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Dates
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Titles
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Speakers
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9/12/05
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Intro to vision at Maryland
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Prof. Ramani
Duraiswami, David Jacobs , Rama
Chellappa, and Yiannis Aloimonos
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9/20/05
(Tue, 10am)
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The
Hybrid Imaging Approach
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Dr. Yoav Schechner
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9/28/05
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10/03/05
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Detecting
Rotational Symmetries (V. Shiv Naga
Prasad and Larry S. Davis); Closely Coupled Object Detection and Segmentation
(Liang Zhao, Larry S. Davis ); Face Recognition in
the Presence of Multiple Illumination Sources (Gaurav
Aggarwal, Rama Chellappa)
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V. Shiv Naga Prasad, Dr. Liang Zhao, and Gaurav Aggarwal (CFAR)
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10/10/05
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On-Line Density-Based Appearance
Modeling for Object Tracking, Deformation Invariant Image Matching, Object
Recognition in High Clutter Images Using Line Features, Robust Point Matching
for Two-Dimensional Nonrigid Shapes, On the
Equivalence of Common Approaches to Lighting Insensitive Recognition, Fast
Multiple Object Tracking via a Hierarchical Particle Filter, An Algebraic
Approach to Surface Reconstruction from Gradient Fields
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Bohyung Han, Haibin Ling, Philip David, Yefeng
Zheng, Margarita Osadchy,
Changjiang Yang, Amit Agrawal
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10/17/05
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No seminar, ICCV
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10/26/05
(Wed, 11 am)
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Prof. Kristin Dana (Rutgers University)
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10/31/05
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Revisiting
the Image Brightness Constraint
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Venu
Madhav Govindu
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11/28/05
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Mapping
land cover and land cover change using pattern recognition algorithms status
and challenges
|
Chengquan
Huang
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TBD
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Prof. Misha Kazhdan (asst. prof. in
graphics at Johns Hopkins)
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Friday, February 3/06, 11:00 AM
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Spectral Methods for Regularization in
Learning Theory
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Alessandro Verri
Universita' di Genova
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|
Friday, February 10/06, 2:00 PM
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Linear ordering of Objects
Using Graph 1-Factor
|
Gopi
Meenakshisundaram
University of California
Irvine
|
|
Monday, February 13/06, 2:00 PM
|
Video visualization - Beyond pixels and
frames
|
Yaron
Caspi
The Weizmann Institute
|
|
Monday, March 13/06, 2:00 PM
|
Autocalibration,
Crowd Segmentation and Person Reidentification - An
Industry View at Challenges in Visual Surveillance
|
Peter Tu and Nils Krahnstoever
Visualization &
Computer Vision Lab
GE Global Research
Niskayuna, New York
|
|
Friday, March
17/06, 11:00 AM
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Improving Audio Source Localization by
Learning the Precedence Effect
|
Kevin W. Wilson
MIT
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Monday, April 3,
11:00 AM
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Automatic
Sales Lead Generation from Web Data
|
Raghu
Krishnapuram
IBM India Research
Lab
New Delhi, INDIA
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TBD
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TBD
|
Venu Govindaraju
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Monday, April
10, 11:00 AM
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Gradient domain methods for recovering
shape, reflectance and illumination from images
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Amit Agrawal
University of Maryland
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Monday, April
17, 11:00 AM
|
Passive Vision, the Joy of Sitting Still
|
Robert Pless
Computer Science
and Engineering
Washington University
St. Louis, MO
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Friday, April
21, 1 PM
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Modeling Age Progression in Young
Faces Abstract
|
Narayanan Ramanathan
University Maryland
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Monday, April 24
|
On Unlocking Mysteries of Past
Civilizations as Challenging New Problems in Computer Vision and Pattern
Recognition
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David B. Cooper
Professor of Engineering
Brown University
|
|
Friday, April 28
|
A Joint Model of Illumination and Shape
for Visual Tracking
|
Amit
Kale
Center for
Visualization and Virtual Environments
University of Kentucky
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Monday, May 1
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The Fundamental Matrix
in Human Action Recognition
|
Dr.
