|
Data Compression Techniques for Branch Prediction |
1999 |
J. S. De Bonet
|
abstract
Without special handling branch instructions would
disrupt the smooth flow of instructions into the
microprocessor pipeline. To eliminate this
disruption, many modern systems attempt to predict
the outcome of branch instructions, and use this
prediction to fetch, decode and even evaluate future
instructions. Recently, researchers have realized
that the task of branch prediction for processor
optimization is similar to the task of symbol
prediction for data compression. Substantial
progress has been made in developing approximations
to asymptotically optimal compression methods, while
respecting the limited resources available within
the instruction prefetching phase of the processor
pipeline. Not only does the infusion of data
compression ideas result in a theoretical
fortification of branch prediction, it results in
real and significant empirical improvement in
performance, as well. We present an overview of
branch prediction, beginning with early techniques
through more recent data compression inspired
schemes. A new approach is described which uses a
non-parametric probability density estimator similar
to the LZ77 compression scheme citeZiv77.
Results are presented comparing the branch
prediction accuracy of several schemes with those
achieved by our new approach. |
|
|
|
|
Poxels: Probabilistic Voxelized Volume Reconstruction |
1999 |
J. S. De Bonet and P. Viola
Proceedings of ICCV 1999
|
|
abstract
This paper examines the problem of reconstructing a
voxelized representation of 3D space from a series
of images. An iterative algorithm is used to find
the scene model which jointly explains all the
observed images by determining which region of space
is responsible for each of the observations. The
current approach formulates the problem as one of
optimization over estimates of these
responsibilities. The process converges to a
distribution of responsibility which accurately
reflects the constraints provided by the
observations, the positions and shape of both solid
and transparent objects, and the uncertainty which
remains. Reconstruction is robust, and gracefully
represents regions of space in which there is little
certainty about the exact structure due to limited,
non-existent, or contradicting data. Rendered
images of voxel spaces recovered from synthetic and
real observation images are shown. |
|
|
|
Flexible Histograms: A Multiresolution Target
Discrimination Model |
1998 |
J. S. De Bonet and P. Viola and J. W. Fisher III
Proceedings of SPIE 1998
|
abstract
In previous work we have developed a
methodology for texture recognition and synthesis
that estimates and exploits the dependencies across
scale that occur within
images (see DeBonet97a, DeBonet98a). In this paper
we discuss the application of this technique to
synthetic aperture radar (SAR) vehicle
classification. Our approach measures
characteristic cross-scale dependencies in training
imagery; targets are recognized when these
characteristic dependencies are detected. We
present classification results over a large public
database containing SAR images of vehicles.
Classification performance is compared to the Wright
Patterson baseline classifier (see Velten98).
These preliminary experiments indicate that this
approach has sufficient discrimination power to
perform target detection/classification in SAR. |
|
|
|
|
Structure-driven SAR image registration |
1998 |
J. S. De Bonet and A. Chao
Proceedings of SPIE 1998
|
|
abstract
We present a fully automatic method for
the alignment SAR images, which is capable of
precise and robust alignment. A multiresolution SAR
image matching metric is first used to automatically
determine tie-points, which are then used to perform
coarse-to-fine resolution image alignment. A
formalism is developed for the automatic
determination of tie-point regions that contain
sufficiently distinctive structure to provide strong
constraints on alignment. The coarse-to-fine
procedure for the refinement of the alignment
estimate both improves computational efficiency and
yields robust and consistent image alignment. |
|
|
|
Texture Recognition Using a Non-parametric Multi-Scale Statistical
Model |
1998 |
J. S. De Bonet and P. Viola
Proceedings IEEE Conf. on Computer Vision and Pattern Recognition 1998
|
abstract
We describe a technique for using the joint occurrence of local
features at multiple resolutions to measure the similarity between
texture images. Though superficially similar to a number of ``Gabor''
style techniques, which recognize textures through the extraction of
multi-scale feature vectors, our approach is derived from an accurate
generative model of texture, which is explicitly multi-scale and
non-parametric. The resulting recognition procedure is similarly
non-parametric, and can model complex non-homogeneous textures. We
report results on publicly available texture databases. In addition,
experiments indicate that this approach may have sufficient
discrimination power to perform target detection in synthetic aperture
radar images (SAR). |
|
|
|
|
Novel Statistical Multiresolution Techniques for
Image Synthesis, Discrimination, and Recognition |
May, 1997 |
J. S. De Bonet
|
|
abstract
By treating images as samples from
probabilistic distributions, the fundamental
problems in vision -- image similarity and object
recognition -- can be posed as statistical
questions. Within this framework, the crux of
visual understanding is to accurately characterize
the underlying distribution from which each image
was generated. Developing good approximations to
such distributions is a difficult, and in the
general case, unsolved problem.
