# An Image Edge Detection Algorithm Based on an Artificial Plant Community
## 5 Experimental Analysis
### Experiment Results
These test cases, such as the personal computer, horseman, piano, and house, have different histograms, but the proposed APC algorithm shows a strong ability to connect possible edges and construct an optimal edge image. Despite the image differences and randomness in each test, the proposed APC algorithm can balance the edge detection accuracy and image noise. The Stanford background dataset was then applied to the test. This dataset was selected from many famous databases, including LabelMe, MSRC, Pascal VOC, and Geometric Context. The Stanford background dataset has 715 outdoor images and is divided into eight categories: the sky, trees, roads, grass, water, buildings, mountains, and forest objects. Figure 4 shows our edge detection solutions and histograms on four images from the Stanford background dataset. The four images contain 320 \(\times\) 214, 320 \(\times\) 285, 320 \(\times\) 240, and 320 \(\times\) 212 pixels, respectively. The first column in Figure 4 contains the original images, which have different histograms, as shown in the second column in Figure 4. Columns 3 to 8 show us the solving processes used by the artificial plant community. At the beginning of the test, the plant community is randomly distributed, and edges with habitable values higher than the high threshold are identified first, as shown in column three of Figure 4, where the number of iterations is 20. In the subsequent iterative calculation, the artificial plant community will continuously search for the edges and delete the nonedges, according to the objective function. Compared with the Pascal VOC 2012 test in Figure 3, there are fewer pixels in the Stanford background dataset, decreasing the calculation time. Despite the image differences and randomness in each test, the artificial plant community can successfully build optimal edge images by feeding, growing, and fruiting, as shown from iterations 20 to 120 in Figure 4. These edge results on the Stanford background dataset again show that the artificial plant community algorithm can be used to solve image edge detection problems. After a process of iterative computing of continuous seeding, growing, and fruiting, the proposed APC algorithm can balance the edge detection accuracy and image noise and finish the image edge detection task. To sum up, the results presented in Figures 3 and 4 show us the evolution process of the proposed APC algorithm. The original images in the first columns on Figures 3 and 4 should be preprocessed, including image format conversion, filtering, calculating habitable values, nonmaximum suppressing, and double-threshold checking. After this, the processed images will be detected by the APC algorithm. The next major step involves the initialization, seeding, growing, and fruiting of the APC, and the end judgment. The  
Figure 4: The test results of the proposed algorithm on the Stanford background dataset. image pixels above the high threshold \(Th_{H}\) are classified as edge pixels. After that, the APC repeatedly selects the edge pixels below the high threshold \(Th_{H}\) and eliminates nonedges or false edges until the optimal edge image is obtained.