Mubarak Shah
Computer Vision Lab
School of Computer Science
University of Central Florida
http://www.cs.ucf.edu/~vision/
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Friday, May 5,
2:00 PM
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Non-linear dimensionality reduction
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Alfred Hero
University of Michigan
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Monday, May 8,
11:00 AM
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Informational Intelligence
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Dr. Stefan Jaeger
Institute for
Advanced Computer Studies
Language and Media
Processing Laboratory
University of Maryland
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Friday, May 12,
1:00 PM
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Interpolation artifacts in sub-pixel variational image processing
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Gustavo K. Rohde
Naval Research Laboratory
Washington, DC
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TBD
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TBD
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Sameer Shirdhonkar
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Top
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09/20/05
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The Hybrid Imaging Approach
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Speaker
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Dr. Yoav Schechner,
Dept. of
Electrical Eng. Technion - Israel Inst. Technology, Haifa
http://www.ee.technion.ac.il/~yoav/
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Abstract
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Computer vision typically regards images as given entities
to be processed. However, richer information can be extracted by modifying
and analyzing the imaging process itself. This modification includes the
sensor or the illumination, in conjunction to carefully tailored algorithms.
This hybridization exploits the advantages of both the sensor and the
algorithmic components of a vision system. We describe our recent results in
this approach, which apply to the full observation setup: illumination of the
object, scattering media between the object and the camera, optical phenomena
in the camera, and multi-sensor computational processing. In particular, the
talk shows new results in the development of multiplexing for enhanced
imaging under varying illumination directions. We then describe denoising that is tailored to vision in scattering media.
In addition, we describe a method for blindly estimating simultaneous spatio-temporal inconsistencies of sensors (gain, vignetting, radiometric response). Finally, we explore
audio-visual interaction, whereby a vision algorithm using a sparsity prior uniquely pinpoints the pixels that
correspond to sound sources, with high definition.
Top
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10/03/05
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CFAR ICCV papers
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Speaker
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V. Shiv Naga Prasad, Dr. Liang Zhao, and Gaurav Aggarwal (CFAR)
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Abstract
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Detecting Rotational Symmetries
V. Shiv Naga
Prasad and Larry S. Davis
Abstract:
We present an algorithm for detecting
multiple rotational symmetries in natural images. Given an image, its
gradient magnitude field is computed, and information from the gradients is
spread using a diffusion process in the form of a Gradient Vector Flow (GVF)
field. We construct a graph whose nodes correspond to pixels in the image,
connecting points that are likely to be rotated versions of one another. The
$n$-cycles present in the graph are made to vote for $C_n$
symmetries, their votes being weighted by the errors in transformation
between GVF in the neighborhood of the voting points, and the irregularity of
the $n$-sided polygons formed by the voters. The votes are accumulated
at the centroids of possible rotational symmetries,
generating a confidence map for each order of symmetry. We tested the method
with several natural images.
Closely Coupled Object Detection and Segmentation
Liang
Zhao, Larry S. Davis
Abstract
We propose a closely coupled
object detection and segmentation algorithm for enhancing both processes in a
cooperative and iterative manner. Figure-ground segmentation reduces the
effect of background clutter on template matching; the matched template
provides shape constraints on segmentation. More precisely, we estimate the
probability of each pixel belonging to the foreground by a weighted sum of
the estimates based on shape and color alone. The weight on the shape-based
estimate is related to the probability that a familiar object is present and
is updated dynamically so that we enforce shape constraints only where the
object is present. Experiments on detecting people in images of cluttered
scenes demonstrate that the proposed algorithm improves both segmentation and
detection. More accurate object boundaries are extracted; higher object
detection rates and lower false alarm rates are achieved than performing the
two processes separately or sequentially.
Face Recognition in the Presence of Multiple Illumination Sources
Gaurav Aggarwal,
Rama Chellappa.
