2010 International Workshop on Nonlinear Circuits,
Communication and Signal Processing
NCSP'10, Waikiki, Hawaii, March 3-5, 2010

Color Texture Boundary Detection Using Three-Layer CNN Based on Hybrid-Feature
Takashi Inoue† and Yoshifumi Nishio††Tokushima University
2-1 Minami-Josanjima, Tokushima, Japan
Phone:+81-88-656-7470, FAX:+81-88-656-7471
Email: : {takashi, nishio}@ee.tokushima-u.ac.jp

Abstract

for the information extraction, which is the so-called secondorder feature [4].
In this paper, a color texture boundary detection using cellular
C.T. Lin et al. have proposed the texture segregation as theneural networks (CNN) is presented. The approach is similar early vision using CNN [5]. They have confirmed the texto the early vision system of the human brain. The proposed ture boundary detection with high accuracy by using the firstalgorithm has been tested on synthetic color texture images. and second-order features. However, the first- and secondAdditionally, we carry out the error evaluation between ideal order features are created respectively, unlike the early visionboundaries and detected boundaries. From our simulations, system of the human brain. Additionally, in their research, a
we confirm that the boundaries can be detected like human’s texture input image is the gray-scale image unlike the humansensation.
visual system, and we think that the color information is oneof the basic visual features for understanding the visual field.
In this paper, a color texture boundary detection using
1. Introduction
CNN is presented. The approach is similar to the early viResearchers in recent decades have elucidated the signal sion system of the human brain. The proposed algorithm fortransduction in the retina and the function of the visual cor- the texture boundary detection consists of the proposed andtex. The highly flexible nature of the neural circuits in the the conventional CNN. The proposed CNN can treat the color
visual cortex, especially the early vision, has been an interest- information and can consider the first- and second order feaing subject for studying and developing the neural plasticity. tures at the same time like the early vision system. The proCellular Neural Networks (CNN) were introduced by Chua posed algorithm has been tested on the synthetic color texture
and Yang in 1988 [1] and are one of the neural circuits in images. Additionally, we carry out the error evaluation bethe retina. The idea of CNN was inspired from the architec- tween the ideal boundaries and the detected boundaries. Fromture of the cellular automata and the neural networks. Unlike our simulations, we can confirm that the boundaries can be
the conventional neural networks, CNN has local connectiv- detected based on the first- and second-order features like theity property. CNN can be used for various applications of the human visual system.
image processing [2].
The early vision represents the first stage of the visual processing. These mechanisms are operated in parallel across 2. Proposed algorithm for texture boundary detection
the visual field and are believed to be used for detecting the
In this section, we present the proposed algorithm for themost basic visual features, such as contrast, edge, grouping,color
texture boundary detection using CNN. Figure 1 showsbrightness and lightness. A. K. Jain and F. Farrokhnia [3]theflowchartof the proposed algorithm. Following subsecconfirmed as follows. There are two fundamental features in
tions
are
going
to introduce each block of the proposed algothe human visual system: one is that the diﬀerence of lightrithm.
intensity projects into the retina, the other is that the behaviorof the retina is similar to the band-pass filtering. These facts
implicate that the abrupt changes on the light intensity will 2.1. Conversion method using HSB modelcause the stronger stimulation in the visual cortex and thoseIn the first step of the proposed algorithm, the synthetic
abrupt changes are called first-order feature. K. Obermayer
and G. G. Blasde confirmed an orientation-selective neuron color texture image is converted to three gray-scale images
which is sensitive to the areas of lines and boundaries, called using HSB model. HSB model is similar to the perception of
simple cells. The receptive field of the simple cells can be human, hence it is suitable for a treatment of the color informodeled by the Gabor function, and it has been widely used mation. Therefore, HSB model is used for the color texture- 552 -

II

Isolate layer
VuI

Color input image

VyI

∫

∑

BI

Conversion, Histogram analysis

AIC

HF-CNN

Two-layer

I1
Vu1

First layer

C2

Full Wave Rectification (1)

Vy1

∫

∑

B1

A1
C1

Smoothing with Binary (2)Gradient (4)Heat Diffusion (3)Gradient (4)Second layer

I2
Vu2

∑

B2

Vy2

∫
A2

Logic AND (5)Logic AND (5)Figure 2: Structure of HF-CNN.

