http:^^www.cs.wisc.edu^computer-vision^pubs.html

This data set contains WWW-pages collected from computer science departments of various universities

the motion parallax. We conclude from these analyses that reliable
qualitative shape information is generally available only at
discontinuities in the image flow field.
</blockquote>


<LI> <B><A NAME="thesis-allmen">
     Image Sequence Description using Spatiotemporal Flow Curves:
     Toward Motion-Based Recognition</A></B><BR>
     Ph.D. Dissertation, M. C. Allmen,
     Computer Sciences Department Technical Report 1040,
     August 1991.
     (<!WA58><!WA58><!WA58><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/thesis-allmen.ps">postscript</A>
     or <!WA59><!WA59><!WA59><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/thesis-allmen.ps.gz">1.1M gzip'ed postscript</A>)<P>
<blockquote>
Recovering a hierarchical motion description of a long image sequence is
one way to recognize objects and their motions.
Intermediate-level and high-level motion analysis, i.e., recognizing a
coordinated sequence of events
such as walking and throwing,
has been formulated previously as a process that follows high-level object
recognition. This thesis develops an alternative approach to
intermediate-level and high-level motion analysis.
It does not depend on complex object descriptions and can therefore be
computed prior to object recognition. Toward this end,
a new computational framework for low and intermediate-level processing of
long sequences of images is
presented.
<P>
 
Our new computational framework
uses spatiotemporal (ST) surface flow and ST flow curves.
As contours move, their projections
into the image also move. Over time, these projections sweep out
ST surfaces. Thus, these
surfaces are direct representations of object motion.
ST surface flow is defined as the natural extension
of optical flow to
ST surfaces. For every point on an ST surface, the instantaneous
velocity of that point on the surface is recovered.
It is observed that arc length of a rigid contour does not change if
that contour is moved in the direction of motion on the ST surface. Motivated
by this observation, a function measuring arc length change is defined.
The direction of motion of a contour undergoing
motion parallel to the image plane is shown to be perpendicular to the
gradient of this function.
<P>
 
ST surface flow is then used to recover ST flow curves. ST flow curves
are defined such that the tangent at a point on the curve equals the ST
surface flow at that point. ST flow curves are then grouped so that each
cluster represents a temporally-coherent structure, i.e.,
structures that result
from an object or surface in the scene undergoing motion. Using these clusters
of ST flow curves, separate moving objects in the scene can be hypothesized
and occlusion and disocclusion between them can be identified.
<P>
 
The problem of detecting cyclic motion, while recognized by the psychology
community, has received very little attention in the computer vision
community. In order to show the representational
power of ST flow curves, cyclic motion is detected using ST flow curves
without prior recovery of complex object descriptions.
</blockquote>


</UL>
<HR>
<P>
<H2><A NAME="shape">3D Shape Representation</A></H2>
<UL>

<LI><!WA60><!WA60><!WA60><img alg="o" src="http://www.cs.wisc.edu/~dyer/images/new.gif"> 
     <B><A NAME="sigg96-seitz"> View Morphing</A></B><BR>
     S. M. Seitz and C. R. Dyer, <CITE>Proc. SIGGRAPH 96</CITE>, 1996, To
     appear. 
(<!WA61><!WA61><!WA61><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/sigg96-seitz.ps">
4.2M postscript</A>
or <!WA62><!WA62><!WA62><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/sigg96-seitz.ps.gz">
1.6M gzip'ed postscript</A>)<P>
<blockquote>
Image morphing techniques can generate compelling 2D transitions between
images.  However, differences in object pose or viewpoint often cause
unnatural distortions in image morphs that are difficult to correct
manually.  Using basic principles of projective
geometry, this paper introduces a simple extension to image morphing
that correctly handles 3D projective camera and scene transformations.
The technique, called <I> view morphing</I>, works by prewarping two images
prior to computing a morph and then postwarping the interpolated images.
Because no knowledge of 3D shape is required, the technique may be applied
to photographs and drawings, as well as rendered scenes.
The ability to synthesize changes both in viewpoint and image structure
affords a wide variety of interesting 3D effects via simple image
transformations.
</blockquote>