Abstract:
Most existing face recognition algorithms work well for
controlled images but are quite susceptible to changes in illumination and
pose. This has led to the rise of analysis-by- synthesis approaches due to
their inherent potential to handle these external factors. Though these
approaches work quite well, most of them assume that the face is illuminated
by a single light source which is usually not true in realistic conditions.
In this paper, we propose an algorithm to recognize faces illuminated by
arbitrarily placed, multiple light sources. The algorithm does not need to
know the number of light sources and works extremely well even while
recognizing faces illuminated by different number of light sources. Results
using this algorithm are reported on multiple-illumination datasets generated
from PIE [10] and Yale Face Database B [5]. We also highlight the importance
of the hard non-linearity in the Lambert’s law which is often ignored,
probably to linearize the estimation process.
Top
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10/10/05
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CFAR ICCV papers
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Speaker
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Bohyung
Han, Haibin Ling, Philip David, Yefeng
Zheng, Margarita Osadchy,
Changjiang Yang, Amit Agrawal (CfAR)
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Abstract
|
On-Line Density-Based Appearance Modeling for Object
Tracking
Bohyung Han,
Larry Davis
Object tracking is a challenging
problem in real-time computer vision due to variations of lighting condition,
pose, scale, and view-point over time. However, it is exceptionally difficult
to model appearance with respect to all of those variations in advance;
instead, on-line update algorithms are employed to adapt to these changes.
We present a new on-line
appearance modeling technique which is based on sequential density
approximation. This technique provides accurate and compact representations
using Gaussian mixtures, in which the number of Gaussians is automatically
determined. This procedure is performed in linear time at each time step,
which we prove by amortized analysis. Features for each pixel and rectangular
region are modeled together by the proposed sequential density approximation
algorithm, and the target model is updated in scale robustly. We show the
performance of our method by simulations and tracking in natural videos.
Deformation
Invariant Image Matching
Haibin
Ling and David Jacobs
We propose a novel framework
to build descriptors of local intensity that are invariant to general
deformations. In this framework, an image is embedded as a 2D surface in 3D
space, with intensity weighted relative to distance in $x$-$y$. We show that
as this weight increases, geodesic distances on the embedded surface are less
affected by image deformations. In the limit, distances are deformation
invariant. We use geodesic sampling to get neighborhood samples for interest
points, then use a geodesic-intensity histogram (GIH) as a deformation
invariant local descriptor. In addition to its invariance, the new descriptor
automatically finds its support region. This means it can safely gather
information from a large neighborhood to improve discriminability.
Furthermore, we propose a matching method for this descriptor that is
invariant to affine lighting changes. We have tested this new descriptor on
interest point matching for two data sets, one with synthetic deformation and
lighting change, another with real non-affine deformations. Our method shows
promising matching results compared to several other approaches.
Posters
Object Recognition
in High Clutter Images Using Line Features
Philip David and
Daniel DeMenthon
We present an object recognition algorithm that uses model
and image line features to locate complex objects in high clutter
environments.
Finding correspondences between model and image features
is the main challenge in most object recognition systems. In our approach,
corresponding line features are determined by a three-stage process. The
first stage generates a large number of approximate pose hypotheses from
correspondences of one or two lines in the model and image. Next, the pose
hypotheses from the previous stage are quickly ranked by comparing local
image neighborhoods to the corresponding local model neighborhoods. Fast
nearest neighbor and range search algorithms are used to implement a distance
measure that is unaffected by clutter and partial occlusion.
The ranking of pose hypotheses
is invariant to changes in image scale, orientation, and partially invariant
to affine distortion. Finally, a robust pose estimation algorithm is applied
for refinement and verification, starting from the few best approximate poses
produced by the previous stages. Experiments on real images demonstrate
robust recognition of partially occluded objects in very high clutter
environments.
Robust Point
Matching for Two-Dimensional Nonrigid Shapes
Yefeng
Zheng and David Doermann
Recently, nonrigid shape
matching has received more and more attention. For nonrigid
shapes, most neighboring points cannot move independently under deformation
due to physical constraints.