Heat Diffusion (3)Binary Threshold (6)(1) Three first-order diﬀerential equations:

Logic AND (5)Skeltonization (7)Equation of isolate layer:
dv xI,i jdtConventional CNN

Texture boundary detection result=+

Figure 1: Proposed algorithm.

∑i+r ∑ j+r−v xI,i j +AI(i, j;k,l) vyI,kl (t)k=i−r
l= j−r∑i+r ∑ j+r
BI(i, j;k,l) vuI,kl (t) + II ,k=i−r

l= j−r(1)

Equations of two-layer:
dv x1,i jdtboundary detection in the proposed algorithm. From this conversion step, we can obtain the three gray-scale images.

=+

2.2. Histogram analysis+

In the second step of the proposed algorithm, three typesof density histogram are created from each gray-scale image
in order to decide a parameter in the proposed CNN. The parameter in the proposed CNN represent the input method of
the three gray-scale images. Because the proposed CNN has
three single CNNs, the three gray-scale images in the previous step are inputted to each layer of the proposed CNN. We
decide the inputted gray-scale image from the three types ofdensity histogram, because we do not know which gray-scale
image should be inputted to each layer of the proposed CNN,
in this input step.

+
dv x2,i j
dt

+
+

(2)

∑i+r ∑ j+r−v x2,i j +A2(i, j;k,l) vy2,kl (t)k=i−r
l= j−r∑i+r ∑ j+r
B2(i, j;k,l) vu2,kl (t)k=i−r
l= j−r∑i+r ∑ j+r
C1(i, j;k,l) vy1,kl (t) + I2 ,

(3)

k=i−r

k=i−r

l= j−rl= j−r(2) Three output equations:2.3. CNN based on hybrid-feature (HF-CNN)
Figure 2 shows the structure of CNN based on the first- andsecond-order features, so-called the hybrid-feature CNN (HFCNN). HF-CNN consists of three single CNNs. In particular,
one of the three layers is called the isolate layer and the othertwo layers are called the two-layer. Hence, the isolate layer
influences one layer of the two-layer, and the two-layer influence each other. Namely, the isolate layer is not influenced
by other layers.
The dynamical system equations associated with HF-CNN
are described by the following equations:=∑i+r ∑ j+r−v x1,i j +A1(i, j;k,l) vy1,kl (t)k=i−r
l= j−r∑i+r ∑ j+r
B1(i, j;k,l) vu1,kl (t)k=i−r
l= j−r∑i+r ∑ j+r
C(i, j;k,l) vyI,kl (t)k=i−r
l= j−r∑i+r ∑ j+r
C2(i, j;k,l) vy2,kl (t) + I1 ,vyI,i j (t) = 21 (|v xI,i j (t) + 1| − |v xI,i j (t) − 1|),(4)

vy1,i j (t) = 12 (|v x1,i j (t) + 1| − |v x1,i j (t) − 1|),(5)

vy1,i j (t) = 12 (|v x2,i j (t) + 1| − |v x2,i j (t) − 1|),(6)where v x , vy , and vu represent a state, an output and an inputof the cell, respectively. In Eqs. (2) and (3), C, C1 and C2 are
the coupling templates introduced to couple each layer.
The templates of each layer in HF-CNN are shown as follows.
Template of isolate layer: 0 0 0 
 0.1 0.15 0.1 Ai =  0.15 0 0.15  , Bi =  0 0 0  , Ii =0.
(7)





- 553 -

0.1

0.15

0.1

0

0

0

Templates of two-layer:



 0

 0
cos Ωy0A1 =  cos Ωx −(3+λ)2 cos Ωx  , B1 =  0



0


 0

A2 =  cos Ωy0cos Ωy

0

0




 0


cos Ωy ,B= 2  0cos Ωx0−(3+λ)2cos Ωx

0

0

0
0
0
0
λ2
0




0 
 , I1 =0.
0

0

(8)



0 
 , I2 =0.

