Publications and Documents from 1998 to Present
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Computational modeling and exploration of contour
integration
for visual saliency - 2005
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| T. Nathan Mundhenk |
and |
Laurent Itti |
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| Biological Cybernetics |
vol 93 |
issue 3 |
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September |
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Abstract:
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We propose a computational model of
contour
integration for visual saliency. The model uses biologically plausible
devices
to simulate how the representations of elements aligned collinearly
along a contour in an image are enhanced. Our model
adds such devices as a dopamine-like fast plasticity, local GABAergic
inhibition and multi-scale processing of images.
The fast plasticity addresses the problem of how neurons in visual
cortex seem to be able to influence neurons they are
not directly connected to, for instance as observed in contour closure
effect. Local GABAergic inhibition is used to control
gain in the system without using global mechanisms, which may be
non-plausible given the limited reach of axonal arbors in
visual cortex. The model is then used to explore not only its validity
in real and artificial images, but to discover some
of the mechanisms involved in processing of complex visual features
such as Junections and end-stops as well as contours.
We present evidence for the validity of our model in several phases,
starting with local enhancement of only a few collinear
elements. We then test our model on more complex contour integration
images with a large number of Gabor elements. Sections
of the model are also extracted and used to discover how the model
might relate contour integration neurons to neurons that
process end-stops and Junections. Finally, we present results from real
world images. Results from the model suggest that
it is a good current approximation of contour integration in human
vision. As well, it suggests that contour integration mechanisms may be
strongly related to mechanisms for detecting end-stops and Junection
points. Additionally, a contour
integration mechanism may be involved in finding features for objects
such as faces. This suggests that visual cortex
may be more information efficient and that neural regions may have
multiple roles. |
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Distributed biologically based real time tracking in
the
absence of prior target information - 2005
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| T. Nathan Mundhenk, |
Jacob Everist, |
Chris Landauer, |
Laurent Itti |
and |
Kirstie Bellman |
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| Proc. SPIE Conference on
Intelligent Robots
and Computer Vision XXIII: Algorithms, Techniques, and Active Vision |
vol 6006 |
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pg 330-341 |
Boston, Ma |
October |
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Abstract:
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We are developing a distributed
system for the
tracking of people and objects in complex scenes and environments using
biologically based algorithms.
An important component of such a system is its ability to track targets
from multiple cameras at multiple viewpoints. As such, our system must
be able
to extract and analyze the features of targets in a manner that is
sufficiently invariant of viewpoints, so that they can share
information about
targets, for purposes such as tracking. Since biological organisms are
able to describe targets to one another from very different visual
perspectives,
by discovering the mechanisms by which they understand objects, it is
hoped such abilities can be imparted on a system of distributed agents
with many
camera viewpoints. Our current methodology draws from work on saliency
and center surround competition among visual components that allows for
real time
location of targets without the need for prior information about the
targets visual features. For instance, gestalt principles of color
opponencies, continuity and motion form a basis to locate targets in a
logical manner. From this, targets can be located and tracked
relatively reliably for short periods. Features can then be extracted
from salient targets allowing for a signature to be stored which
describes the basic visual features of a target.