Furthermore, the rough
structure of a shape should be preserved under a deformation, otherwise even
people can recognize the shape. Therefore, though the absolute distance
between two points may change significantly, the neighborhood of a point is
well preserved in general. Based on this observation, we formulate point
matching as a graph matching problem. Each point is a node in the graph, and
two nodes are connected by an edge if their Euclidean distance is less than a
threshold. The optimal match between two graphs is the one that maximizes the
number of matched edges. The shape context distance is used to initialize the
graph matching, followed by relaxation labeling to refine the match. Nonrigid deformation is overcome by bringing one shape
closer to the other in each iteration using deformation parameters estimated
from the current point correspondence. Experiments on real and synthesized
data demonstrate the effectiveness of our approach: it outperforms the shape
context and TPS-RPM algorithms under nonrigid
deformation and noise on a public data set.
On the Equivalence
of Common Approaches to Lighting Insensitive Recognition
Margarita Osadchy, David Jacobs, Michael Lindenbaum
Lighting variation is commonly
handled by methods invariant to additive and multiplicative changes in image
intensity. It has been demonstrated that comparing images using the direction
of the gradient can produce broader insensitivity to changes in lighting
conditions, even for 3D scenes. We analyze two common approaches to
image comparison that are invariant, normalized correlation using small
correlation windows, and comparison based on a large set of oriented
difference of Gaussian filters. We show analytically that these methods
calculate a monotonic (cosine) function of the gradient direction difference
and hence are equivalent to the direction of gradient method. Our
analysis is supported with experiments on both synthetic and real scenes.
Fast Multiple
Object Tracking via a Hierarchical Particle Filter
Changjiang Yang,
Ramani Duraiswami, Larry Davis
An
Algebraic Approach to Surface Reconstruction from Gradient Fields
Amit Agrawal,
Rama Chellappa, Ramesh Raskar.
Top
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10/31/05
|
Revisiting the
Image Brightness Constraint
|
|
Speaker
|
Venu
Madhav Govindu
|
|
Abstract
|
I will present a principled
approach to using the image brightness constraint for optical flow
algorithms. Using a simple noise model, a probabilistic representation for
optical flow will be derived. It will be shown that this representation
subsumes existing approaches to flow modeling and explains the behavior of
some conventional approaches. Modified algorithms will be developed for a
stratification of smoothness assumptions, namely constant, affine and smooth
flow.
Top
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Friday,
February 3, 11:00 AM
|
Spectral Methods for Regularization in
Learning Theory
|
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Speaker
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Alessandro Verri
Universita' di Genova
|
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Abstract
|
In this talk we show that a large
class of regularization methods designed for solving ill-posed inverse
problems gives rise to consistent learning algorithms. The intuition behind
our approach is that, by looking at regularization from a filter function
perspective, filtering out undesired components of the target function
ensures stability with respect to the random sampling thereby inducing good
generalization properties. We present a formal derivation of the methods
under study by recalling that learning can be written as the inversion of a
linear embedding equation given a stochastic discretization.
Consistency as well as finite sample bounds are derived for both regression
and classification.
(joint work with Lorenzo Rosasco and Ernesto De Vito) alessandro
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Friday, February 10,
2:00 PM
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Linear ordering of Objects Using Graph
1-Factor
|
|
Speaker
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Gopi Meenakshisundaram
University
of California Irvine
|
|
Abstract
|
Destined to live with the RAM
model of computing for a foreseeable future, optimal linear ordering of
elements to improve cache coherency and performance of out of core algorithms
becomes crucial. While ordering the elements, the access pattern has to
be taken into account, which in turn is application dependent. Assuming,
between pairs of elements, we have the probability estimates of the second
element being accessed after the first, we propose a solution to the problem
of linear ordering of elements using 1-factor graph partitioning algorithm.