<LI><!WA63><!WA63><!WA63><img alg="o" src="http://www.cs.wisc.edu/~dyer/images/new.gif"> 
     <B><A NAME="icpr96-seitz"> Toward Image-Based Scene Representation
	  Using View Morphing</A></B><BR>
     S. M. Seitz and C. R. Dyer, <CITE>Proc. 13th Int. Conf. Pattern
	  Recognition, Vol. I, Track A: Computer Vision</CITE>, 1996, 84-89.
(<!WA64><!WA64><!WA64><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/icpr96-seitz.ps">
1.2M postscript</A>
or <!WA65><!WA65><!WA65><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/icpr96-seitz.ps.gz">
486K gzip'ed postscript</A>)
     (Longer version appears as Computer Sciences Department
     <CITE>Technical Report 1298</CITE>
     (<!WA66><!WA66><!WA66><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/tr1298-seitz.ps">postscript</A>
     or <!WA67><!WA67><!WA67><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/tr1298-seitz.ps">552K gzip'ed postscript</A>).)<P>
<blockquote>
The question of which views may be inferred from a set of basis images
is addressed.  Under certain conditions, a discrete set of images
implicitly describes scene appearance for a continuous range of viewpoints.
In particular, it is demonstrated that two basis views of a static scene
determine the set of all views on the line between their optical centers.
Additional basis views further extend the range of predictable views to a
two- or three-dimensional region of viewspace.  These results are shown to
apply under perspective projection subject to a generic visibility
constraint called monotonicity.  In addition, a simple scanline algorithm is
presented for actually generating these views from a set of basis images.
The technique, called <I> view morphing</I> may be applied to both calibrated
and uncalibrated images.  At a minimum, two basis views and their
fundamental matrix are needed.  Experimental results are presented on
real images.  This work provides a theoretical foundation for image-based
representations of 3D scenes by demonstrating that perspective view
synthesis is a theoretically well-posed problem.
</blockquote>

<LI> <B><A NAME="rvs95-seitz">
     Physically-Valid View Synthesis by Image Interpolation</A></B><BR>
     S. M. Seitz and C. R. Dyer, <CITE>Proc. Workshop on Representation
     of Visual Scenes</CITE>, 1995, 18-25.
     (<!WA68><!WA68><!WA68><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/rvs95-seitz.ps">postscript</A>
     or <!WA69><!WA69><!WA69><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/rvs95-seitz.ps.gz">500K gzip'ed postscript</A>)<P>
<blockquote>
Image warping is a popular tool for
smoothly transforming one image to another.  ``Morphing''
techniques based on geometric image interpolation create compelling visual
effects, but the validity of such transformations has not been established.
In particular, does 2D interpolation of two
views of the same scene produce a sequence of physically valid in-between
views of that scene?  In this paper, we describe a simple image rectification
procedure which guarantees that interpolation does in fact produce valid views,
under generic assumptions about visibility and the projection process.
Towards this end, it is first shown that two basis views are sufficient to
predict the appearance of the scene within a specific range of new viewpoints.
Second, it is demonstrated that interpolation of the rectified basis images
produces exactly this range of views.
Finally, it is shown that generating this range of views is a theoretically
well-posed problem, requiring neither knowledge of camera positions nor
3D scene reconstruction.
A scanline algorithm for view interpolation is presented that requires only
four user-provided feature correspondences to produce valid orthographic
views.  The quality of the resulting images is demonstrated with
interpolations of real imagery.
</blockquote>


<LI> <B><A NAME="pami93-eggert">
     The Scale Space Aspect Graph</A></B><BR>
     D. W. Eggert, K. W. Bowyer, C. R. Dyer, H. I. Christensen
     and D. B. Goldgof, <CITE>IEEE Trans. Pattern Analysis and
     Machine Intelligence</CITE><B> 15</B>, 1993, 1114-1130.
     (<!WA70><!WA70><!WA70><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/pami93-eggert.ps">postscript</A>
     or <!WA71><!WA71><!WA71><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/pami93-eggert.ps.gz">280K gzip'ed postscript</A>)<BR>
     (An earlier
     version of this paper appeared in
     <CITE>Proc. Computer Vision and Pattern Recognition Conf.</CITE>,
     1992, 335-340
     (<!WA72><!WA72><!WA72><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/cvpr92-eggert.ps">postscript</A>
     or <!WA73><!WA73><!WA73><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/cvpr92-eggert.ps.gz">250K gzip'ed postscript</A>).)
     <P> 
<blockquote>
Currently the aspect graph is computed from the theoretical standpoint 
of perfect resolution in object shape, the viewpoint and the projected image.
This means that the aspect graph may include details that an observer could 
never see in practice. Introducing the notion of scale into the aspect graph 
framework provides a mechanism for selecting a level of detail that is 
"large enough" to merit explicit representation. This effectively allows 
control over the number of nodes retained in the aspect graph. This paper 
introduces the concept of the scale space aspect graph, defines three 
different interpretations of the scale dimension, and presents a detailed 
example for a simple class of objects, with scale defined in terms of the 
spatial extent of features in the image.
</blockquote>