This signature can then be used to share target information with other
cameras, at other viewpoints, or may be used to create the prior
information needed for other types of trackers. Here we discuss such a
system, which, without the need for prior target feature information,
extracts salient features from a scene, binds them and uses the bound
features as a set for understanding trackable objects. |
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Biologically inspired feature based categorization of
objects
- 2004
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| T. Nathan Mundhenk, |
Vidhya Navalpakkam, |
Hendrik Makaliwe, |
Shrihari Vasudevan |
and |
Laurent Itti |
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| Proc. SPIE Human Vision and
Electronic Imaging
IX |
vol 5292 |
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pg 330-341 |
San Jose, California |
January |
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Abstract:
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We have developed a method for
clustering
features into objects by taking those features which include intensity,
orientations and colors from the most salient points in an image as
determined by our biologically motivated
saliency program. We can train a program to cluster these features by
only supplying as training input the number of
objects that should appear in an image. We do this by clustering from a
technique that involves linking nodes in a
minimum spanning tree by not only distance, but by a density metric as
well. We can then form classes over objects
or object segmentation in a Novemberel validation set by training over
a set of seven soft and hard parameters. We discus
as well the uses of such a flexible method in landmark based navigation
since a robot using such a method may have
a better ability to generalize over the features and objects. |
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Teaching the computer subjective notions of feature
connectedness in a visual scene for real time vision - 2004
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| T. Nathan Mundhenk, |
Chris Landauer, |
Kirstie Bellman, |
Michael A. Arbib |
and |
Laurent Itti |
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| Proc. SPIE Conference on
Intelligent Robots
and Computer Vision XXII: Algorithms, Techniques, and Active Vision |
vol 5608 |
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pg 136-147 |
Philadelphia, PA |
October |
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Abstract:
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We discus a tool kit for usage in
scene
understanding where prior information about targets is not necessarily
understood. As such, we give it a notion of connectivity such that it
can classify features in an image for the purpose
of tracking and identification. The tool VFAT (Visual Feature Analysis
Tool) is designed to work in real time in an
intelligent multi agent room. It is built around a modular design and
includes several fast vision processes. The first
components discussed are for feature selection using visual saliency
and Monte Carlo selection. Then features that
have been selected from an image are mixed into useful and more complex
features. All the features are then
reduced in dimension and contrasted using a combination of Independent
Component Analysis and Principle
Component Analysis (ICA/PCA). Once this has been done, we classify
features using a custom non-parametric
classifier (NPclassify) that does not require hard parameters such as
class size or number of classes so that VFAT
can create classes without stringent priors about class structure.
These classes are then generalized using Gaussian
regions which allows easier storage of class properties and computation
of probability for class matching. To speed
up to creation of Gaussian regions we use a system of rotations instead
of the traditional Psuedo-inverse method. In
addtion to discussing the structure of VFAT we discuss training of the
current system which is relatively easy to
perform. ICA/PCA is trained by giving VFAT a large number of random
images. The ICA/PCA matrix is computed
by features extracted by VFAT. The non-parametric classifier NPclasify
it trained by presenting it with images of
objects having it decide how many objects it thinks it sees. The
difference between what it sees and what it is
supposed to see in terms of the number of objects is used as the error
term and allows VFAT to learn to classify
based upon the experimenters subjective idea of good classification. |
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Key Words:
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iRoom, Biological, Vision, Tool, Multi Agent,
Saliency,
Real Time |
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Camera Localization methods for Intelligent Room
Systems
using RF Techniques - 2004
|
| Pradeep NataraJanuary, |
T. Nathan Mundhenk, |
Kirstie Bellman, |
Michael A. Arbib |
and |
Laurent Itti |
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| Proc. SPIE Conference on
Intelligent Robots
and Computer Vision XXII: Algorithms, Techniques, and Active Vision |
vol 5608 |
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pg 177-187 |
Philadelphia, PA |
October |
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Abstract:
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One of the important components of
a
multi sensor “intelligent” room, which can
observe, track and react to its occupants, is a multi
camera system. This system involves the
development of algorithms that enable a set of
cameras to communicate and cooperate with each
other effectively so that they can monitor the
events happening in the room. To achieve this, the
cameras typically must first build a map of their
relative locations. In this paper, we discuss RF and
vision based techniques for estimating distances
between cameras. The algorithm proposed for RF
can estimate distances with relatively good
accuracy even in the presence of random noise.