The versatility of this
algorithm is shown by its application to various problems in computer
graphics including cache-coherent triangle ordering (also called stripification), simplification, compression, efficient
back-face culling, quadrilateral mesh stripification,
and tetrahedral mesh stripification. In simplicial complex realization of manifold spaces, the
algorithm can be extended to generate space-filling curves. The graph
abstraction of the problem, makes the solution seamlessly extendable to
elements in higher dimensions including higher dimensional databases and nodes
of the hierarchical partitioning of the objects like quadtrees
and octrees in computer graphics.
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Monday, February 13, 2:00 PM
|
Video visualization - Beyond pixels and
frames
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Speaker
|
Yaron Caspi
The Weizmann
Institute
|
|
Abstract
|
Video data is represented by
pixels and frames. This restricts the way it is captured, accessed and
visualized. On one hand, visual information is distributed across all
frames, and therefore, in order to depict the visual information, the entire
video sequence must be viewed sequentially, frame by frame. On the
other hand, important visual information is lost by the limited frame
rate. Similarly in the spatial domain, sensor and optics limit the
capturing process, while huge redundancy prevents an efficient visualization
of information. In this talk I will show how to exceed both limitations of
capturing devices and of visual displays. In particular, how fusion of
information from multiple sources allows to exceed temporal and spatial
limitations, and how visualization of video data can benefit from importance
ranking. I will describe a process that depicts the essence of video or
animation, by embedding high dimensional data in low dimensional Euclidean
space. I will also show how super-pixels (in contrast to pixels) contribute
to the exploitation of temporal redundancy for the task of spatial
segmentation of regions with high importance
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Monday, March 13, 2:00 PM
|
Autocalibration,
Crowd Segmentation and Person Reidentification - An
Industry View at Challenges in Visual Surveillance
|
|
Speakers
|
Peter Tu and Nils Krahnstoever
Visualization & Computer Vision Lab
GE Global Research
Niskayuna,
New York
|
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Abstract
|
The Visualization and Computer Vision
Group at GE Global Research serves a large number of GE businesses in areas
such as medical image processing, industrial inspection, and intelligent
video systems. We will give a brief overview of our group and will then
focus on recent security related projects. We will motivate specific research
needs that originate from our collaboration with GE Security. The objective
of the ongoing research is to extend the functionality and robustness of the
intelligent video product line currently offered by GE. The technical
presentation will include recent work on crowd segmentation, person-reidentification and auto-calibration. The crowd
segmentation work will focus on a parts-based approach that is able to
segment crowds using a version of Expectation Maximization. A key feature of
this approach is that the number of people in the crowd need not be known in
advance. Our person-reidentification work focuses
on the ability to fit deformable models to images and generate stable
signatures based on color and edge information. Finally, the talk will
present an approach to reliably and automatically perform metric camera
calibration from detections and tracks of people and show how metric
calibration can be utilized for various detection, tracking and crowd segmentation
tasks.
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Friday, March 17, 11:00 AM
|
Improving Audio Source Localization by
Learning the Precedence Effect
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Speaker
|
Kevin W. Wilson
MIT
|
|
Abstract
|
Speech source localization in
reverberant environments has proved difficult for microphone array
systems. One reason for this is the failure of most techniques to
properly model localization error for nonstationary
signals in reverberant environments. In contrast, the human auditory system
can robustly localize nonstationary sources, such
as speech, in reverberant environments. Insight into the human auditory
system's "error model" is provided by the precedence effect, in
which people localize sources based largely on cues from sound onsets and
which has been hypothesized to improve localization performance in
reverberant environments. Inspired by the precedence effect, we consider the
problem of learning a mapping from reverberated signal spectrograms to
localization precision.
Inspired by the precedence
effect, we consider the problem of learning a mapping from reverberated
signal spectrograms to localization precision. We find this mapping using
ridge regression, and using the learned mappings in the generalized
cross-correlation framework, we demonstrate improved localization performance.
Additionally, the resulting mappings exhibit behavior consistent with the
psychoacoustics of the precedence effect.
