors, Eye, Sensors, CCD cameras, Computer vision technology, Biomimetics, Image sensors, Machine vision, Charge-coupled devices, CCD image sensors

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PROCEEDINGS ARTICLE | May 16, 2005

Musca domestica inspired machine vision system with hyperacuity

Proc. SPIE. 5758, Smart Structures and Materials 2005: Smart Sensor Technology and Measurement Systems

KEYWORDS: Eye, Visualization, Imaging systems, Sensors, Image processing, Photodiodes, Machine vision, Chemical elements, Analog electronics, Prototyping

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PROCEEDINGS ARTICLE | April 14, 2005

A segmentation method for 3D visualization of neurons imaged with a confocal laser scanning microscope

Proc. SPIE. 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images

KEYWORDS: Image processing algorithms and systems, Confocal microscopy, Optical spheres, Visualization, Image segmentation, Image processing, 3D modeling, Binary data, 3D image processing, Neurons

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PROCEEDINGS ARTICLE | May 21, 2004

Computational image processing for a computer vision system using biomimetic sensors and eigenspace object models

Proc. SPIE. 5299, Computational Imaging II

KEYWORDS: Visual process modeling, Detection and tracking algorithms, Sensors, Image processing, Computing systems, Computer vision technology, Biomimetics, Image sensors, Machine vision, Object recognition

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Two challenges to an effective, real-world computer vision system are speed and reliable object recognition. Traditional computer vision sensors such as CCD arrays take considerable time to transfer all the pixel values for each image frame to a processing unit. One way to bypass this bottleneck is to design a sensor front-end which uses a biologically-inspired analog, parallel design that offers preprocessing and adaptive circuitry that can produce edge maps in real-time. This biomimetic sensor is based on the eye of the common house fly (<i>Musca domestica</i>). Additionally, this sensor has demonstrated an impressive ability to detect objects at subpixel resolution. However, the format of the image information provided by such a sensor is not a traditional bitmap transfer of the image format and, therefore, requires novel computational manipulations to make best use of this sensor output. The real-world object recognition challenge is being addressed by using a subspace method which uses eigenspace object models created from multiple reference object appearances. In past work, the authors have successfully demonstrated image object recognition techniques for surveillance images of various military targets using such eigenspace appearance representations. This work, which was later extended to partially occluded objects, can be generalized to a wide variety of object recognition applications. The technique is based upon a large body of eigenspace research described elsewhere. Briefly described, the technique creates target models by collecting a set of target images and finding a set of eigenvectors that span the target image space. Once the eigenvectors are found, an eigenspace model (also called a subspace model) of the target is generated by projecting target images on to the eigenspace. New images to be recognized are then projected on to the eigenspace for object recognition. For occluded objects, we project the image on to reduced dimensional subspaces of the original eigenspace (i.e., a “subspace of a subspace” or a “sub-eigenspace”). We then measure how close a match we can achieve when the occluded target image is projected on to a given sub-eigenspace. We have found that this technique can result in significantly improved recognition of occluded objects. In order to manage the combinatorial “explosion” associated with selecting the number of subspaces required and then projecting images on to those sub-eigenspaces for measurement, we use a variation on the A* (called “A-star”) search method. The challenge of tying these two subsystems (the biomimetic sensor and the subspace object recognition module) together into a coherent and robust system is formidable. It requires specialized computational image and signal processing techniques that will be described in this paper, along with preliminary results. The authors believe that this approach will result in a fast, robust computer vision system suitable for the non-ideal real-world environment.

SPIE Journal Paper | April 1, 2002

Computer-assisted laser photocoagulation of the retina: a hybrid tracking approach

Espen Naess, Torstein Molvik, Dustin Ludwig, Steven Barrett, Stanislaw Legowski, Cameron Wright, Peter de Graaf

JBO Vol. 7 Issue 02

KEYWORDS: Retina, Analog electronics, Reflectivity, Reflectometry, Beam splitters, Laser coagulation, Mirrors, Laser therapeutics, Eye, Prototyping

SPIE Journal Paper | July 1, 2001

Efficiently tracking a moving object in two-dimensional image space

Steven Barrett, Cameron Wright, Harry Zwick, Michael Wilcox, Benjamin Rockwell, Espen Naess

JEI Vol. 10 Issue 03

KEYWORDS: Detection and tracking algorithms, Retina, Algorithm development, Video, Eye, Head, Laser coagulation, Cameras, Scanning laser ophthalmoscopy, Electrical engineering