<LI> <B><A NAME="cvgip92-seales">
     Viewpoint from Occluding Contour</A></B><BR>
     W. B. Seales and C. R. Dyer, 
     <CITE>Computer Vision, Graphics and Image Processing:  Image
     Understanding</CITE><B> 55</B>, 1992, 198-211.
     (<!WA74><!WA74><!WA74><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/cvgip92-seales.ps">postscript</A>
     or <!WA75><!WA75><!WA75><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/cvgip92-seales.ps.gz">290K gzip'ed postscript</A>)<P>
<blockquote>
In this paper we present the geometry and the algorithms for organizing a
viewer-centered representation of the occluding contour of polyhedra.
The contour is computed from a polyhedral boundary model as it would appear
under orthographic projection into the image plane from every viewpoint
on the view sphere.
Using this representation, we show how to derive constraints on regions in
viewpoint space from the relationship between detected image features and
our precomputed contour model.
Such constraints are based on both qualitative (viewpoint extent) and
quantitative (angle measurements and relative geometry) information that has
been precomputed about how the contour appears in the image plane as a set
of projected curves and T-junctions from self-occlusion.
The results we show from an experimental system demonstrate that features
of the occluding contour can be computed in a model-based framework, and
and their geometry constrains the viewpoints from which a model will project
to a set of occluding contour features in an image.
</blockquote>


<LI> <B><A NAME="ecai92-seales">
     An Occlusion-Based Representation of Shape for 
     Viewpoint Recovery</A></B><BR>
     W. B. Seales and C. R. Dyer, 
     <CITE>Proc. 10th European Conf. on Artificial Intelligence</CITE>,
     1992, 816-820.
     (<!WA76><!WA76><!WA76><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/ecai92-seales.ps">postscript</A>
     or <!WA77><!WA77><!WA77><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/ecai92-seales.ps.gz">80K gzip'ed postscript</A>)<P>
<blockquote>
In this paper we present the geometry and the algorithms for organizing and
using a viewer-centered representation of the occluding contour of polyhedra.
The representation is computed from a polyhedral model
under orthographic projection for all viewing directions.
Using this representation, we derive constraints on
viewpoint correspondences between image features and
model contours.
Our results show that the occluding contour, computed
in a model-based framework, can be used to strongly constrain the viewpoints
where a 3D model matches the occluding contour features of the image.
</blockquote>


<LI> <B><A NAME="thesis-seales">
     Appearance Models of Three-Dimensional 
     Shape for Machine Vision and Graphics</A></B><BR>
     Ph.D. Dissertation, W. B. Seales,
     Computer Sciences Department Technical Report 1042,
     August 1991.
     (<!WA78><!WA78><!WA78><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/thesis-seales.ps">postscript</A>
     or <!WA79><!WA79><!WA79><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/thesis-seales.ps.gz">460K gzip'ed postscript</A>)<P>
<blockquote>
A fundamental problem common to both computer graphics and model-based
computer vision is how to efficiently model the appearance of a shape.
Appearance is obtained procedurally by applying a projective transformation
to a three-dimensional object-centered shape representation.
This thesis presents a viewer-centered representation that is based on the
visual event, a viewpoint where a specific change in the structure of the
projected model occurs.
We present and analyze the basis of this viewer-centered representation
and the algorithms for its construction.
Variations of this visual-event-based representation are applied to two
specific problems:  hidden line/surface display, and the solution for model
pose given an image contour.
<P>