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Monday, April 3, 11:00 AM
|
Automatic Sales
Lead Generation from Web Data
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Speaker
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Raghu Krishnapuram
IBM India
Research Lab
New Delhi, INDIA
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|
Abstract
|
The World Wide Web has grown
into an "information-mesh", with most important facts being
reported through Web sites. Several news papers, press releases, trade journals,
business magazines and other related sources are on-line. These sources could
be used to identify prospective buyers of products and services
automatically. In this talk, we present a system called ETAP (Electronic
Trigger Alert Program) that extracts "trigger events" from Web data
that help in identifying prospective customers. Trigger events are events of
corporate relevance and are indicative of the propensity of companies to
purchase new products. Examples of trigger events are "change in management",
"revenue growth" and "mergers & acquisitions".
We pose the problem of trigger event extraction as a classification problem
and present methods to generate the training data required to learn the
classifiers automatically. We also propose a method of feature abstraction
that uses named entity recognition to solve the problem of data sparsity. Our experiments show the effectiveness of the
method.
Biographical
sketch:
Raghu Krishnapuram received his Ph.D. degree in electrical and
computer engineering from Carnegie Mellon University,
Pittsburgh,
in 1987. From 1987 to 1997, he was on the faculty of the Department of
Computer Engineering and Computer Science at the University
of Missouri, Columbia. >From 1997 to 2000, Dr. Krishnapuram was a Full Professor at the Department of
Mathematical and Computer Sciences, Colorado School of Mines,Golden,
Colorado. Since then, he has been at at IBM India
Research Lab, New Delhi.
Dr. Krishnapuram's research encompasses many
aspects of Web mining, information retrieval, e-commerce, fuzzy set theory,
neural networks, pattern recognition, computer vision, and image processing.
He has published over 160 papers in journals and conferences in these areas.
Dr. Krishnapuram is an IEEE Fellow, and a co-author
(with J. Bezdek, J. Keller and N. Pal) of the book
"Fuzzy Models and Algorithms for Pattern Recognition and Image
Processing".
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Monday, April 10, 11:00 AM
|
Gradient domain
methods for recovering shape, reflectance and
illumination
from images
|
|
Speaker
|
Amit Agrawal
University of Maryland
|
|
Abstract
|
Classical approaches for
recovering shape such as Photometric Stereo and Shape from Shading requires
surface reconstruction from the estimated gradient field, which is usually
non-integrable. Most of the previous approaches
lacks the property of local error confinement and cannot handle outliers. We
analyze the space of all possible solutions for surface reconstruction from
gradient fields and present a general framework for obtaining meaningful
solutions in this space. We derive several new algorithms using our framework
that give feature preserving reconstructions in presence of noise and
outliers.
Traditionally, edge suppression
is achieved by setting the image gradients to zero using thresholds. We present
an approach for removing edges in an image using another image taken under
different illumination conditions by (a) gradient projection and (b) gradient
field transformation using cross-projection tensors. We show results on
several applications such as recovering intrinsic images
(reflectance/illumination maps), recovering foreground layer, removing
shadows from color images and removing glass reflections.
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Monday, April 17, 11:00 AM
|
Passive
Vision, the Joy of Sitting Still
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|
Speaker
|
Robert
Pless
Computer Science
and Engineering
Washington University
St. Louis, MO
|
|
Abstract
|
Many classical vision
algorithms mimic the structure and function of the human visual system — a
strategy which has successfully driven research into stereo and structure
from motion based algorithms. However, for problems such as surveillance,
tracking, anomaly detection and scene segmentation; the lessons of the human
visual system are not so clear. For these problems, significant advantages
are possible in a "Passive Vision" paradigm that advocates
collecting statistical representations of scene variation from a single
viewpoint over very long time periods. This talk motivates this approach by
providing a collection of examples where very simple statistics, which can be
easily kept over very long time periods, dramatically simplify scene
interpretation problems including segmentation, feature attribution, and
offer orders of magnitude performance improvement for tracking algorithms.
