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

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

Our work emphasizes the need for (1) controlling camera motion
through efficient processing of the image stream, and (2) designing
provably-correct strategies, i.e., strategies whose success can be
accurately characterized in terms of the geometry of the viewed
object.  For each task, efficiency is achieved by extracting from
each image only the information necessary to move the camera
differentially, assuming a dense sequence of images, and using 2D
rather than 3D information to control camera motion.  Provable
correctness is achieved by controlling camera motion based on the
occluding contour's dynamic shape and maintaining specific
task-dependent geometric constraints that relate the camera's motion
to the differential geometry of the object.
</blockquote>

<LI> <B><A NAME="cvpr93-kutulakos">
     Toward Global Surface Reconstruction by Purposive 
     Viewpoint Adjustment</A></B><br>
     K. N. Kutulakos and C. R. Dyer, 
     <CITE> Proc. Computer Vision and Pattern 
     Recognition Conf.</CITE>, 1993, 726-727.
     (<!WA34><!WA34><!WA34><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/cvpr93-kutulakos.ps">postscript</A>
     or <!WA35><!WA35><!WA35><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/cvpr93-kutulakos.ps.gz">10K gzip'ed postscript</A>)<P>
<blockquote>
We consider the following problem: How should an observer change viewpoint
in order to generate a dense image sequence of an arbitrary smooth surface
so that it can be incrementally reconstructed using the occluding contour
and the epipolar parameterization?  We present a collection of qualitative
behaviors that, when integrated appropriately, purposefully control
viewpoint based on the appearance of the surface in order to provably solve
this problem.
</blockquote>

<LI> <B><A NAME="tr1124-kutulakos">
     Object Exploration By Purposive, Dynamic Viewpoint 
     Adjustment</A></B><br>
     K. N. Kutulakos, C. R. Dyer, V. J. Lumelsky, 
     Computer Sciences Department Technical Report 1124,
     November 1992.
     (<!WA36><!WA36><!WA36><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/tr1124-kutulakos.ps">postscript</A>
     or <!WA37><!WA37><!WA37><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/tr1124-kutulakos.ps.gz">110K gzip'ed postscript</A>)<P>
<blockquote>
  We present a viewing strategy for exploring the surface of an unknown
  object (i.e., making all of its points visible) by purposefully
  controlling the motion of an active observer. It is based on a simple
  relation between (1) the instantaneous direction of motion of the
  observer, (2) the visibility of points projecting to the occluding
  contour, and (3) the surface normal at those points: If the dot product of
  the surface normal at such points and the observer's velocity is positive,
  the visibility of the points is guaranteed under an infinitesimal
  viewpoint change. We show that this leads to an object exploration
  strategy in which the observer <EM>purposefully</EM> controls its motion
  based on the occluding contour in order to impose structure on the set of
  surface points explored, make its representation simple and qualitative,
  and provably solve the exploration problem for smooth generic surfaces of
  arbitrary shape. Unlike previous approaches where exploration is cast as a
  discrete process (i.e., asking where to look next?) and where the
  successful exploration of arbitrary objects is not guaranteed, our
  approach demonstrates that dynamic viewpoint control through directed
  observer motion leads to a qualitative exploration strategy that is
  provably-correct, depends only on the dynamic appearance of the
  occluding contour, and does not require the recovery of detailed
  three-dimensional shape descriptions from every position of the observer.
</blockquote>


<LI> <B><A NAME="icra94-kutulakos">
     Provable Strategies for Vision-Guided Exploration in Three Dimensions</A></B><BR>
     K. N. Kutulakos, C. R. Dyer, and V. J. Lumelsky,
     <CITE>Proc. 1994 IEEE Int. Conf. Robotics and Automation</CITE>,
     1994, 1365-1372.
     (<!WA38><!WA38><!WA38><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/icra94-kutulakos.ps">postscript</A>
     or <!WA39><!WA39><!WA39><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/icra94-kutulakos.ps.gz">210K gzip'ed postscript</A>)<P>
<blockquote>
An approach is presented for exploring an unknown, arbitrary surface
in three-dimensional (3D) space by a mobile robot.  The main
contributions are (1) an analysis of the capabilities a robot must
possess and the trade-offs involved in the design of an exploration
strategy, and (2) two provably-correct exploration strategies that
exploit these trade-offs and use visual sensors (e.g., cameras and
range sensors) to plan the robot's motion.  No such analysis existed
previously for the case of a robot moving freely in 3D space.  The
approach exploits the notion of the <EM>occlusion boundary</EM>, i.e.,
the points separating the visible from the occluded parts of an
object.  The occlusion boundary is a collection of curves that
``slide'' over the surface when the robot's position is continuously
controlled, inducing the visibility of surface points over which they
slide.  The paths generated by our strategies force the occlusion
boundary to slide over the entire surface.  The strategies provide a
basis for integrating motion planning and visual sensing under a
common computational framework.
</blockquote>