We have also described a vision-based algorithm
for localization using stereovision techniques. This
algorithm can compute the location of the camera
given the location of a calibration object and vice
versa. |
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Schizophrenia and the Mirror Neuron System - 2004
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| Michael A. Arbib |
and |
T. Nathan Mundhenk |
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| Neuropsychologia |
vol 43 |
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pg 268-280 |
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October |
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Abstract:
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We analyze how data on the mirror
system for
grasping in macaque and human ground the mirror system hypothesis for
the evolution of the
language-ready human brain, and then focus on this putative relation
between hand movements and speech to contribute to the understanding
of how it may be that a schizophrenic patient generates an action
(whether manual or verbal) but does not attribute the generation of
that
action to himself.We make a crucial discussion between self-monitoring
and attribution of agency.We suggest that vebal hallucinations occur
when an utterance progresses through verbal creation pathways and
returns as a vocalization observed, only to be dismissed as external
since
no record of its being created has been kept. Schizophrenic patients on
this theory then confabulate the agent. |
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Key Words:
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FARS model, Grasping, Mirror system,
Schizophrenia,
Agency |
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Contour-facilitation in a model of bottom-up attention
- 2003
|
| Rob J. Peters, |
T. Nathan Mundhenk, |
Laurent Itti |
and |
Christof Koch |
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| Proc. Society for Neuroscience
Annual Meeting
(SFN 03) |
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November |
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Abstract:
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Previously we showed that
interactions among
overlapping orientation-tuned units could improve a bottom-up attention
model in predicting human eye movement targets. We have now extended
this work to address the question of how elongated
contours affect saliency in natural scenes. We used a model of
contour-facilitation based on putative long-range excitatory and
inhibitory interactions among orientation-tuned units in early visual
cortex. Each unit tends to excite other units that
are nearly collinear, and inhibit those that are nearly parallel. We
tested the model on artificial images such as arrays of Gabor patches
with embedded implicit contours ('snakes'), as well as natural images
such as outdoor photos and overhead satellite photos. Our results agree
with previous psychophysical measurements of human observers'
sensitivity to implicit contours such as Gabor snakes; we found that a
basic bottom-up saliency model was completely blind to such contours,
while an enhanced saliency model with contour-facilitiation module
could consistently identify the embedded contour (left figure) as the
most salient element in the image (right figure). Preliminary
eyetracking results suggest that observers are less sensitive to high
spatial-frequency contours in natural scenes. |
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Key Words:
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Computational Modeling, Human Psychophysics,
Model of
Bottom-Up Saliency-Based Visual Attention, Human Eye-Tracking Research |
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Low-cost high-performance mobile robot design utilizing
off-the-shelf parts and the Beowulf concept: the Beobot project - 2003
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| T. Nathan Mundhenk, |
Chris Ackerman, |
Daesu Chung, |
Nitin Dhavale, |
Brian Hudson, |
Reid Hirata, |
Eric Pichon, |
Zhan Shi, |
April Tsui |
and |
Laurent Itti |
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| Proc. SPIE Conference on
Intelligent Robots
and Computer Vision XXI |
vol 5267 |
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pg 293-303 |
Providence, RI |
October |
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Abstract:
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Utilizing off the shelf low cost
parts, we have
constructed a robot that is small, light, powerful and relatively
inexpensive (less than $3900). The system is constructed around the
Beowulf concept of linking multiple discrete
computing units into a single cooperative system. The goal of this
project is to demonstrate a new robotics platform
with sufficient computing resources to run biologically-inspired vision
algorithms in real-time. This is accomplished
by connecting two dual-CPU embedded PC motherboards using fast gigabit
Ethernet. The motherboards contain
integrated Firewire, USB and serial connections to handle camera,
servomotor, GPS and other miscellaneous
inputs/outputs. Computing systems are mounted on a
servomechanism-controlled off-the-shelf “Off Road”
RC car.
Using the high performance characteristics of the car, the robot can
attain relatively high speeds outdoors. The robot
is used as a test platform for biologically-inspired as well as
traditional robotic algorithms, in outdoor navigation and
exploration activities. Leader following using multi blob tracking and
segmentation, and navigation using statistical
information and decision inference from image spectral information are
discussed. The design of the robot is opensource
and is constructed in a manner that enhances ease of replication. This
is done to facilitate construction and
development of mobile robots at research institutions where large
financial resources may not be readily available as
well as to put robots into the hands of hobbyists and help lead to the
next stage in the evolution of robotics, a home
hobby robot with potential real world applications. |
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Key Words:
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Beowulf, Robot, Vision, Biology, Low Cost,
Modular,
Off-the-shelf |
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Utilization and viability of biologically-inspired
algorithms
in a dynamic multi-agent camera surveillance system - 2003
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| T. Nathan Mundhenk, |
Nitin Dhavale, |
Salvador Marmol, |
Elizabeth Callega, |
Vidhya Navlpakkam, |
Kirstie Bellman, |
Chris Landauer, |
Michael A. Arbib |
and |
Laurent Itti |
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| Proc. SPIE Conference on
Intelligent Robots
and Computer Vision XXI |
vol 5267 |
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pg 281-292 |
Providence, RI |
October |
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Abstract:
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In view of the growing complexity
of
computational tasks and their design, we propose that certain
interactive
systems may be better designed by utilizing computational strategies
based on the study of the human brain.
