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This paper presents a new approach to the transfer of technology in Image Understanding (RI). This
approach is based upon the use of a shared software development environment for JU, called the
KBVision System, which can serve to ease the transfer of computer vision algorithms. In particular,
we discuss a DARPA-sponsored SBIR project in which the KBVision' System has been used to
integrate and distribute algorithms from two very different research environments.
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This paper describes a new method for building object models for the
purpose of overlapped object recognition. The method relies on local
fragments of the boundary to derive a set of autoregressive
parameters that serve to detect similar boundary fragments. First a
rule based algorithm which detects the occlusion of two or more
objects is introduced. This algorithm makes use of aheuristic rule
which take into account the number of intersection points of the
boundary with a standard invariant shape and of global features
(area, perimeter) to confirm the presence of occlusion.
The object is then decomposed into visible parts by using first a
polygonal approximation method and then the concave vertices
obtained at the latter step. The decomposition algorithm prepares
the input data for the description of the model and the object through the autoregressive filter method.
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Recursion and feedback are two important processes in image processing. Image algebra, a unified algebraic structure
developed for use in image processing and image analysis, provides a common mathematical environment for expressing
image processing transforms. It is only recently that image algebra has been extended to include recursive operations [1].
Recently image algebra was shown to incorporate neural nets [2], including a new type of neural net, the morphological
neural net [3]. This paper presents the relationship of the recursive image algebra to the field of fractions of the ring of
matrices, and gives the two dimensional moving average filter as an example. Also, the popular multilayer perceptron
with back propagation and a morphology neural network with learning rule are presented in image algebra notation. These
examples show that image algebra can express these important feedback concepts in a succinct way.
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This paper describes a new method for building object models for the
purpose of overlapped object recognition. The method relies on local
fragments of the boundary to derive a set of autoregressive
parameters that serve to detect similar boundary fragments. First a
rule based algorithm which detects the occlusion of two or more
objects is introduced. This algorithm makes use of aheuristic rule
which take into account the number of intersection points of the
boundary with a standard invariant shape and of global features
(area, perimeter) to confirm the presence of occlusion.
The object is then decomposed into visible parts by using first a
polygonal approximation method and then the concave vertices
obtained at the latter step. The decomposition algorithm prepares
the input data for the description of the model and the object
through the autoregressive filter method.
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The analytical and processing developments in the field of Image
Understanding over the last 15 years have led to the creation of a set
of processing tools for the detection, characterization (feature
extraction), and classification of 2 dimensional signals. This set of
tools is applicable to 2 dimensional signals other than the traditional
"image" type signals. In particular, for passive sonar detection
processing several 2 dimensional signal transforms are generated
from the 1 dimensional sensor time series data. These transforms
are selected in order to concentrate signal energy locally within the 2
dimensional transform. A classic example is the Lofargram which is
a grequency versus time transform of the time series data. If the
acoutic source is emitting tones (for example from machinery) then
the Lofargram will contain line like structures.
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As part of on-going studies of automated techniques for object recognition in imagery, recent experiments in two and three
dimensions have produced promising results. Newly developed methods that exploit projectively invariant relationships in
imagery are able to recognize the same object in images that differ in tilt, scale and rotation. Automatically extracted corner
points are used as the base features in simple two-dimensional objects, and patches of known gray-value are used in threedimensional
terrain perspective views. In both cases, projective invariants are calculated and compared with a catalog of
archetypal values, resulting in successful identification of the objects within experimental error.
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The Geometric Hashing technique developed by the NYU Courant Institute has been applied to various automatic
target recognition applications. In particular, I-MATH has extended the hashing algorithm to perform automatic target
recognition ofsynthetic aperture radar (SAR) imagery. For this application, the hashing is performed upon the geometric
locations of dominant scatterers.
In addition to being a robust model-based matching algorithm -- invariant under translation, scale, and 3D rotations
of the target -- hashing is of particular utility because it can still perform effective matching when the target is partially
obscured. Moreover, hashing is very amenable to a SIMD parallel processing architecture, and thus potentially realtime
implementable.
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We have developed a structured light system for noncontact 3-D measurement of human body surface
areas and volumes. We illustrate the image processing steps and algorithms used to recover range data
from a single camera image, reconstruct a complete surface from one or more sets of range data, and
measure areas and volumes.
The development of a working system required the solution to a number of practical problems in
image processing and grid labeling (the stereo correspondence problem for structured light). In many
instances we found that the standard cookbook techniques for image processing failed. This was due in
part to the domain (human body), the restrictive assumptions of the models underlying the cookbook
techniques, and the inability to consistently predict the outcome of the image processing operations.
In this paper, we will discuss some of our successes and failures in two key steps in acquiring range
data using structured light: First, the problem of detecting intersections in the structured light grid, and
secondly, the problem of establishing correspondence between projected and detected intersections. We
will outline the problems and solutions we have arrived at after several years of trial and error. We can
now measure range data with an r.m.s. relative error of 0.3% and measure areas on the human body
surface within 3% and volumes within 10%.
We have found that the solution to building a working vision system requires the right combination
of theory and experimental verification.
