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Real-world application of machine vision techniques has been limited by
several factors. Most heuristic algorithms rely on several fudge factors
which are difficult to tune manually. Many physics-based approached make
simplifying assumptions that significantly reduce performance in
real-world settings.  Additionally, these algorithms often require accurate
technical information about the problem, such as camera parameters or
surface properties. We conjecture that these difficulties can be bypassed
by learning the target function directly from examples. Previous research
in this area has primarily focused on window-based approaches, which are
inherently scale-dependent. These techniques, while effective for 
low-level vision, suffer from "the curse of dimensionality" when applied 
to intermediate-level vision task. We propose a family of 
scale-independent neural network techniques closely related to pyramids, 
the discrete Fourier transform and the wavelet transform. We (hope to) 
show that this methodology can be applied to learn shape-from-shading 
from a small number of examples.  
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<LI><B>Rebecca Hasti</B><BR>
<I>Hand Gesture Recognition Using Orientation Histograms</I>
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A quick and efficient method for computer recognition of hand gestures 
would be useful in a number of situations.  For example, a system which
recognized hand gestures in real-time could be used instead of a mouse
to operate a computer.  For this project, I plan to implement a gesture
recognition method for static hand gestures using orientation histograms
described by W. Freeman and M. Roth in "Orientation Histograms for Hand
Gesture Recognition," Proc. Int. Workshop on Automatic Face and Gesture
Recognition, 1995.  This method of pattern recognition using orientation
histograms is relatively simple and fast and somewhat insensitive to 
scene illumination.
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<LI><B>Kirk Hogenson</B> and <B>Todd Turnidge</B><BR>
<I>Applications of the Steerable Pyramid</I>
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In "The Design and Use of Steerable Filters," Adelson et al. discuss the
creation of an orientable filter, i.e., a filter that selects a specific
direction.  This orientable (or "steerable") filter is capable of
detecting the response of the image to filtering at any desired orientation, 
based on the result of a few 'basis' filters.
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The steerable filter can be extended to select a specific scale as well as
orientation, yielding a "steerable pyramid filter."  The pyramid is
roughly analogous to the Laplacian Pyramid in that each level
corresponds to information at a different scale in the image.  As with
orientation, image response at any desired scale can be determined from
the 'basis' scales (i.e., levels in the pyramid).
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With such a pyramid, one can accomplish numerous tasks often performed in
machine vision applications, such as edge and contour detection, adaptive
noise reduction, and stereo matching.
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For our project, we intend to implement such a steerable filter, as well as
some its applications. 
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<LI><B>Mike James</B><BR>
<I>Internal Signature Keying: Improving Robustness of a Snake's Local Edge Finder</I>
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This project proposes to improve the distraction avoidance capabilities of
a snake tracking system's without moving up to the global object level. The
edge localization abilities of the snake are currently achieved at a local
level, but only look for the strongest edge along a line normal to the snake.
When tracking an object boundary the snake will be a closed contour. For a solid
object the pixel values immediately inside the contour are likely to remain
constant. Viewed along a line normal to the boundary this can be viewed as a
signature to look for in subsequent search iterations. The local edge finder can
then be set up to look for this signature, as well as the intensity step signifying
the boundary. It is hoped that this will help the edge finder avoid picking up on
potentially stronger background edges that may fall inside the search window.
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<LI><B>Ted Perkins</B><BR>
<I>Solving Single-Image Random-Dot Stereograms (SIRDS) for Depth</I>
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Normal random-dot stereograms work by presenting each eye with
two separate images; correspondences between the two images allow
the reconstruction of a depth map based on horizontal disparity
between corresponding patterns of dots.  SIRDS combine the images
for both eyes into a single image, relying on semi-periodic random dot
fields.  A program will be written to 'look' at SIRDS and reconstruct
a depth map.  The first stage of the problem will scan for potential
matches for each pixel, and an iterative relaxation scheme will be
used to arrive at a (hopefully) globally coherent solution.
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<LI><B>Dan Replogle</B><BR>
<I>Mosaic Construction</I>
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The project will construct a mosaic from a set of two images.  The project
will be modified to handle larger sets of images if time allows.  The project
will use a 2D transformation model, including translation and rotation. 
Hierarchical matching will be used.  Matches will be made first at smaller,
subsampled images, then refined.  Matches that minimize the sum of squared
differences will be considered the best.    
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<LI><B>Greg Sharp</B><BR>
<I>Texture Replication</I>
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In computer generated images, objects and textures which are difficult to
model are often copied directly from scanned photographs.  To a large degree,
the success of this technique depends upon the image quality of the object
in the original photograph.  For example, objects which are obscured or
have uneven illumination are not good candidates, because information about
the shape and/or texture of the object is missing or distorted.
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In this paper, we present a algorithm to approximate missing or distorted
image information for a textured object.  A texture description is recovered
from a object by identifying texel properties such as texel size, shape,
density and orientation.  The region of missing or distorted image information
is then textured by applying a texture replication technique to the region.
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<LI><B>Mark Smucker</B><BR>
<I>Implementation and Comparison of Three Global Multi-level Thresholding Techniques</I>
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My intended project is to implement three different algorithms for
multilevel thresholding of gray scale images and compare them on
various images.  The three methods I intend to implement are
interesting because of their relative newness and considerable
differences in approaches.  I intend to try and compare them in the
style done by Lee and Chung for other global thresholding techniques
besides summarizing and implementing them.
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The first algorithm I intend to implement comes from the paper, "A
fast histogram-clustering approach for multi-level thresholding" by
Tsai and Chen, which is "computationally fast and efficient" and
should be a good baseline system to test the other two algorithms
against since it does not attempt to consider the global
characteristics of the gray level distribution.  The second algorithm
takes a connectionist approach while the third uses a simulated
annealing approach.
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Besides the comparison report, one goal of this project is to provide
code modules that will allow future CS766 students to experiment with
multilevel thresholding of images.
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<LI><B>Jon Weyers</B><BR>
<I>Multiple Baseline Stereo with Non-Calibrated Views</I>
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I will attempt to apply the Multiple Baseline Stereo method
(Okutomi and Kanade) to a set of three images, taken from highly
separated, uncalibrated viewpoints.  This differs from the method
described in the paper in two ways.  First, I will be testing the
performance of the method using only two baselines, the minimum
number necessary for meaningful results.  Second, I will calculate
the relative lengths of the baselines from "conjugate triples"
specified interactively by the user, rather than assuming the
absolute lengths of the baselines are known.  This makes the method
applicable to snapshots taken from an unmounted camera.  I will
apply the method to images made using the Apple QuickTake camera,
displaying results as a gray-level map of relative distances.
(The exact distance would require exact knowledge of the length
of the baselines.)
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