Dr. Michael J. Wilcox Profile

Dr. Michael J. Wilcox

Research Professor

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Publications (9)

Proceedings Article | 25 September 2007 Paper

Beyond the resolution limit: subpixel resolution in animals and now in silicon

M. Wilcox

Proceedings Volume 6699, 669902 (2007) https://doi.org/10.1117/12.734879

KEYWORDS: Analog electronics, Sensors, Target detection, Prototyping, Retina, Image resolution, Image filtering, Spatial frequencies, Image processing, Feature extraction

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SPIE Journal Paper | 1 October 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, 043005, (October 2006) https://doi.org/10.1117/12.10.1117/1.2372515

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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Proceedings Article | 28 June 2006 Paper

Scalable analog wavefront sensor with subpixel resolution

Michael Wilcox

Proceedings Volume 6272, 62723G (2006) https://doi.org/10.1117/12.672152

KEYWORDS: Sensors, Analog electronics, Wavefront sensors, Mirrors, Prototyping, Eye, Deformable mirrors, Image resolution, Optical resolution, Chemical elements

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Proceedings Article | 16 May 2005 Paper

Musca domestica inspired machine vision system with hyperacuity

Dylan Riley, William Harman, Eric Tomberlin, Steven Barrett, Michael Wilcox, Cameron Wright

Proceedings Volume 5758, (2005) https://doi.org/10.1117/12.593797

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

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Proceedings Article | 14 April 2005 Paper

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

Jeffrey Anderson, Steven Barrett, Michael Wilcox

Proceedings Volume 5746, (2005) https://doi.org/10.1117/12.594565

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

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Proceedings Article | 21 May 2004 Paper

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

Cameron Wright, Steven Barrett, Daniel Pack, Thomas Schei, Jeffrey Anderson, Michael Wilcox

Proceedings Volume 5299, (2004) https://doi.org/10.1117/12.525146

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

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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 (Musca domestica). 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 | 1 July 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, (July 2001) https://doi.org/10.1117/12.10.1117/1.1380202

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

Proceedings Article | 11 November 1996 Paper

Fiber optics that fly

Michael Wilcox, Donald Thelen

Proceedings Volume 2824, (1996) https://doi.org/10.1117/12.258127

KEYWORDS: Photodetectors, Sensors, Analog electronics, Photodiodes, Retina, Visualization, Eye, Axons, Data processing, Animal model studies

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The need for autonomous systems to work under unanticipated conditions requires the use of smart sensors. High resolution systems develop tremendous computational loads. Inspiration from animal vision systems can guide us in developing preprocessing approaches implementable in real time with high resolution and deduced computational load. Given a high quality optical path and a 2D array of photodetectors, the resolution of a digital image is determined by the density of photodetectors sampling the image. In order to reconstruct an image, resolution is limited by the distance between adjacent detectors. However, animal eyes resolve images 10-100 times better than either the acceptance angle of a single photodetector or the center-to-center distance between neighboring photodetectors. A new model of the fly's visual system emulates this improved performance, offering a different approach to subpixel resolution. That an animal without a cortex is capable of this performance suggests that high level computation is not involved. The model takes advantage of a photoreceptor cell's internal structure for capturing light. This organelle is a waveguide. Neurocircuitry exploits the waveguide's optical nonlinearities, namely in the shoulder region of its gaussian sensitivity-profile, to extract high resolution information from the visual scene. The receptive fields of optically disparate inputs overlap in space. Photoreceptor input is continuous rather than discretely sampled. The output of the integrating module is a signal proportional to the position of the target within the detector array. For tracking a point source, resolution is 10 times better than the detector spacing. For locating absolute position and orientation of an edge, the model performs similarly. Analog processing is used throughout. Each element is an independent processor of local luminance. Information processing is in real time with continuous update. This processing principle will be reproduced in an analog integrated circuit using photodiodes and fiber optic waveguides as the nonlinear light sensing devices, current mirrors and opamp circuits for the processing. The outputs of this circuit will go to other artificial neural networks for further processing.

Proceedings Article | 11 March 1993 Paper

Identifying retinal vessel networks in ocular fundus images

Shirley Lee, Gregory Donohoe, Michael Wilcox

Proceedings Volume 1964, (1993) https://doi.org/10.1117/12.141783

KEYWORDS: Image segmentation, Image processing, Artificial intelligence, Evolutionary algorithms, Digital filtering, Veins, Spherical lenses, Arteries, Image filtering, Image analysis

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