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The success of machine vision systems in solving real-world problems will depend on
how well they can balance the conflicting requirements of high accuracy, flexibility to
operate under a wide range of environmental conditions, fast response time, and size
constraints. Most machine vision systems developed in the past have sacrificed one or
more of these factors in favor of the others. In this paper, we discuss our experience in
developing high speed machine vision and world modeling systems for mobile robotics
applications. A pipeline binocular stereo range detection system developed in our
laboratory matches 256 x 256 pixel stereo image pairs in one second and generates 2-
D and 3-D obstacle maps in near real-time. These obstacle maps then get integrated
into pixel- and voxel-based dynamic world models. Using data provided by stereo
cameras mounted on top of an indoor mobile robot, these systems have the capability
to create very realistic models of the environment. An autonomous navigation system
uses these environment models to successfully navigate a mobile robot in an indoor
environment cluttered with dynamic and static obstacles.
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Conventional fieldable signal processing systems utilize custom hardware manufactured
and configured specifically for a single signal processing application. Developing new
systems or reconfiguring existing systems involves great expense and time expenditure.
We at Alliant Techsystems have developed a signal processing system based on
commercially available hardware which is completely software programmable and yet small
and fast enough to be used in fieldable multisensor signal processing applications. This
paper will discuss Alliant's reconfigurable signal processing system.
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Litton Data Systems has developed a hybrid ATR system using
DataCube signal processing and imaging boards controlled by a host
Sun workstation. The input image preprocessor includes detection
algorithms that locate candidate targets in a simulated or real JR
scene and segmentation algorithms that generate target outlines
which are normalized for recognition processing by Litton's LIGHTMOD
optical correlator.
Target detection performs a raster scan over the image using nested
boxes to find detection pixels based on intesity/variance contrasts.
Segmentation operates on windows centered at the centroids of
connected components of detection pixels. Optimal thresholds are
determined from gray scale histograms. Multimodal optimization
seeks to maximize edge/boundary proximities for candidate target
and island components obtained by thresholding with respect to the
optimal thresholds. Relaxation smooths the boundaries of optimal
target components or merged target/island components.
Normalization algorithms scale and center the component boundaries
in a 128x128 LIGHT-MOD window. The LIGHT_MOD correlates the FT
of the normalized outline with the FTs of a reference library of target
outlines. Template matching is the basic technique used for
recognition.
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Applying distributed memory parallel systems to image processing provides the scaleability that is
required to handle large real time applications. These systems can only be used efficiently with an
architecture which is able to balance processing and memory with internal and external communication
bandwidth.
By efficiently harnessing the power of nodal heterogeneity and communication reconfigurability,
systems with an ideal balance can be assembled and applied to achieve very high levels of efficiency.
This paper outlines such a Heterogeneous Distributed Memory Parallel Processor (HDMPP) and
discusses the software environment that is required to take advantage of it.
Meiko's Application Center has considerable experience in applying these HDMPP systems to practical
applications including: high definition television, radar, sonar, medical imaging, remote sensing,
machine vision, data compression, image enhancement, and pattern recognition.
These examples are used to illustrate how HDMPP systems have been successfully applied and how
applications have been prototyped, developed, optimized and deployed on a single commercial off the
shelf product.
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Porting Algorithms to Specialized and Parallel Architectures
To extract meaningful information from available data, researchers are often
confronted with data in some complex superposed state. Therefore, the
physical quantity of interest is not directly observable. In the physical
sciences, a common form of information mixing is linear superposition. This
includes fields as diverse as radio astronomy, Fourier transform spectroscopy,
atmospheric physics, and medical diagnostics. One problem confronting
researchers in these disciplines is restoring or deconvolving data.
In addition to simple data restoration, noise can complicate the restoration
process. This phenomenon can enter both prior to or during the collection of
data. Noise presents a major obstacle to perfect restoration, information gain,
and scientific understanding.
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Parallel processing has been widely accepted as the approach to providing the necessary computational
power to solve computer vision systems problems. Although several projects are underway to develop new
architectures for computer vision, tools to effectively use those systems or commercially available multiprocessors
are limited or non-existent. Unless we can develop efficient methods for mapping vision algorithms
and developing programs on these architectures, the performance gains from parallel processing will be
limited, and will be beyond the reach of a non-expert in parallel processing. This paper presents a design
for a software development environment (SDE) for implementing vision systems applications on multiprocessors.
The SDE design exploits characteristics of vision systems, and uses a classification scheme for
vision algorithms to develop a parallelization and performance evaluation tool. These tools use databases
that store knowledge of parallelization for different known computations on common architectures. The
parallelization and performance evaluation tools use this knowledge to guide a user interactively parallelize
algorithms. Some parts of SDE are currently operational and others still need to be developed.
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Presented in this paper is a methodology that has been used in development of a model-based computer
vision system. The methodology focuses on problem solving while using commercial off-the-shelf computer
vision software.
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A methodology and supporting software tools for obtaining insight
and quantitative measures on the accuracy of ATC (automatic target
cueing) algorithms are described. Algorithm accuracy is
characterized by trade-off curves in which selected performance
measures are plotted against each other over a range of algorithm
operating points. Performance trade-off curves determined as a
function of critical sensor, target and background parameters allow
algorithm sensitivity issues to be investigated and provide a basis for
evaluating algorithm accuracy effects in the context of specific
missions.