0

0

(9)
Coupling template : a a a 
C =  a 0 a  , a = |VyIi j − VyIkl |/8.


a a a




0
sin Ωy00sin Ωx 
C1 =  − sin Ωx
 .

0


 0

C2 =  sin Ωy0− sin Ωy− sin Ωx0
sin ΩxTemplate 1: Full Wave Rectification Template [5].
Template 2: Smoothing with Binary Template [6].
Template 3: Heat Diﬀusion Template [6].
Template 4: Gradient Template [6].
Template 5: Logic AND Template [6].
Template 6: Binary Threshold Template [6].
Template 7: Skeltonization Template [6].
Finally, we can obtain the result of the texture boundary
detection through a series of steps as Fig. 1.
3. Color texture boundary detection results

(10)

(11)

In this section, we show results of the color texture boundary detection using the proposed algorithm. All input images
have 4 textures with 256 × 256 pixels.

0




− sin Ωy 
 .
0

(12)

0

First-Order Feature Extraction: As the introduction in the
Section 1, the first-order feature represents the boundary inwhich the intensity of light suddenly changes. This fact implies that the first-order feature can be obtained by using theGaussian operation and the Gradient operation. Therefore, in
HF-CNN, the template of the isolate layer as Eq. (7) and thecoupling template as Eq. (10) have an ability of the Gaussian
operation and of the Gradient operation, respectively. By theeﬀect of this setting, the first-order feature can be created.
Second-Order Feature Extraction: As the introduction in
the Section 1, the simple cells can be modeled by the Gaborfilter operation Eqs. (8), (9), (11) and (12). In the Gabor filter
template, there are two parameters as Ω x and Ωy . We cancontrol the 4 types orientations (−45◦ , 0◦ , 45◦ and 90◦ ) of theGabor filter template by changing Ω x and Ωy . Namely, we
can obtain 4 output images corresponding to each orientationfrom HF-CNN.
Hybrid-Feature: The first-order feature is created in theisolate layer and the coupling part from the isolate layer totwo-layer. Additionally, in the two-layer, the second-orderfeature is created with considering the first-order feature obtained by the isolate layer. Therefore, the output image from
HF-CNN has the hybrid-feature combining the first- and thesecond-order features.
2.4. Creation of texture boundaries

(a)

(b)

(c)

(d)

Figure 3: Simulation results for synthetic color texture input
image using the proposed algorithm. (a) Input image. (b) Detection result for Fig. 3(a). (c) Input image. (d) Detection
result for Fig. 3(c).
Figures 3(a) and (c) show the input images which is thesynthetic color texture. Figures 3(b) and (d) show results ofthe texture boundary detection using the proposed algorithm.
In Fig. 3(b), the boundaries of each texture can be detected
well. On the other hands, in Fig. 3(d), we think that it isdiﬃcult to detect the accurate boundaries of the some textures
including the nonperiodic feature.

In the fourth step of the proposed algorithm, the output images using HF-CNN are processed by using templates in the 4. Evaluation of detection performance
conventional CNN. The templates in the proposed algorithm
In this section, we evaluate the performance of texture
are founded in [5] and [6].
boundary detection using the proposed algorithm. The per-- 554 -

formance of the texture boundary detection depends on thefeature of the texture as the periodicity. Therefore, we focus on three types of synthetic texture as “Uniform Texture”,“Nonuniform Texture” and “Combining Texture” in this evaluation. “Uniform Texture” is the image combining the periodic textures. “Nonuniform Texture” is the image combining the nonperiodic textures. “Combining Texture” is the image combining the periodic texture and nonperiodic texture.
Hence, each performance of the texture boundary detection
using the three types of synthetic texture is compared. In thisevaluation, we consider only the synthetic texture composedof two textures because it is hard to judge the accuracy of the
texture containing multi boundaries.

