Image analysis – Image transformation or preprocessing – Combining image portions
Reexamination Certificate
1999-09-13
2002-10-29
Couso, Yon J. (Department: 2723)
Image analysis
Image transformation or preprocessing
Combining image portions
C345S630000
Reexamination Certificate
active
06473536
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to an image synthesis method, an image synthesizer, and a recording medium on which an image synthesis program has been recorded.
2. Description of the Prior Art
A technique for calculating an optical flow from two images, and registering the two images on the basis of the obtained optical flow has been known. Description is made of a conventional method of calculating an optical flow.
(1) Lucas-Kanade Method
A large number of methods of calculating the apparent optical flow of a moving object in a moving image have been conventionally proposed. The Lucas-Kanade method which is a local gradient method out of the methods is one of the best methods. The reason for this is that the speed of processing is high, implementing is easy, and the result has confidence.
As for the details of the Lucas-Kanade method, see an article: B. Lucas and T. Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision,” In Seventh International Joint Conference on Artificial Intelligence (IJCAI-81), pp.674-979, 1981.
The outline of the Lucas-Kanade method will be described.
When a gray scale pattern I=(x, y, t) of image coordinates p=(x, y) at time t is moved to coordinates (x+&dgr;x, y+&dgr;y) with its gradation distribution kept constant after a very short time period (&dgr;t), the following optical flow constraint equation (1) holds:
∂
I
∂
x
δ
x
δ
t
+
∂
I
∂
y
δy
δ
t
+
∂
I
∂
t
=
0
(
1
)
In order to calculate an optical flow {v=(&dgr;x/&dgr;t, &dgr;y/&dgr;t)=(u, v)} in a two-dimensional image, another constraint equation is required because the number of unknown parameters is two. Lucas and Kanade have assumed that the optical flow is constant in a local region of an object.
Suppose the optical flow is constant within a local region &ohgr; on an image, for example. In this case, a squared error E of a gray scale pattern which is to be minimized can be defined by the following equation (2) when the following substitutions are made:
I
0
(
P
)
=
I
(
x
,
y
,
t
)
,
I
1
(
p
+
v
)
=
I
(
x
+
u
,
y
+
v
,
t
+
δ
t
)
E
=
∑
ω
[
I
1
(
p
+
v
)
-
I
0
(
p
)
]
2
(
2
)
When v is very small, the terms of second and higher degrees in Taylor's expansion can be ignored, so that a relationship expressed by the following equation (3) holds:
I
1
(
p+v
)=
I
1
(
p
)+
g
(
p
)
v
(3)
where g(p) is a linear differential of I
1
(p).
The error E is minimized when the derivative of E with respect to v is zero, so that a relationship expressed by the following equation (4) holds:
0
=
∂
∂
v
E
≈
∂
∂
v
∑
ω
[
I
1
(
p
)
+
g
(
p
)
v
-
I
0
(
p
)
]
2
=
∑
ω
2
g
(
p
)
[
I
1
(
p
)
+
g
(
p
)
v
-
I
0
(
p
)
]
(
4
)
Therefore, the optical flow v is found by the following equation (5):
v
≈
∑
ω
g
(
p
)
[
I
0
(
p
)
-
I
1
(
p
)
]
∑
ω
g
(
p
)
2
(
5
)
Furthermore, the optical flow can be found with high precision by Newton-Raphson iteration, as expressed by the following equation (6):
v
k
+
1
=
v
k
+
∑
g
k
[
I
0
-
I
1
k
]
∑
(
g
k
)
2
I
1
k
=
I
1
(
p
+
v
k
)
,
g
k
=
g
(
p
+
v
k
)
,
I
0
=
I
0
(
p
)
(
6
)
(2) Hierarchical Estimation Method
The largest problem of the gradient methods, including the Lucas-Kanade method, is that they cannot be applied to a large motion because a good initial value is required. Therefore, a method of producing images respectively having resolutions which differ at several levels like a pyramid hierarchical structure to solve the problem has been conventionally proposed.
Images having resolutions which differ at several levels are first previously produced from each of two consecutive images. An approximate optical flow is then calculated between the images having the lowest resolution. A more precise optical flow is calculated between the images having resolutions which are higher by one level. The processing is successively repeated until the optical flow is calculated between the images having the highest resolution.
FIG. 4
,
FIG. 3
,
FIG. 2
, and
FIG. 1
respectively illustrate an original image, an image having a lower resolution than that of the original image shown in
FIG. 4
, an image having a lower resolution than that of the image having a low resolution shown in
FIG. 3
, and an image having a lower resolution than that of the image having a low resolution shown in FIG.
2
. In
FIGS. 1
to
4
, S indicates one patch.
An optical flow is gradually found from the image shown in
FIG. 1
(an image in a hierarchy
1
) the image shown in
FIG. 2
(an a hierarchy
2
the image shown in
FIG. 3
(an image in a hierarchy
3
), and the image shown in
FIG. 4
(an image in a hierarchy
4
) in this order. In
FIGS. 1
to
4
, an arrow indicates an optical flow vector found for each patch.
However, the problem is that in a real image, there are few regions containing sufficient texture, so that a reliable optical flow is not obtained.
A technique for affixing a plurality of images to one another and synthesizing the images to obtain a seamless image having a wide field of view and having a high resolution (image mosaicking) has been conventionally actively studied. Classical applications include synthesis of aerial photographs or satellite photographs. In recent years, a method of synthesizing a plurality of digital images to obtain a seamless panoramic image, and constructing a virtual reality environment has been paid attention to.
The following two methods have been known as a technique for obtaining a panoramic image by synthesis.
The first method is a method of first translating a camera to previously pick up a plurality of images. The plurality of images obtained are simultaneously displayed on a monitor by a personal computer. A user designates corresponding points between the two images, to synthesize the two images.
In the first method, the motion of the camera is restricted to translation. In the first method, the user must designate the corresponding points.
The second method is a method of fixing a camera to a tripod and restricting the motion of the camera to only rotation on a horizontal surface, to pick up a plurality of images. The plurality of images obtained are projected on a cylindrical surface, to synthesize the images (see U.S. Pat. No. 5,396,583).
In the second method, the, motion of the camera must be restricted to only rotation on a horizontal surface. Further, the focal length or the angle of view of the camera must be measured.
SUMMARY OF THE INVENTION
An object of the present invention is to provide an image synthesis method in which highly precise positioning is performed even when a scene having a large depth is obtained by synthesis, an image synthesizer,and a recording medium on which an image synthesis program has been recorded.
Another object of the present invention is to provide an image synthesis method in which a seamless panoramic image can be obtained from a plurality of images, a camera for picking up the plurality of images is allowed to freely move, and the focal length need not be measured, an image synthesizer, and a recording medium on which an image synthesis program has been recorded.
A first image synthesis method according to the present invention is an image synthesis method for synthesizing a first image and a second image without a seam on the basis of coordinate values, on the first image, respectively cor
Chiba Naoki
Kano Hiroshi
Arent Fox Kintner & Plotkin & Kahn, PLLC
Couso Yon J.
Sanyo Electric Co,. Ltd.
LandOfFree
Image synthesis method, image synthesizer, and recording... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Image synthesis method, image synthesizer, and recording..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Image synthesis method, image synthesizer, and recording... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2991783