PROCEEDINGS ARTICLE | June 7, 2001

Irradiation control parameters for computer-assisted laser photocoagulation of the retina

Proc. SPIE. 4245, Ophthalmic Technologies XI

KEYWORDS: Mirrors, Eye, Laser therapeutics, Laser coagulation, Imaging systems, Retina, Reflectivity, Reflectometry, Optical tracking, Analog electronics

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PROCEEDINGS ARTICLE | May 17, 2001

Automated PRL measurements using a confocal laser ophthalmoscope

Proc. SPIE. 4246, Laser and Noncoherent Light Ocular Effects: Epidemiology, Prevention, and Treatment

KEYWORDS: Confocal microscopy, Eye, Detection and tracking algorithms, Visualization, Retina, Video, Image resolution, Optical tracking, Scanning laser ophthalmoscopy, Laser safety

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PROCEEDINGS ARTICLE | May 3, 2000

Hybrid digital/analog retinal-tracking system

Proc. SPIE. 3911, Biomedical Diagnostic, Guidance, and Surgical-Assist Systems II

KEYWORDS: Confocal microscopy, Mirrors, Laser coagulation, Retina, Laser energy, Reflectivity, Reflectometry, Optical tracking, Analog electronics, Laser tissue interaction

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SPIE Journal Paper | January 1, 2000

Initial in vivo results of a hybrid retinal photocoagulation system

Cameron Wright, Steven Barrett, R. Ferguson, Henry Rylander, Ashley Welch

JBO Vol. 5 Issue 01

KEYWORDS: Analog electronics, Eye, Retina, In vivo imaging, Human-machine interfaces, Laser coagulation, Mirrors, Laser therapeutics, Optical tracking, Blood vessels

PROCEEDINGS ARTICLE | October 4, 1999

Signal processing for robotically assisted laser photocoagulation of the retina

Proc. SPIE. 3809, Signal and Data Processing of Small Targets 1999

KEYWORDS: Mirrors, Eye, Laser coagulation, Retina, Reflectivity, Reflectometry, Signal processing, Optical tracking, Analog electronics, Signal detection

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PROCEEDINGS ARTICLE | June 1, 1998

Hybrid retinal tracking and coagulation system

Proc. SPIE. 3246, Ophthalmic Technologies VIII

KEYWORDS: Confocal microscopy, Mirrors, Beam splitters, Retina, Reflectivity, Control systems, Fiber lasers, Reflectometry, Analog electronics, Camera shutters

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PROCEEDINGS ARTICLE | May 26, 1997

Hybrid retinal photocoagulation system

Proc. SPIE. 2971, Ophthalmic Technologies VII

KEYWORDS: Confocal microscopy, Eye, Laser coagulation, Retina, Argon ion lasers, Reflectivity, Reflectometry, Optical tracking, In vivo imaging, Analog electronics

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PROCEEDINGS ARTICLE | May 26, 1997

Digital imaging-based retinal photocoagulation system

Proc. SPIE. 2971, Ophthalmic Technologies VII

KEYWORDS: Laser coagulation, Cameras, Blood vessels, Retina, Video, Argon ion lasers, Reflectivity, Computing systems, In vivo imaging, Prototyping

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SPIE Journal Paper | April 2, 1997

Hybrid approach to retinal tracking and laser aiming for photocoagulation

Cameron Wright, R. Ferguson, Henry Rylander, Ashley Welch, Steven Barrett

JBO Vol. 2 Issue 02

KEYWORDS: Eye, Analog electronics, Retina, Signal detection, Mirrors, Reflectometry, Laser coagulation, Automatic tracking, Safety, Confocal microscopy

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PROCEEDINGS ARTICLE | May 17, 1996

Integrated computer-aided retinal photocoagulation system

Proc. SPIE. 2673, Ophthalmic Technologies VI

KEYWORDS: Laser coagulation, Cameras, Retina, Argon ion lasers, Reflectivity, Computing systems, Control systems, CCD cameras, System integration, Pulsed laser operation

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PROCEEDINGS ARTICLE | May 17, 1996

Hybrid tracking and control system for computer-aided retinal surgery

Proc. SPIE. 2673, Ophthalmic Technologies VI

KEYWORDS: Confocal microscopy, Eye, Laser therapeutics, Blood vessels, Retina, Computing systems, Reflectometry, Analog electronics, Signal detection, Automatic tracking