<LI> <B><A NAME="icra93-kutulakos">
     Vision-Guided Exploration:  A Step toward General Motion
     Planning in Three Dimensions</A></B><br>
     K. N. Kutulakos, 
     V. J. Lumelsky, and C. R. Dyer, <CITE> Proc. 1993 IEEE 
     Int. Conf. on Robotics and Automation</CITE>, 1993, 289-296. 
     (<!WA40><!WA40><!WA40><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/icra93-kutulakos.ps">postscript</A>
     or <!WA41><!WA41><!WA41><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/icra93-kutulakos.ps.gz">50K gzip'ed postscript</A>)<BR>
     (Longer version appears as Computer Sciences Department
     <CITE>Technical Report 1111</CITE>
     (<!WA42><!WA42><!WA42><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/tr1111-kutulakos.ps">postscript</A>
     or <!WA43><!WA43><!WA43><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/tr1111-kutulakos.ps.gz">90K gzip'ed postscript</A>).)<P>
<blockquote>
We present an approach for solving the path planning problem for a mobile
robot operating in an unknown, three dimensional environment containing
obstacles of arbitrary shape.  The main contributions of this paper are (1)
an analysis of the type of sensing information that is necessary and
sufficient for solving the path planning problem in such environments, and
(2) the development of a framework for designing a provably-correct
algorithm to solve this problem.  Working from first principles, without any
assumptions about the environment of the robot or its sensing capabilities,
our analysis shows that the ability to explore the obstacle surfaces (i.e.,
to make all their points visible) is intrinsically linked with the ability
to plan the motion of the robot.  We argue that current approaches to the
path planning problem with incomplete information simply do not extend to
the general three-dimensional case, and that qualitatively different
algorithms are needed.
</blockquote>



</UL>
<HR>
<P>
<H2><A NAME="motion">Motion Analysis</A></H2>
<UL>

<LI><B><A NAME="iccv95-seitz">
     Complete Scene Structure from Four Point Correspondences</A></B><BR>
     S. M. Seitz and C. R. Dyer,
     <CITE>Proc. 5th Int. Conf. Computer Vision</CITE>,
     1995, 330-337.
     (<!WA44><!WA44><!WA44><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/iccv95-seitz.ps">postscript</A>
     or <!WA45><!WA45><!WA45><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/iccv95-seitz.ps.gz">250K gzip'ed postscript</A>)<P>
<blockquote>
A new technique is presented for computing 3D scene structure from point 
and line features in monocular image sequences.  Unlike previous methods, 
the technique guarantees the completeness of the recovered scene, ensuring 
that every scene feature that is detected in each image is reconstructed.
The approach relies on the presence of four or more reference features 
whose correspondences are known in all the images.  Under an orthographic 
or affine camera model, the parallax of the reference features provides 
constraints that simplify the recovery of the rest of the visible scene.
An efficient recursive algorithm is described that uses a unified framework 
for point and line features.  The algorithm integrates the tasks of feature 
correspondence and structure recovery, ensuring that all reconstructible 
features are tracked.  In addition, the algorithm is immune to outliers and
feature-drift, two weaknesses of existing structure-from-motion techniques.
Experimental results are presented for real images.
</blockquote>

<LI> <B><A NAME="nram94-seitz">
     Detecting Irregularities in Cyclic Motion</A></B><BR>
     S. M. Seitz and C. R. Dyer,
     <CITE>Proc. Workshop on Motion of Non-Rigid and Articulated Objects</CITE>,
     1994, 178-185.
     (<!WA46><!WA46><!WA46><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/nram94-seitz.ps">postscript</A>
     or <!WA47><!WA47><!WA47><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/nram94-seitz.ps.gz">910K gzip'ed postscript</A>)<P>
<blockquote>
Real cyclic motions tend not to be perfectly even, i.e., the period varies 
slightly from one cycle to the next, because of physically important changes
in the scene.  A generalization of period is defined for cyclic motions
that makes periodic variation explicit.  This representation, called the 
period trace, is compact and purely temporal, describing the evolution 
of an object or scene without reference to spatial quantities such as 
position or velocity.  By delimiting cycles and identifying correspondences 
across cycles, the period trace provides a means of temporally registering 
a cyclic motion.  In addition, several purely temporal motion features are 
derived, relating to the nature and location of irregularities.  Results 
are presented using real image sequences and applications to athletic and 
medical motion analysis are discussed.
</blockquote>


