Results - Implementation and Results - 利用權重彩色與灰階共現機率之三維紋理合成

Chapter 4 Implementation and Results

4.2 Results

It takes about 2 minutes and 22 seconds to generate weighted color and grey level co-occurrence probability features. It takes different times to construct similarity sets based on characteristics of an input 2D texture. However, it does not exceed 1.5 hours.

And it takes about 3 hours 20 minutes to construct a 3D-candidate set. Once the feature vectors and similarity sets are constructed, they are used in syntheses process with different target sizes for results. It takes about 1 day and 1 hour for synthesis process.

We compare our approach with the previous method which only extracts color features. As we can see in Fig. 4.1~Fig. 4.8, our results preserve more features and structures better than the results synthesized by the previous approach. This is because we can obtain more information than the previous method. The previous method only extracts color features to generate feature vectors from input 2D texture, but our approach extracts not only color features but also GLCP features. The grey level co-occurrence method is one of the statistical methods. GLCP features describe the probability of any grey level occurring spatially relative to any other grey level within a given window for each pixel. From the distribution, we can obtain the information of various characteristics

for each pixel from input 2D texture. For irregular textures, color features are hard to record various characteristics accurately for each pixel. However, GLCP features record accurately the various characteristics because of the statistical relatedness among pixel pair within a given window for each pixel.

The input 2D texture in Fig. 4.1(a) is a stochastic and marble-like texture. And the input 2D texture in Fig. 4.2(a) is a particle-like texture. They only contain two kinds of colors, and they are vivid. Fig. 4.1(b) shows the result synthesized by the previous approach and Fig. 4.1(c) shows our result. As we can see from the results, the quality of our method is almost the same as that of the previous method. And our result is continuous and not the duplication of the input 2D texture. As long as there are few complete particle patterns in the input 2D texture, our approach and the previous method can synthesize the desired results, as shown in Fig. 4.2(b) and Fig. 4.2(c). From Fig. 4.1 and Fig. 4.2, our approach and the previous method can preserve features and structures.

The input 2D texture in Fig. 4.3(a) is a stochastic and marble-like texture. The input 2D texture in Fig. 4.4(a) is a kind of stochastic textures. The input 2D texture in Fig.

4.5(a) is a stochastic and camouflage-like texture. In Fig. 4.3(c), our result preserves more white features in whole area. But in the previous result (Fig. 4.3(b)), it preserves

less white features. And our result is more colorful than that of the previous method. In Fig. 4.4(c), our result preserves complete features in whole area. But the previous result (Fig. 4.4(b)), it does not preserve features as we can see in Fig. 4.5(c), our result preserves more features than that of the previous method (Fig. 4.5(b)). From Fig. 4.3 and Fig. 4.4 and Fig. 4.5, we can preserve more features than the previous method.

The input 2D textures in Fig. 4.6(a), Fig. 4.7(a), and Fig. 4.8(a) are grey level textures. They are stochastic patterns as we can see in Fig. 4.6(b), our result preserves more black features in whole area. But the previous result (Fig. 4.6(b)) preserves less black features. In Fig. 4.7(c), color variation of our result is better than that of the previous method (Fig. 4.7(b)). Our result is information-rich. The previous result does not keep the features as we can see in Fig. 4.8(c), our result preserves white features in whole area. But the previous result (Fig. 4.8(b)) preserves less white features. From Fig.

4.6, Fig. 4.7 and Fig. 4.8, the quality of our approach is better than that of the previous method.

We can see in Fig. 4.1 and Fig. 4.2, the results of our approach and the previous method are similar. However, we can see in Fig 4.3 and 4.8, the results of our approach are better than the previous method. This is because they are regular textures in Fig. 4.1(a)

and Fig. 4.2(a), so we can synthesize good results by only using color features. But in Fig 4.3(a) and 4.8(a), they are irregular textures, but color features can not record the information of various characteristics accurately. GLCP features can solve the above problem because of the statistical relatedness among pixel pair within a given window for each pixel. Therefore, our approach can synthesize well than previous method.

Figure 4.1 The comparison between the previous method and our method (a) input 2D texture