A series of novel techniques is discussed for
modeling images by attempting to approximate such
distributions directly. These techniques provide
the foundations for texture synthesis, texture
discrimination, and general image classification
systems. |
|
|
|
Multiresolution Sampling Procedure for Analysis and Synthesis of Texture Images |
1997 |
J. S. De Bonet
ACM SIGGRAPH Computer Graphics 1997
|
abstract
This paper outlines a technique for treating input
texture images as probability density estimators
from which new textures, with similar appearance and
structural properties, can be sampled. In a
two-phase process, the input texture is first
analyzed by measuring the joint occurrence of
texture discrimination features at multiple
resolutions. In the second phase, a new texture is
synthesized by sampling successive spatial frequency
bands from the input texture, conditioned on the
similar joint occurrence of features at lower
spatial frequencies. Textures synthesized with this
method more successfully capture the characteristics
of input textures than do previous techniques. |
|
|
|
|
A Non-parametric Multi-Scale Statistical Model for Natural Images |
1997 |
J. S. De Bonet and P. Viola
Advances in Neural Information Processing 1997
|
|
abstract
The observed distribution of visual
images is far from uniform. On the contrary, images
have complex and important structure that can be
used for image processing, recognition and
analysis. There have been many proposed approaches
to the principled statistical modeling of images,
but each has been limited in either the complexity
of the models or the complexity of the images. We
present a non-parametric multi-scale statistical
model for images that can be used for recognition,
image de-noising, and in a ``generative mode'' to
synthesize high quality textures. |
|
|
|
Structure Driven Image Database Retrieval |
1997 |
J. S. De Bonet and P. Viola
Advances in Neural Information Processing 1997
|
abstract
A new algorithm is presented which approximates the perceived
visual similarity between images. The images are initially
transformed into a feature space which captures visual structure,
texture and color using a tree of filters. Similarity is the
inverse of the distance in this em perceptual feature space.
Using this algorithm we have constructed an image database system
which can perform example based retrieval on large image
databases. Using carefully constructed target sets, which limit
variation to only a single visual characteristic, retrieval rates
are quantitatively compared to those of standard methods. |
|
|
|
|
MIMIC: Finding Optima by Estimating Probability Densities |
1996 |
J. S. De Bonet and C. Isbell and P. Viola
Advances in Neural Information Processing 1996
|
abstract
In many optimization problems the
structure of solutions reflects complex
relationships between the different input
parameters. Any search of the cost landscape should
take advantage of these relations. For example,
experience may tell us that certain parameters are
closely related and should not be explored
independently. Similarly, experience may establish
that a subset of parameters must take on particular
values. We present a framework in which we analyze
the structural relationships of the optimization
landscape. A novel and efficient algorithm for the
estimation of this structure is derived. We use
knowledge of this structure to guide a randomized
search through the solution space. Our technique
obtains significant speed gains over other
randomized optimization procedures. |
|
|
|
Reconstructing Rectangular Polyhedra From Hand-Drawn Wireframe Sketches |
1995 |
J. S. De Bonet
|
|
abstract
Human observers are capable of
interpreting hand drawn sketches as three-
dimensional objects, despite inconsistencies in
lengths, variability in angles, and uncon- nected
vertices. The current system is an attempt to
achieve such robust performance in the limited
domain of sketches of wireframe rectangular
polyhedra. The Latest version of this system
reconstructs three-dimensional objects from perfect
drawings, in which all angles and line junctions are
consistent with projections of rectangular poly-
hedron. Ambiguities which are inherent in such
drawings are avoided by choosing a line grammar
which yields only a single interpretation. Next,
reconstruction from im- perfect drawings, in which
all the line segments were randomly perturbed, was
then achieved by grouping line endpoints into
vertices while simultaneously restricting lines to
particular orientations, and recovering
three-dimensional form from the corrected line
drawing. Finally, when actual hand-drawn sketches
were used as input, we found that to successfully
perform reconstruction the constraints on line
orientations had to be replaced with constraints
segment lengths and an additional three-dimensional
point clustering process was needed. |
|
|
|
Page loaded on December 25, 2024 at 09:52 PM.
Page last modified on
2006-05-27
|
Copyright © 1997-2024, Jeremy S. De Bonet.
All rights reserved.
|
|