Compared with current engineering paradigms, brain theory offers the
promise of improved self-organization and
adaptation to the current environment, freeing the programmer from
having to address those issues in a procedural
manner when designing and implementing large-scale complex systems. To
advance this hypothesis, we discus a
multi-agent surveillance system where 12 agent CPUs each with its own
camera, compete and cooperate to monitor
a large room. To cope with the overload of image data streaming from 12
cameras, we take inspiration from the
primate’s visual system, which allows the animal to operate a
real-time
selection of the few most conspicuous
locations in visual input. This is accomplished by having each camera
agent utilize the bottom-up, saliency-based
visual attention algorithm of Itti and Koch (Vision Research
2000;40(10-12):1489-1506) to scan the scene for
objects of interest. Real time operation is achieved using a
distributed version that runs on a 16-CPU Beowulf
cluster composed of the agent computers. The algorithm guides cameras
to track and monitor salient objects based
on maps of color, orientation, intensity, and motion. To spread camera
view points or create cooperation in
monitoring highly salient targets, camera agents bias each other by
increasing or decreasing the weight of different
feature vectors in other cameras, using mechanisms similar to
excitation and suppression that have been documented
in electrophysiology, psychophysics and imaging studies of low-level
visual processing. In addition, if cameras need
to compete for computing resources, allocation of computational time is
weighed based upon the history of each
camera. A camera agent that has a history of seeing more salient
targets is more likely to obtain computational
resources. The system demonstrates the viability of biologically
inspired systems in a real time tracking. In future
work we plan on implementing additional biological mechanisms for
cooperative management of both the sensor
and processing resources in this system that include top down biasing
for target specificity as well as Novemberelty and the
activity of the tracked object in relation to sensitive features of the
environment. |
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A new computational algorithm for the modeling of early
visual contour integration in humans - 2003
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| T. Nathan Mundhenk |
and |
Laurent Itti |
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| Neurocomputing |
vol 52-54 |
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pg 599-604 |
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June |
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Abstract:
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In order to gain a better
understanding of
visual saliency, we have developed
and algorithm which simulates the phenomenon of contour integration for
the
purpose of visual saliency. The model developed consists of the
classical
butterfly pattern of connection between orientation selective neurons
in the
primary visual cortex. In addition, we also add a local group
suppression gain
control to eliminate extraneous noise and a fast plasticity term which
helps to
account for closure effect often observed in humans exposed to closed
contour
maps. Results from real world images suggest that our algorithm is
effective at
picking out reasonable contours from a scene. The results improved with
the
introduction of both the fast plasticity and group suppression. An
addition of
multi scale analysis has also increased the effectiveness as well. |
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Key Words:
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contour, integration, visual, saliency, model |
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Towards a simpler model of contour integration in early
visual processing using a composite of methods - 2002
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| T. Nathan Mundhenk |
and |
Laurent Itti |
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| Proc. 9th Joint Symposium on
Neural
Computation (JSNC'02) |
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Pasadena, California |
May |
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Abstract:
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iLab has been attempting to
simulate contour
integration in early visual
preprocessing. Our model starts with a standard butterfly pattern of
neural connections
that excite or suppress neighboring neurons depending on their
preferred visual
orientation used for instance by Li (1998). This creates systems where
neurons tend to
excite other neurons with a collinear orientation, but tend to suppress
neurons with a
parallel orientation.
Our current model attempts to distance itself from many current models
that use
either neuro synchronization or cascade effect to obtain good contour
detection. Instead,
we have concentrated on a simpler composite model that uses group
suppression gain
control, multi scale image analysis and fast plasticity. In this, group
suppression works
by summing the excitation for small groups of neurons. If the group
exceeds threshold,
proportionately suppression among the group’s neurons is
increased.