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Friday, April 21, 1:00 PM
|
Modeling Age
Progression in Young Faces Abstract
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|
Speaker
|
Narayanan
Ramanathan
University of Maryland
|
|
Abstract
|
We propose a craniofacial
growth model that characterizes growth related shape variations observed in human
faces during formative years. The model draws inspiration from the `revised' cardioidal strain transformation model proposed in
psychophysical studies related to craniofacial growth. The model takes into
account anthropometric evidences collected on facial growth and hence is in
accordance with the observed growth patterns in human faces across years. We
characterize facial growth by means of growth parameters defined over facial
landmarks often used in anthropometric studies. We illustrate how the age-based
anthropometric constraints on facial proportions translate into linear and
non-linear constraints on facial growth parameters and propose methods to
compute the optimal growth parameters. The proposed craniofacial growth model
can be used to predict one's appearance across years and to perform face
recognition across age progression. This is demonstrated on a database of age
separated face images of individuals under 18 years of age.
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Monday, April 24, 11:00 AM
|
On Unlocking
Mysteries of Past Civilizations as Challenging New Problems in Computer
Vision and Pattern Recognition
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Speaker
|
David
B. Cooper
Professor of
Engineering
Brown University
|
|
Abstract
|
Archaeological excavation-site
analysis and digital preservation of national heritage are two fields using
data and having needs that are wonderful for geometry-based applications of
computer vision and pattern recognition. In this talk I will briefly
illustrate a number of projects – past, present, and future – that we have
been involved in, and I will go into extensive detail on our work on the
automatic reconstruction of ceramic pot representations from 3D noisy
dense-data measurements of their damaged fragments (i.e., their pot sherds). Archaeological site analysis by computer
is a wonderful playground for developing new
computer-vision/pattern-recognition methodologies for analyzing, inferencing, and manipulating 2D and 3D geometric
structure from noisy data. The attraction is that the geometry involved
is highly varied, there is an enormous amount of fragments available,
little work has been done in using sophisticated shape analysis for working
with the data, and many problems amenable to solution have yet to be formulated.
Ceramic
pot fragments are among the most numerous finds at archaeological sites and
also among the most useful for making inferences about the history and use of
the site. Typically, a ceramic pot breaks into 15 to 30 pieces,
and these pot sherds might be collected into piles,
each of the order of 200 sherds, which contain
incomplete sets of sherds from a number of
pots. Since it takes a skilled technician anywhere from a few hours to
a few days to reconstruct one such pot, very few pots are
reconstructed. In this talk, I formulate the problem of automatic
pot model estimation by computer based on 3D dense-data laser-scan
measurements of the sherd outer surfaces (typically
3,000 to more than 10,000 points per sherd) as a
problem in statistical learning of 3D freeform geometry based on noisy
measurement data of unorganized fragments. What makes the problem
difficult is that sherds may be missing, they may
be small thus containing little surface shape information, they are chipped
and they may be eroded.
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Friday, April 28, 1:00 PM
|
A Joint Model of
Illumination and Shape for Visual Tracking
|
|
Speaker
|
Amit Kale
Center for
Visualization and Virtual Environments
University of Kentucky
|
|
Abstract
|
Visual tracking involves generating
an inference about the motion of an object from measured image locations in a
video sequence. In this talk I will present a unified framework that
incorporates shape and illumination in the context of visual tracking. First,
we introduce a multiplicative, low dimensional model of illumination that is
defined by a linear combination of a set of smoothly changing basis
functions. Secondly, we show that a small number of centroids
in this new space can be used to represent the illumination conditions
existing in the scene. These centroids can be
learned from ground truth and are shown to generalize well to other objects
of the same class for the scene. Finally we show how this illumination model
can be combined with shape in a probabilistic sampling framework. Results of
the joint shape-illumination model will be demonstrated in the context of
vehicle and face tracking in challenging conditions.
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Monday,
May 1, 11:00 AM
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The Fundamental Matrix in Human Action Recognition
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Speaker
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Dr. Mubarak Shah
Computer
Vision Lab
School of Computer
Science
University |