# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 5 Experiment and Result Analysis
### Comparison of Algorithms
Depending on the position of the random sampling ROI, as shown in Figure 13c, some of the reflected shadows of the product's lighting were recognized, resulting in a decrease in IoU performance. As the HSV value of the shadow ROI of the product was measured similarly to that of the reference ROI of the product, it was difficult to clearly distinguish the boundary between the background and the foreground. Figure 11: Test 1 product result of the proposed algorithm: (**a**) reference ROI for auto HSV color picking; (**b**) merge operation through random sample ROIs with close HSV Color similarity; (**c**) Test 1 product result (random sampling ROI: 150); (**d**) another Test 1 product result (random sampling ROI: 150); (**e**) another Test 1 product result (random sampling ROI: 150); (**f**) another Test 1 product result (random sampling ROI: 70); (**g**) another Test 1 product result (random sampling ROI: 70). Figure 12: Test 2 product result of proposed algorithm: (**a**) reference ROI for auto color picking; (**b**) merge operation through random sample ROIs with close HSV color similarity; (**c**) Test 2 product result (random sampling ROI: 150); (**d**) another Test 2 product result (random sampling ROI: 150); (**e**) another Test 2 product result (random sampling ROI: 150); (**f**) another Test 2 product result (random sampling ROI:70); (**g**) another Test 2 product result (random sampling ROI:70). Figure 14 shows the performance evaluation results when the size of the reference ROI is large and the reference ROI contains a different color (e.g., blue, similar to the bottom surface). This shows that the object localization result is poor, including the shadow of the bottom surface of the product. The proposed algorithm randomly samples an ROI within the background space from a single HSV color range and compares the HSV color similarity with a reference ROI. In addition, HSV color-based object localization selects only ROIs from random sampling that contain a color similar to the HSV color of the reference ROI as the candidate HSV colors. The Test 3 product consists of a product and a supporting external frame with an empty space between the two, where the color of the bottom surface (blue) of the 3D printer is expressed. Therefore, in auto HSV color picking, if the HSV of the reference ROI is mixed with multiple colors, it is difficult to clearly distinguish the product boundary, as shown in Figure 14.  
Table 1 summarizes the test comparison results of the algorithms used for object localization. The GrabCut algorithm shows the highest performance of the overall algorithms. The GrabCut algorithm exhibits good results because the rectangular window set by the user is recognized as the foreground, and anything other than the rectangle is recognized as the background; however, it has the drawback as the user must intervene directly and set the bounding box every time the product is changed. Background subtraction using Otsu and GMM algorithms showed high performance when not affected by light and lighting conditions but did not otherwise find the position of the object accurately. As shown in  
Figure 14: Test 3 product result of proposed algorithm: (**a**) reference ROI for auto color picking; (**b**) final mask binarization image after merge operation (random sampling ROI: 70); (**c**) Test 3 product result (random sampling ROI: 70). Figure 13: Test 3 product result of proposed algorithm: (**a**) reference ROI for auto color picking; (**b**) final mask binarization image after merge operation (random sampling ROI: 150); (**c**) Test 3 product result (random sampling ROI: 150); (**c**) another Test 3 product result (random sampling ROI: 150); (**d**) final mask binarization image after merge operation (random sampling ROI: 70) of another Test 3 product in Figure 12g; (**g**) another Test 3 product result (random sampling ROI:70); (**h**) another Test 3 product result (random sampling ROI:70).