平成21年度電気関係学会四国支部連合大会

359

18-35

Texture Boundary Detection Based on
Color and Orientation Using CNN
Takashi INOUE
Yoshifumi NISHIO(Tokushima University)

1. Introduction
Cellular Neural Networks (CNN) [1] were introduced by
Chua and Yang in 1988. The idea of the CNN was inspired
from the architecture of the cellular automata and theneural networks. The CNN can be used for various image
processing application.
Some scenes in the real world may have some regions
and boundaries. Hence, it is important in image processing to detect those boundaries. C.T. Lin et al. haveproposed the texture segregation as early vision using
CNN [2]. They have conﬁrmed the texture boundary detection of high accuracy by considering the orientation
feature. However, those texture images are gray-scale image, and we feel that the color information should be considered in texture segregation tasks. In particular, when
we segregate the two textures having the same orientation
feature and diﬀerent color, those texture can be segregatedby considering the color information. Thus, this paper will
focus on the color texture boundary detection.
In this study, we propose the texture boundary detection based on color information and orientation featureusing CNN. The proposed CNN can consider color information and orientation feature at the same time, hence,
we can realize the advanced texture boundary detection.
In this paper, we show some texture boundary detection
results using the proposed algorithm.

Figure 2: Proposed algorithm for boundary detection.

3. Simulation Results
In this section, we show some texture boundary detection results. Figures 3 (a) and (c) are color texture input
images, and those images have four regions. In these fourregions, right up region and left up region have the samecolor and diﬀerent orientation feature. Further, right up
region and right down region have diﬀerent color and thesame orientation feature.

2. Proposed Algorithm
In this section, we describe the proposed algorithm for
texture boundary detection. Figure 1 shows the structureof the proposed CNN. The proposed CNN consists of three
single CNN. One of the three layers decides the independent layer and the other two layers have mutual coupling.
Namely, independent layer considers the color informationby using binarization process and two layers consider theorientation feature by using Gabor ﬁlter. Thus, the proposed CNN can consider the both information at the sametime.

(a)

(b)

(c)

(d)

Figure 3: Simulation results. (a) Color texture image.
(b) Boundary detection result for Fig. 3(a). (c) Color texture image. (d) Boundary detection result for Fig. 3(c).
Figure 3(b) shows the texture boundary detection resultfor Fig. 3(a), and we can see that the two textures having
the same orientation and diﬀerent color can be segregated.
However, in Fig. 3(d), we can see that the boundaries offour regions in Fig. 3(c) can not be detected well.

4. Conclusions
Figure 1: Structure of proposed CNN.
Figure 2 shows the proposed algorithm. First, a color
image is converted to three gray-scale images by using theHSB model. Second, we make the histogram from threegray-scale images, and we decide the color of independent
layer from the histogram analysis. Third, the proposed
CNN processes the three gray-scale images in considering
the color information and orientation feature. From third
processing, some regions having the diﬀerent color anddiﬀerent orientation are segregated. Forth, the boundaries of segregated regions are detected by using someprocesses such as “Full Wave Rectiﬁcation” in the conventional CNN. Finally, we can obtain the texture boundary
detection result.

In this study, we have proposed the CNN that could
consider the both information at the same time, and thetexture boundary detection algorithm. By computer simulation, we could conﬁrm advanced texture boundary detection. However, in this paper, we used only simple texture images. In the future works, we should carry out thetexture boundary detection for complex texture images.
References[1] L.O. Chua and L. Yang, “Cellular Neural Networks:Theory,” IEEE Trans. Circuits Syst., vol. 32, no. 10,
pp. 1257-1272, Oct. 1988.
[2] C.T. Lin, C.H. Huang and S.A. Chen,“CNN-Based
Hybrid-Order Texture Segregation as Early Vision Processing and Its Implementation on CNN-UM,” IEEETrans. Circuits Syst., vol. 54, no. 10, pp. 2277-2287, Oct.
2007.