Ex Parte He

Patent Trial and Appeal Board

May 20, 2014

11100870 (P.T.A.B. May. 20, 2014)

UNITED STATES PATENT AND TRADEMARK OFFICE
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BEFORE THE PATENT TRIAL AND APPEAL BOARD
__________

Ex parte ZHEN HE
__________

Appeal 2012-001108
Application 11/100,870
Technology Center 2600
__________

Before DEMETRA J. MILLS, JEFFREY N. FREDMAN, and
SUSAN L.C. MITCHELL, Administrative Patent Judges.

FREDMAN, Administrative Patent Judge.

DECISION ON APPEAL
This is an appeal1 under 35 U.S.C. § 134 involving claims to media, a
system, and a method of edge detection for an image processor in an
imaging system. The Examiner rejected the claims as anticipated and as
obvious. We have jurisdiction under 35 U.S.C. § 6(b). We affirm.

1 Appellant identifies the Real Party in Interest as Xerox
Corporation (see App. Br. 2).
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Application 11/100,870

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Statement of the Case
Background
“One method to ensure proper post-processing of the mixed-content
binary image data is edge detection. Conventional edge detection algorithms
often operate on local gradient calculations. Different gradient operators and
complicated logic combination are applied to the data to detect edges with
different orientations. This adds computation to the process” (Spec. 2 ¶
0004).
The Specification teaches “a method of edge detection defining a local
window around a current image element. The method counts at least one set
of pixels inside the window and determines if a number of pixels within the
set of pixels is above a threshold” (Spec. 2 ¶ 0006). The Specification
teaches that if “the number of pixels is above the threshold, at least two
centroids associated with the window are located. If a distance between the
two centroids is larger than a threshold distance[,] the current image element
is defined as an edge element” (Spec. 2 ¶ 0006).
The Claims
Claims 1-20 are on appeal. Claim 1 is representative and reads as
follows:
1. A method of edge detection an image processor in an
imaging system, comprising:
defining a local window around a current image
element;
counting at least one set of pixels inside the window;
determining if a number of pixels within the set of
pixels is above a threshold; and
if the number of pixels is above the threshold:
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Application 11/100,870

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defining, within the window, at least a first set
of pixels corresponding to a first color and a second
set of pixels corresponding to a second color;
locating at least two geometric centroids
associated with the window, a first geometric centroid
determined from the first set of pixels, and a second
geometric centroid determined from the second set of
pixels;
determining if a distance between the two centroids is
larger than a threshold distance; and
if the distance between the two centroids is larger than
the threshold distance, defining the current image element as
an edge element.

The issues
A. The Examiner rejected claims 1-10, 12-14, and 18-20 under 35 U.S.C.
§ 102 (b) as anticipated by Horie2 (Ans. 6-14).
B. The Examiner rejected claims 11 and 15-17 under 35 U.S.C. § 103(a)
as obvious over Horie and Rao3 (Ans. 14-18).
A. 35 U.S.C. § 102(b) over Horie
The issue with respect to this rejection is: Does the evidence of
record support the Examiner’s conclusion that Horie teaches “a first
geometric centroid determined from the first set of pixels, and a second
geometric centroid determined from the second set of pixels” as required by
claim 1?

2 Horie et al., US 6,735,341 B1, issued May 11, 2004.
3 Rao et al., US 6,721,448 B2, issued Apr. 13, 2004.
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Findings of Fact
1. The Specification teaches that:
The geometric centroids can be found in many
different ways. For example, assume a window of NxN.
One can denote the binary pixel set inside the window as B=
{b(i,j), i=l, 2 ... N; j=1, 2 ... N}. In this example, b(i,j)=l for
black and 0 for white. The black pixel set can then be
defined as Pk = {b(i,j):b(i,j)=l}. The white pixel set can then
be defined as Pw = B - Pk. The geometric centroid of the
black pixel set, denoted as Ck, can be located by (Xk,Yk) =
(mean(ik), mean(jk)), where the black pixels are elements of
the window pixel set P and ik, jk are the row and column
numbers of the black pixels.

(Spec. 6 ¶ 0027.)
2. Horie teaches methods which “discriminate whether or not a
local area within a macro region is an edge area, a gradient area, or a
monochrome area, and optimum correction is executed based on the
discrimination result” (Horie, col. 10, ll. 64-67).
3. Horie teaches “all of the image data are divided into blocks (in
this instance, each block comprises 8x8 pixels), and the characteristics of
each block are extracted” (Horie, col. 9, ll. 24-27).
4. Horie teaches determining the “dot count value (number of
pixels having a maximum or minimum pixel density relative to the four
pixels adjacent to a specific pixel density within one block)” (Horie, col. 9,
ll. 34-36).
5. Horie teaches that “a block having a dot count value that is
greater than a specific threshold value is designated a photo\line image
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block, and is recorded as [1] in the photo\line image attribute binary map”
(Horie, col. 9, ll. 58-61).
6. Figure 23 of Horie is reproduced below:

FIG. 23 is a flow chart showing the content of the local area
discrimination process (S51) of FIG. 22. In step S61, the
pixel values of the pixels included in the local area are
converted to coordinates in the color space. In step S63, the
maximum color space distance F within the local area is
calculated based on the converted coordinates. In step S65,
the local area characteristics are discriminated based on the
maximum color space distance F. In step S67, noise is
eliminated from the discrimination result

(Horie, col. 12, ll. 45-53).
7. Horie teaches that for “processing within a 3x3 pixel block, the
color space distance is calculated for all two-pixel combinations among the
nine pixels, and the maximum value F is designated the degree of change in
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pixel value (maximum color space distance F within the local area) of the
center pixel in the block” (Horie, col. 12, l. 65 to col. 13, l. 2).
8. Figure 24 of Horie is reproduced below:

“FIG. 24 plots positions P1 through P9 of 9 pixels within a 3x3 pixel block
in the two-dimensional color space R-G, B-G. The maximum color distance
F within the local area is the value of the color difference between P3 and
P7” (Horie, col. 13, ll. 23-27).
9. Horie teaches that
[T]he value of the maximum color space distance F within
the local area is compared to threshold values TH1 and TH2.
When F>TH2 and, the local area is discriminated as an edge
area (S73). If F