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SPIE Journal Paper | January 2, 1996

Computer-aided retinal photocoagulation system

Steven Barrett, Cameron Wright, Maya Jerath, R. Stephen Lewis, Bryan Dillard, Henry Rylander, Ashley Welch

JBO Vol. 1 Issue 01

KEYWORDS: Computing systems, Retina, Argon ion lasers, Laser coagulation, Prototyping, Control systems, Reflectivity, Eye, In vivo imaging, Cameras

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PROCEEDINGS ARTICLE | May 1, 1995

Automated retinal robotic laser system instrumentation

Proc. SPIE. 2396, Biomedical Optoelectronic Instrumentation

KEYWORDS: Retina, Argon ion lasers, Laser applications, Reflectivity, Computing systems, Control systems, Camera shutters, Prototyping, Frame grabbers, Laser systems engineering

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PROCEEDINGS ARTICLE | March 3, 1995

Automated placement of retinal laser lesions in vivo

Proc. SPIE. 2374, Novel Applications of Lasers and Pulsed Power

KEYWORDS: Retina, Argon ion lasers, Laser applications, Computing systems, Control systems, In vivo imaging, Pulsed laser operation, Prototyping, Laser tissue interaction, Laser systems engineering

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Researchers at the University of Texas at Austin's Biomedical Engineering Laser Laboratory investigating the medical applications of lasers have worked toward the development of a retinal robotic laser system. The overall goal of the ongoing project is to precisely place and control the depth of laser lesions for the treatment of various retinal diseases such as diabetic retinopathy and retinal tears. Researchers at the USAF Academy's Department of Electrical Engineering and the Optical Radiation Division of Armstrong Laboratory have also become involved with this research due to similar related interests. Separate low speed prototype subsystems have been developed to control lesion depth using lesion reflectance feedback parameters and lesion placement using retinal vessels as tracking landmarks. Both subsystems have been successfully demonstrated in vivo on pigmented rabbits using an argon continuous wave laser. Work is ongoing to build a prototype system to simultaneously control lesion depth and placement. Following the dual-use concept, this system is being adapted for clinical use as a retinal treatment system as well as a research tool for military laser-tissue interaction studies. Specifically, the system is being adapted for use with an ultra-short pulse laser system at Armstrong Laboratory and Frank J. Seiler Research Laboratory to study the effects of ultra-short laser pulses on the human retina. The instrumentation aspects of the prototype subsystems were presented at SPIE Conference 1877 in January 1993. Since then our efforts have concentrated on combining the lesion depth control subsystem and the lesion placement subsystem into a single prototype capable of simultaneously controlling both parameters. We have designated this combined system CALOSOS for Computer Aided Laser Optics System for Ophthalmic Surgery. We have also investigated methods to improve system response time. Use of high speed nonstandard frame rate CCD cameras and high speed frame grabbers hosted on personal computers featuring the 32 bit, 33 MHz PCI bus have been investigated. Design details of an initial CALOSOS prototype design is provided in SPIE Conference proceedings 2396B-32 (Biomedical Optics Conference, Clinical Laser Delivery and Robotics Session). This paper will review in vivo testing to date and detail planned system upgrades.

SPIE Journal Paper | January 1, 1994

Digital tracking and control of retinal images

Steven Barrett, Maya Jerath, Henry Rylander, Ashley Welch

OE Vol. 33 Issue 01

KEYWORDS: Detection and tracking algorithms, Retina, Cameras, Eye, Video, Frame grabbers, Mirrors, Prototyping, Algorithm development, Control systems

PROCEEDINGS ARTICLE | July 7, 1993

Instrumentation for feedback-controlled retinal photocoagualation

Proc. SPIE. 1892, Medical Lasers and Systems II

KEYWORDS: Eye, Detection and tracking algorithms, Cameras, Retina, Video, Reflectivity, Control systems, LASIK, Feedback control, Laser systems engineering

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PROCEEDINGS ARTICLE | June 24, 1993

Digital tracking and control of retinal images

Proc. SPIE. 1877, Ophthalmic Technologies III

KEYWORDS: Mirrors, Eye, Detection and tracking algorithms, Cameras, Retina, Video, Algorithm development, Prototyping, Frame grabbers, Laser systems engineering

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