Fast plasticity
works by increasing the excitatory ability of a neuron if it has been
excited by
neighboring neurons to a large enough extent. Finally, multi scale
processing works by
taking the result of processing the same image in multiple scales on
the same neural
kernel model at each scale.
Experiments on real world images shows that contours are most
noticeably
improved by the use of group suppression gain control, while tests on
computer generated
contours provided by Jachen Braun that are of varying size, phase and
alignment shows
improvement most from the use of fast plasticity and multi scale
processing. Our results
so far suggest that all three additions a both viable and helpful.
Further, our model
suggests that simpler mechanisms can be used by the brain in the act of
early visual
contour integration. |
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Towards Visually-Guided Neuromorphic Robots: Beobots -
2002
|
| Jen Ng, |
Reid Hirata, |
T. Nathan Mundhenk, |
Eric Pichon, |
April Tsui, |
Tong Ventrice, |
Philip Williams |
and |
Laurent Itti |
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| Proc. 9th Joint Symposium on
Neural
Computation (JSNC'02) |
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Pasadena, California |
May |
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Abstract:
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Despite the advancements made in
the field of
AI and Robotics, robots today
remain vastly inferior to animals in terms of mental agility. The main
reason for this is
that robots do not possess the neural capabilities of an animal brain.
Neural algorithms
adapt well to diverse environments, whereas robot AI is usually limited
to a test lab
setting. To resolve this disparity, an intuitive solution would be to
try to emulate the
neural functions present in animal brains. However, neural algorithms
require vast
amounts of computational power to process, in particular those
algorithms that require
real-time vision. Many robots, which run on power-saving embedded
processors, do not
have a lot of CPU cycles to spare.
We are developing a high-performance visually-guided robotics platform
with
enough processing speed to run neural algorithms. This ?Beobot?
platform consists of a
high-performance radio-controlled truck chassis (the ?robot?) carrying
an x86-based
supercomputer (the ?Beowulf? cluster). The computing cluster consists
of two compact
dual-CPU motherboards linked together by a gigabit Ethernet connection.
Powering the
computer are four Pentium-III (Coppermine) 1Ghz processors along with
768MB of
memory per motherboard. Two Firewire cameras provide the Beobot?s
vision. A compact
flash card is used as a makeshift hard drive, and it has enough space
to store a thin
UNIX-variant kernel and iLab?s vision software.
The vision software itself consists of several general-purpose neural
algorithms.
Most prominent of these is iLab?s Saliency-based visual attention
system, which enables
the Beobot to drive its attention towards the most salient locations
and objects in a visual
scene. In addition, we have developed prototype algorithms that allow
the Beobot to
parse scene layouts and perform object recognition. A primitive
action/memory AI
system allows it to implement simple visually-guided behavior. Finally,
the componentoriented
nature of the vision software enables future additions of neural
modules.
The potential advantage of the Beobot comes from its use of x86-based
hardware
and UNIX-based C++ development environment. Nearly all the parts of the
Beobot are
inexpensive, off-the-shelf components. This enables easy replacement of
broken parts.
Furthermore, the expandability of PC hardware enables devices to be
plugged into the
Beobot for additional functionalities. All these traits combined make
the Beobot
potentially easy to replicate, and this allows for wider adoption upon
the successful
completion of the prototype. |
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A New Robotics Platform for Neuromorphic Vision:
Beobots -
2002
|
| Daesu Chung, |
Reid Hirata, |
T. Nathan Mundhenk, |
Jen Ng, |
Rob J. Peters, |
Eric Pichon, |
April Tsui, |
Tong Ventrice, |
Dirk Walther, |
Philip Williams |
and |
Laurent Itti |
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| Proc: 2nd International Workshop
on
Biologically Motivated Computer Vision (BMCV 02) |
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pg 558-567 |
Tubingen, Germany |
November |
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A Model of Contour Integration in Early Visual Cortex -
2002
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| T. Nathan Mundhenk |
and |
Laurent Itti |
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| Proc: 2nd International Workshop
on
Biologically Motivated Computer Vision (BMCV 02) |
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pg 80-90 |
Tubingen, Germany |
November |
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Abstract:
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We have created an algorithm to
integrate
contour elements and find
the salience value of them. The algorithm consists of basic long-range
orientation specific neural connections as well as a Novemberel group
suppression
gain control and a fast plasticity term to explain interaction beyond a
neurons
normal size range. Integration is executed as a series of convolutions
on 12
orientation filtered images Augustmented by the nonlinear fast
plasticity and group
suppression terms. Testing done on a large number of artificially
generated
Gabor element contour images shows that the algorithm is effective at
finding
contour elements within parameters similar to that of human subjects.
