## 3 Results
### Experimental Results and Comparisons
#### 3.2.4 Support Vector Machine Classification Results Based on Texture Features  
As shown in Figure 15, after SSR enhancement, the gray levels inside the scars were haphazard, which was related to the degree of decay inside the scars, and different scars presented different gray intensity characteristics. In contrast to the scars, the stem/calyx area had a distinct regularity to it. Both the apple stem and the calyx showed a gray intensity characteristic of lower gray intensity internally and then gradually recovered to the same gray intensity as the normal surface area as the degree of indentation diminished.  
Figure 14: Detection effectiveness of each method on complex images with edge information. (**a**) original figure; (**b**) genetic algorithm segmentation result; (**c**) edge detection algorithm result; (**d**) segmentation effect of the proposed image segmentation method.  
Figure 13: Detection effectiveness of each method on complex images with light reflectance. (**a**) Original figure; (**b**) genetic algorithm segmentation result; (**c**) edge detection algorithm result; (**d**) segmentation effect of the proposed image segmentation method.  
In order to investigate the effect of improved SSR image enhancement and adaptive gamma-corrected image processing methods on texture features, the texture features of the three image processing stages of extracting the R component, improved SSR image enhancement, and adaptive gamma correction were collected as sample datasets. A total of five texture features, angular second moment, contrast, entropy, inverse differential moment, and correlation, were collected for each sample, and the three-stage stem/calyx and scar texture features are shown in Figure 16. It can be seen that the distribution of data for each textural feature of the stems/calyxes has a certain regularity compared to the scars. Further analysis showed that compared to the R-component maps, the randomness of the amount of information became larger after SSR image enhancement, and the clarity and regularity of the texture were significantly improved (the value of the inverse differential moment reflected this information). The texture contrast was further increased after further adaptive gamma correction compared to the SSR image enhancement map.  
The texture features of the defective samples obtained from the images of the three image processing stages were fed into the support vector machine for binary classification, respectively. The results, as shown in Table 1, indicate that SSR image enhancement and adaptive gamma correction were effective in highlighting differences in textural features between stems/calyxes and scars.  
The decision hyperplane expression was obtained by training the texture data obtained by the full image processing process as follows:  
\[\begin{array}{l}\mathrm{y}=0.00473\times ASM+0.05776\times CON+0.27029\times 1 DM \\ \qquad\qquad+0.03863\times ENT+0.01327\times COR-3.60491\end{array} \tag{29}\]  
It is worth noting that IDM accounted for a relatively large proportion of the overall classification judgments, reflecting the fact that the clarity and regularity of the texture were quite important for stem/calyx identification, as predicted early in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Image Processing** & **Number of Stem/** & **Number of Scar** & **Total** & **Accuracy (\%)** \\
**Stage** & **Calyx Textures** & **Textures** & **Textures** & **Textures** \\ \hline R-component & 200 & 230 & 430 & 84.4 \\ SSR & 200 & 230 & 430 & 93.0 \\ Gamma & 200 & 230 & 430 & 93.7 \\ \hline \end{tabular}
\end{table}
Table 1: Stem/calyx recognition accuracy.  
Figure 15: Some SSR image enhancement results. (a) Scars; (b) stems/calyxes.