Image analysis – Pattern recognition – Template matching
Reexamination Certificate
2001-01-16
2002-03-12
Boudreau, Leo (Department: 2621)
Image analysis
Pattern recognition
Template matching
C382S118000, C382S190000, C382S195000, C382S199000, C382S278000, C382S282000, C382S285000, C382S305000, C707S793000
Reexamination Certificate
active
06356659
ABSTRACT:
FIELD OF THE INVENTION
The present Invention relates to technology for recognizing and analyzing objects that are shown in camera or video images. In particular, the present invention relates to technology which compensates for variability of images for different objects of the same type, or for images of the same object (due to differences in position, scale, orientation, pose, illumination, deformation, noise etc.).
BACKGROUND OF THE INVENTION
Techniques exist for analyzing captured images in order to perform image matching or other functions, such as the extraction of a fixed, sparse image representation from each image in an image gallery and for each new image to be recognized. Conventional techniques, however, require a great deal of computational power and time. When comparing a new image with all images stored in a gallery of thousands of images, each comparison requires a new, computationally-expensive matching process. There is a need for an image analysis technique that is more easily implemented.
SUMMARY OF THE INVENTION
It is therefore an objective of the present invention to provide a technique for image analysis that is more easily implemented than are conventional techniques, and which requires less computational power and time.
The present invention provides a process for image analysis. The process includes selecting a number M of images, forming a model graph from each of the number of images, such that each model has a number N of nodes, assembling the model graphs into a gallery, and mapping the gallery of model graphs into an associated bunch graph by using average distance vectors &Dgr;
ij
for the model graphs as edge vectors in the associated bunch graph, such that
Δ
ij
=
1
M
∑
m
Δ
ij
m
.
where &Dgr;
ij
is a distance vector between nodes i and j in model graph m. A number M of jets is associated with each node of the associated bunch graph, and at least one jet is labeled with an attribute characteristic of one of the number of images. An elastic graph matching procedure is performed, wherein the graph similarity function is replaced by a bunch-similarity function S(G, G
I
) such that
S
(
G
,
G
I
)
=
1
N
∑
n
max
m
S
(
J
n
m
,
J
n
l
)
.
The model bunch graph may be manually prepared by associating a standard grid of points over an image, correcting node positions to fall over designated sites characteristic of the image, and extracting jets at the nodes. Object recognition may be performed by selecting a target image, extracting an image graph from the target image, comparing the target image graph to the gallery of model graphs to obtain a graph similarity S(G
M
, G
I
) such that
S
(
G
M
,
G
I
)
=
1
N
∑
n
S
(
J
n
M
,
J
n
I
)
-
λ
E
∑
e
(
Δ
x
_
e
M
-
Δ
x
_
e
I
)
2
;
and
identifying a model graph having a greatest graph similarity with the image graph.
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Malsburg Christoph von der
Wiskott Laurenz
Eyematic Interfaces, Inc.
Gray Cary Ware & Freidenrich
Mariam Daniel G.
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