Testing
of real world images yields reasonable results and shows that the
algorithm has
strong potential for use as an addition to our already existent vision
saliency
algorithm. |
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CINNIC, a new computational algorithm for the modeling
of
early visual contour integration in humans - 2002
|
| T. Nathan Mundhenk |
and |
Laurent Itti |
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| Proc. 11th Annual Computational
Neuroscience
Meeting (CNS 02) |
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Chicago, Il |
July |
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Abstract:
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In order to gain a better
understanding of
visual saliency, we have developed
and algorithm which simulates the phenomenon of contour integration for
the
purpose of visual saliency. The model developed consists of the
classical
butterfly pattern of connection between orientation selective neurons
in the
primary visual cortex. In addition, we also add a local group
suppression gain
control to eliminate extraneous noise and a fast plasticity term which
helps to
account for closure effect often observed in humans exposed to closed
contour
maps. Results from real world images suggest that our algorithm is
effective at
picking out reasonable contours from a scene. The results improved with
the
introduction of both the fast plasticity and group suppression. An
addition of
multi scale analysis has also increased the effectiveness as well. |
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Key Words:
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contour, integration, visual, saliency, model |
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Techniques for Fisheye Lens Calibration Using a Minimal
Number of Measurements - 2000
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| T. Nathan Mundhenk, |
Michael J. Rivett, |
Xiaoqun Liao |
and |
Ernest L. Hall |
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| Proc. SPIE Conference on
Intelligent Robots
and Computer Vision XIX |
vol 4197 |
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pg 81-88 |
Boston, Ma |
November |
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Abstract:
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A method is discussed describing
how different
types of Omni-Directional “fisheye” lenses can be
calibrated for use in robotic vision. The technique discussed will
allow for full calibration and correction of
x,y pixel coordinates while only taking two uncalibrated and one
calibrated measurement. These are done
by finding the observed x,y coordinates of a calibration target. Any
Fisheye lens that has a roughly
spherical shape can have its distortion corrected with this technique.
Two measurements are taken to
discover the edges and centroid of the lens. These can be done
automatically by the computer and does not
require any knowledge about the lens or the location of the calibration
target. A third measurement is then
taken to discover the degree of spherical distortion, This is done by
comparing the expected measurement
to the measurement obtained and then plotting a curve that describes
the degree of distortion. Once the
degree of distortion is known and a simple curve has been fitted to the
distortion shape, the equation of that
distortion and the simple dimensions of the lens are plugged into an
equation that remains the same for all
types of lenses. The technique has the advantage of needing only one
calibrated measurement to discover
the type of lens being used. |
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Key Words:
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omni, vision, fisheye, circular, regression,
correction, distortion, nikon |
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Simple Obstacle Detection to Prevent Miscalculation of
Line
Location and Orientation in Line Following Using Statistically
Calculated Values - 2000
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| T. Nathan Mundhenk, |
Michael J. Rivett |
and |
Ernest L. Hall |
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| Proc. SPIE Conference on
Intelligent Robots
and Computer Vision XIX |
vol 4197 |
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pg 181-190 |
Boston, Ma |
November |
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Abstract:
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Visual line following in mobile
robotics can be
made more complex when objects are placed on or around
the line being followed. An algorithm is presented that suggests a
manner in which a good line track can be
dis criminated from a bad line track using the expected size of the
line. The mobile robot in this case can
determine the size of the width of the line. It calculates a mean size
for the line as it moves and maintains a
set size of samples, which enable it to adapt to changing conditions.
