# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 4 Experiment
### Results  
In the process of seed point sampling, a method of adaptive seed placement based on the contour prior is adopted, which can better adapt to the local image structure. The traditional regular grid sampling method ignores the complexity of image information, leading to under-segmentation in complex regions when the sampling interval is large. To improve the boundary adherence capability, most existing segmentation algorithms usually increase the sampling frequency, which leads to unnecessary segmentation in flat regions. In this section, we compare the results of the proposed method with those of state-of-the-art methods. The optimal parameters suggested by the authors were used for all comparative experiments, and the expected number of superpixels was set as 150. We implemented the \begin{table}
\begin{tabular}{c c} \hline
**Detector Type** & **Uncooled VOx Microbolometer** \\ \hline Pixel Pitch & 12 \(\upmu\)m \\ \hline Spectral Response & LWIR, 8–14 \(\upmu\)m \\ \hline NETD & \textless{}40 mK \\ \hline Frame Rate & 60 Hz \\ \hline Focal Length & 40 mm \\ \hline \end{tabular}
\end{table} 
Table 1: The basic parameters of the IR camera. proposed technique in MATLAB 2018a and tested it on an i5-8400 CPU with 16 GB of RAM. The parallel computing and multi-threaded GPU acceleration were prohibited. Figures 10a-c and 10d-f show quantitative comparisons of \(BR\) and \(USE\), respectively, on the self-built dataset, BSD500 and 3 Dircadb. In terms of the quantitative comparison, a similar conclusion can be inferred; that is, SSCE achieved better boundary adherence with fewer number of superpixels, while also keeping ahead with a higher number. As revealed in Figure 10a-c, when the expected number of superpixels was less than 300, the \(BR\) value of SSCE significantly differed from the others, demonstrating that SSCE has a dramatic advantage in boundary adherence. As the number of superpixels increases, the \(BR\) values of all methods gradually increased and became closer, but the growth rate slowed down significantly. SLIC and SNIC cannot generate superpixels based on image content, thus their performance lagged behind the others. The proposed SSCE clearly outperformed the others with a small number of superpixels, and was only rivaled by CAS when the number of superpixels was large. Benefitting from the boundary constraint in the clustering process, the \(USE\) value of SSCE indicated the best performance, as SSCE uses a weighted penalty for crossing borders. As the number of superpixels increased, all methods leaked fewer pixels from the ground segmentation, but the gain continually decreased. LRW can further segment superpixels based on thresholds, which is better than SLIC and SNIC. USEAQ and CAS also produced better results when a large expected number was set. From the analysis of the quantitative results, only our method achieved high-precision boundary adherence with fewer superpixels. The average running time statistics of all tested methods for the three datasets were shown in Table 2. In all runtime tests, the data reading process was not included. From the results, it could be concluded that all methods were sensitive to image resolution, the higher the resolution, the longer the running time. LRW has the longest running time, while SSCE performed better than CAS. In cluster-based methods, SLIC and SNIC run less time since there were no additional conditions to constrain the clustering process. Overall, our approach had a higher yield. Figure 10: Quantitative evaluation of other superpixel segmentation algorithms and the proposed SSCE: (**a–c**) \(BR\) curves; (**d–f**) \(USE\) curves of self-built dataset, BSD500 and 3 Dircadb, respectively. According to Figure 11, SLIC, SNIC, LRW, and our method generated regularly shaped superpixels in the smooth regions.