The evaluation process is founded on an ATC experiment design
which defines a challenging but representative "cut" through the
multidimensional scenario space associated with the mission under
consideration. The experiment design is included in a written test
plan which documents the mechanics of the evaluation process and
specifies the amount and type of imagery that will be made available
for algorithm development and evaluation. tolls for placing
confidence bounds on hypothetical algorithm performance trade-off
curves provide a basis for addressing the statistical data adequacy
issue in relation to the experiment design.
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The Strategic Computing Object-directed Reconnaissance Parallel-processing Image Understanding System
(SCORPIUS) program was successfully completed in September 1990. Initiated in 1985, SCORPIUS was a
research program that combined emerging technologies from DARPA's Image Understanding (IU) and Computer
Architecture programs in a real world application, the automated exploitation of aerial imagery. Image exploitation
is the process of extracting intelligence from image data. The quantity and quality of the products from various
sensor systems are out-pacing the improvements in exploitation systems. SCORPIUS has demonstrated the concept
that the model-based vision approach can address this imbalance.
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The high volume of satellite derived oceanographic data, and the relatively high level of skill associated with
the detection of important features in multi-sensor oceanographic datasets, has necessitated automating the
analysis process. Since 1983 the Naval Oceanographic and Atmospheric Research Laboratory (NOARL) at
NASA's Stennis Space Center has been involved in a effort which transitions research in automated
interpretive techniques to operational use. The NOARL image understanding system's basic philosophy is
unique in its strong emphasis on integration of different artificial intelligence techniques, conventional image
processing techniques, statistical techniques, and low level vision techniques, making use of the strengths of
each technique to optimally achieve the ultimate goal; an object based map showing icons which relate to
detected features in the oceanographic imagery. The present paper describes the approach implemented,
discusses lessons learned in past development efforts, and explains the rationale for the future evolutionary
course of the system.
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SCORPIUS is a model-based image
understanding system developed on a suite of DARPAsupplied
hardware. It detects and identifies objects in
operational imagery over a range of imaging
conditions.
As a research project, SCORPIUS advances
established technologies while exploring relatively
young fields: image processing, image understanding,
and parallel processing. During the five-year
development of various system processing
components, a substantial body of empirical knowledge
was acquired.
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A system was assembled to study several aspects of locating
ship targets from infrared imagery. The system was either placed on
shore sites or installed on an aircraft to collect data on the
scene. The primary sensor was an infrared camera which produced
images of the scene at standard RS-l70 rates. Requirements that
included real time operation dictated the use of a parallel architecture
for this task. As no suitable commercial systems were avail
able, a custom array of bit slice microprocessors was assembled for
the task. Through extensive field tests strengths and limitations of
the design have been identified. These lessons are being applied to
the development of next generation systems.
A gimbal mounted infrared camera with digitization circuitry
presents a new 256 by 256 pixel image to the parallel pipelined
array of 17 bit slice microprocessors thirty times a second. To
extend processor performance beyond the standard commercial microprocessors.
two basic bit slice designs were employed. The bit slice
machines were highly tuned for the assigned tasks and algorithms.
Unfortunately this restricted the desired flexibility to readily
examine alternate algorithms. The fundamental architecture concept
performed well quickly reducing the large array of data to manageable
set of information. Real time operator displays were driven to
monitor the progress of each test run.
Results of the system operation were stored on video and digi
tal recorders permitting more detailed analysis after each test. Non
real time data reduction provided many insights into the system
operation and to algorithm improvements. Substantial operator interaction.
and data interpretation was required greatly slowing the post
test analysis phase. Overwhelmed with data, the analysts focused on
locating a few data segments of interest. Significant work remains
in improving the interfaces between the field data and the powerful
laboratory computers. Automation of the data analysis is also needed
to efficiently evaluate the great volume of field information.
Continuing improvements in Artificial Intelligence, Expert Systems,
Neural Networks, and other areas may help here.
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In order to design vision systems which work, a sound engineering methodology
must be utilized. In the systems engineering approach, a complex system
is divided into simple subsystems and from the input/output characteristics
of each subsystem, the input/output characteristics of the total system can
be determined. Machine vision systems are complex, and they are composed
of different algorithms applied in sequence. Determination of the performance of a total machine vision system is possible if the performance of each of
the subpieces, i.e. the algorithms, is given. The problem, however, is that
for most algorithms, there is no performance characterization which has been
established and published in the research literature.
Performance characterization has to do with establishing the correspondence
of the random variations and imperfections which the algorithm produces
on the output data caused by the random variations and imperfections
of the input data. This paper illustrates how random perturbation models and
propagation of random errors can be set up for a vision algorithm involving
edge detection, edge linking, arc segmentation, and line fitting. The paper
also discusses important dimensions that must be included in the performance
characterization of any vision module performing a parametric estimation such
as object pose, curve fit, or edge orientation estimation. Finally, we outline
a general parametric model having three components: a relational model; a
noise model; and a computational estimation model.
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