If a measurement is taken that falls
outside of what is to be expected by the robot, then it treats the
measurement as undependable and as such
can take measures to deal with what it believes to be erroneous data.
Techniques for dealing with erroneous
data include attempting to look around the obstacle or making an
educated guess as to where the line
should be. The system discussed has the advantage of not needing to add
any extra equipment to discover if
an obstacle is corrupting its measurements. Instead, the robot is able
to determine if data is good or bad
based upon what it expects to find. |
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Key Words:
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line, following, obstacle, detection, robot,
mobile |
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Intelligent Robot Trends and Predictions for the New
Millennium - 1999
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| Ernest L. Hall |
and |
T. Nathan Mundhenk |
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| Proc. SPIE Conference on
Intelligent Robots
and Computer Vision XVIII |
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pg 14-25 |
Boston, Ma |
September |
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Abstract:
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An intelligent robot is a
remarkably useful
combination of a manipulator, sensors and controls. The current use of
these
machines in outer space, medicine, hazardous materials, defense
applications and industry is being pursued with vigor but
little funding. In factory automation such robotics machines can
improve productivity, increase product quality and improve
competitiveness. The computer and the robot have both been developed
during recent times. The intelligent robot combines
both technologies and requires a thorough understanding and knowledge
of mechatronics. In honor of the new millennium,
this paper will present a discussion of futuristic trends and
predictions. However, in keeping with technical tradition, a new
technique for “Follow the Leader” will also be
presented in the hope of
it becoming a new, useful and non-obvious technique.
Today’s robotic machines are faster, cheaper, more
repeatable, more
reliable and safer. The knowledge base of inverse
kinematic and dynamic solutions and intelligent controls is increasing.
More attention is being given by industry to robots,
vision and motion controls. New areas of usage are emerging for service
robots, remote manipulators and automated guided
vehicles. Economically, the robotics industry now has more than a
billion-dollar market in the U.S. and is growing.
Feasibility studies show decreasing costs for robots and unaudited
healthy rates of return for a variety of robotic applications.
However, the road from inspiration to successful application can be
long and difficult, often taking decades to achieve a new
product. A greater emphasis on mechatronics is needed in our
universities. Certainly, more cooperation between
government, industry and universities is needed to speed the
development of intelligent robots that will benefit industry and
society. |
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Range Detection for AGV Using a Rotating Sonar Sensor -
1998
|
| Dhyana Chandra Ramamurthy, |
Wen-chuan Chiang, |
T. Nathan Mundhenk |
and |
Ernest L. Hall |
|
| Proc. SPIE Conference on
Intelligent Robots
and Computer Vision XVII |
vol 3522 |
|
pg 435-443 |
Boston, Ma |
November |
|
Abstract:
|
A single rotating sonar element is
used with a
restricted angle of sweep to obtain readings to develop a
range map for the unobstructed path of an autonomous guided vehicle
(AGV). A Polaroid ultrasound
transducer element is mounted on a micromotor with an encoder feedback.
The motion of this motor is
controlled using a Galil DMC 1000 motion control board. The encoder is
interfaced with the DMC 1000
board using an intermediate IMC 1100 break-out board. By adjusting the
parameters of the Polaroid
element, it is possible to obtain range readings at known angles with
respect to the center of the robot. The
readings are mapped to obtain a range map of the unobstructed path in
front of the robot. The idea can be
extended to a 360 degree mapping by changing the assembly level
programming on the Galil Motion
control board. Such a system would be compact and reliable over a range
of environments and AGV
applications. |
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Key Words:
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sonar sensing, motion control, obstacle
avoidance,
mobile robots |
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Path Planning for Mobile Robot Navigation Using Sonar
Map and
Neural Network - 1998
|
| Wen-chuan Chiang, |
Dhyana Chandra Ramamurthy, |
T. Nathan Mundhenk |
and |
Ernest L. Hall |
|
| Proc. SPIE Conference on
Intelligent Robots
and Computer Vision XVII |
vol 3522 |
|
pg 256-264 |
Boston, Ma |
November |
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