(a)

(b)

5. Conclusions(c)

Figure 4: Example of “Uniform Texture”. (a) Input image.
(b) Ideal output image. (c) Detection result for Fig. 4(a).
Figure 4(a) shows “Uniform Texture” which is the input
image containing the two textures. Figures 4(b) and (c) show
the ideal boundary and the result of the boundary detection
using the proposed algorithm, respectively.
The error value between the ideal boundary and the detected boundary is calculated by∑255
Di /256
Error = i=0× 100,(13)
128
where Di is the distance between the horizontal positions of
the ideal boundary Ansi and of the detected boundary Dei
calculated by
Di [pixels] = |Ansi − Dei |,(i = 0, 1 · · · , 255).

(14)

For this evaluation, we prepare 10 input images per synthetic texture. All the original texture images used in the synthetic texture are found in [7] and [8].
Table 1 shows the error evaluation of the three types ofsynthetic texture. In the case of “Uniform Texture”, all error

Table 1: Error Evaluation
Ave. [%] Min. [%]Uniform Texture
4.0
0.5
Nonuniform Texture
15.1
5.2
Combining Texture
8.3
2.4
8.2
0.5

values are the smallest value among the three types of synthetic texture. On the other hands, in the case of “Nonuniform
Texture”, all error values are the biggest value. However, we
think that the minimum error is suﬃciently-small although it
is diﬃcult to detect the boundary of two textures including thenonperiodic feature. Because the color information is considered in the proposed algorithm. Namely, if the two textures
have the diﬀerent colors, the boundary of the two textures including the nonperiodic feature can be detected well. In thecase of “Combining Texture”, the all error values are middle between the case of “Uniform Texture” and of “Nonuniform Texture”. In other words, if one of the two textures has
the periodic feature, the performance of the texture boundary
detection is better than the case of “Nonuniform Texture”.
From the comparison of the three types of synthetic texture,
we think that these diﬀerences of the performance may occur
in the case of the human visual system. Therefore, we feelthat the detected boundaries using the proposed algorithm are
similar to the human’s sensation.

In this study, we have presented a color texture boundary
detection using cellular neural networks. The proposed CNN
is inspired from the early vision system of the human brain
and can create two features at the same time like the early vision system. From our simulations, we have confirmed thatboundaries can be detected by using the two features like thehuman’s sensation. However, in the early vision system, there
are higher order features that can be utilized. Therefore, we
consider that the proposed algorithm can be extended by considering the higher order features.
References[1] L.O. Chua and L. Yang, “Cellular Neural Networks:Theory,”
IEEE Trans. Circuits Syst., vol. 32, pp. 1257-1272, Oct. 1988.
[2] F. Dirk and T. Ronald, “Coding of Binary Image Data using
Cellular Neural Networks and Iterative Annealing,” Proc. of
ECCTD’03, vol. 1, pp. 229-232, Sep. 2003.
[3] A. K. Jain and F. Farrokhnia, “Unsupervised Texture Segmentation Using Gabor Filters,” Pattern. Recogn., vol. 24, pp.
1167-1186, 1991.
[4] K. Obermayer and G. G. Blasde “Geometry of Orientation
and Ocular Dominance Columns in Monkey Striate Cortex”
J. Comput. Neurol., vol. 13, pp. 4114-4129, Oct. 1993.
[5] C.T. Lin, C.H. Huang and S.A. Chen, “CNN-Based HybridOrder Texture Segregation as Early Vision Processing and Its
Implementation on CNN-UM,” IEEE Trans. Circuits Syst.,
vol. 54, no. 10, pp. 2277-2287, Oct. 2007.

Max. [%]
7.7
36.3
13.2
36.3

[6] Cellular Sensory Wave Computers Laboratory Computer andAutomation Research Institute Hungarian Academy of Sciences, “Cellular wave Computing Library (Template, Algorithms, and Programs) Version 2.1”
[7] http://textureking.com/[8] http://textures.forrest.cz/- 555 -