<LI> <B><A NAME="cvpr94-seitz">
     Affine Invariant Detection of Periodic Motion</A></B><BR>
     S. M. Seitz and C. R. Dyer,
     <CITE>Proc. Computer Vision and Pattern Recognition Conf.</CITE>,
     1994, 970-975.
     (<!WA48><!WA48><!WA48><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/cvpr94-seitz.ps">postscript</A>
     or <!WA49><!WA49><!WA49><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/cvpr94-seitz.ps.gz">1M gzip'ed postscript</A>)<BR>
     (Different version appears as Computer Sciences Department
     <CITE>Technical Report 1225</CITE>
     (<!WA50><!WA50><!WA50><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/tr1225-seitz.ps">postscript</A>
     or <!WA51><!WA51><!WA51><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/tr1225-seitz.ps">890K gzip'ed postscript</A>).)<P>
<blockquote>
Current approaches for detecting periodic motion assume a stationary camera
and place limits on an object's motion.  These approaches rely on the
assumption that a periodic motion projects to a set of periodic image
curves, an assumption that is invalid in general.
Using affine-invariance,  we
derive necessary and sufficient conditions for an image sequence to be
the projection of a periodic motion.  No restrictions are placed on
either the motion of the camera or the object.
Our algorithm is shown to be provably-correct for
noise-free data and is extended to be robust with respect to
occlusions and noise.  The extended algorithm is evaluated with real and
synthetic image sequences.
</blockquote>


<LI> <B><A NAME="cvgip93-allmen">
     Computing Spatiotemporal Relations for Dynamic Perceptual
     Organization</A></B><BR>
     M. Allmen and C. R. Dyer,
     <CITE>Computer Vision, Graphics and Image Processing:  Image
     Understanding</CITE><B> 58</B>, 1993, 338-351.
     (<!WA52><!WA52><!WA52><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/cvgip93-allmen.ps">postscript</A>
     or <!WA53><!WA53><!WA53><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/cvgip93-allmen.ps.gz">200K gzip'ed postscript</A>)<BR>
     (Earlier version appeared
     as Computer Sciences Department
     <CITE>Technical Report 1130</CITE>
     (<!WA54><!WA54><!WA54><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/tr1130-allmen.ps">postscript</A>
     or <!WA55><!WA55><!WA55><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/tr1130-allmen.ps.gz">200K gzip'ed postscript</A>).) 
     <P>
<blockquote>
To date, the overwhelming use of motion in computational vision has been
to recover the three-dimensional structure of the
scene. We propose that there are other, more powerful, uses for motion.
Toward this end,
we define dynamic perceptual organization as an extension
of the traditional (static) perceptual organization approach.
Just as
static perceptual organization groups coherent features in an image,
dynamic perceptual organization groups coherent motions through an image
sequence. Using dynamic perceptual organization, we propose a new paradigm
for motion understanding and show why it can be
done independently of the recovery of scene structure and scene motion.
The paradigm starts with a spatiotemporal cube of image data and organizes
the paths of points so that
interactions between the paths and perceptual
motions such as common, relative and cyclic
are made explicit.
The results of this can then be used for high-level motion recognition tasks.
</blockquote>


<LI> <B><A NAME="qv93-waldon">
     Dynamic Shading, Motion Parallax and Qualitative Shape</A></B><BR>
     S. Waldon and C. R. Dyer, <CITE>Proc. IEEE 
     Workshop on Qualitative Vision</CITE>, 1993, 61-70.
     (<!WA56><!WA56><!WA56><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/qv93-waldon.ps">postscript</A>
     or <!WA57><!WA57><!WA57><A HREF="ftp://ftp.cs.wisc.edu/computer-vision/qv93-waldon.ps.gz">140K gzip'ed postscript</A>)<P>
<blockquote>
We address the problem of qualitative shape
recovery from moving surfaces. Our analysis is unique in that we
consider specular interreflections and explore the effects of both
motion parallax and changes in shading. To study this situation we
define an image flow field called the reflection flow field,
which describes the motion of reflection points and the motion of the
surface. From a kinematic analysis, we show that the reflection flow
is qualitatively different from the motion parallax because it is
discontinuous at or near parabolic curves. We also show that when the
gradient of the reflected image is strong, gradient-based flow
measurement techniques approximate the reflection flow field and not