## 3 Results
Figures 20-22 illustrate how our object perception method outperforms other methods for the same set of test images shown in Figure 15a-c, respectively. [PERSON]'s thresholding method for Canny edge detection is unable to distinguish between the objects both for dicy cells in water and HUVECs in Matrigel. The regular MSER method also yields spurious features for the HUVECs and cannot detect the dicy cell accurately. The other two methods, namely, MSER with circularity parameter and SURF, perform reasonably well overall but cannot always differentiate a boundary object from a fully visible object, or a cell from a bead, and show greater inaccuracies in tracing the object boundaries. (As a side note, the MSER and SURF methods are considerably faster than our method ([PERSON]'s thresholding method is slightly slower) and take about 0.3 s to run on an average. In scenarios where image processing speed is of maximum importance, either of those methods can be applied instead of our blob detection step if bead clusters are not present.) Figure 19: Processed images of densely clustered silica beads in Matrigel: (**a**) single cluster with one bead sightly separated from the rest; (**b**) multiple clusters; (**c**) single cluster with all the beads tightly aggregated. Figure 21: Processed images of human endothelial cells and silica beads in water using various perception methods: (**a**) MSER with circularity parameter; (**b**) SURF; (**c**) Canny edge detector with [PERSON]’s thresholding; (**d**) MSER. Figure 20: Processed images of amoeba cells and silica beads in water using various perception methods: (**a**) Maximally stable extremal regions (MSER) with circularity parameter; (**b**) Speeded up robust features (SURF); (**c**) Canny edge detector with [PERSON]’s thresholding; (**d**) MSER. Quantitative performance comparisons of object perception are presented in Figure 23 for dicty cells in water, in Figure 24 for HUVECs in water, in Figure 25 for HUVECs in Matrigel, and in Figure 26 for clustered beads in Matrigel, respectively. Other than in the case of the clustered beads, we estimate the error for a particular test image as the sum of the relative deviation of estimated bead radius, bead centroid location, cell bounding box area, and cell centroid location from manually annotated (ground truth) values averaged over all the beads and cells in the image. Any false detection or missing detection of an object counts to a relative error measure of unity for that particular object. For the clustered beads, the error is calculated as the number of beads that are not identified by the perception method. A bead is considered to be unidentified if its predicted center location is off by more than five pixels in any direction. (We overestimate the performance of the [PERSON]'s thresholding method due to our choice of error calculation that ignores spurious feature detections.) Figure 23: Performance comparison of different perception methods for dicty cells and silica beads in water. Figure 22: Processed images of human endothelial cells and silica beads in Matrigel using various perception methods: (**a**) MSER with circularity parameter; (**b**) SURF; (**c**) Canny edge detector with [PERSON]’s thresholding; (**d**) MSER. Figure 24: Performance comparison of different perception methods for human endothelial cells and silica beads in water. Figure 25: Performance comparison of different perception methods for human endothelial cells and silica beads in Matrigel. Our method consistently shows smaller error measures both in terms of median value (inaccuracy) and inter-quartile range (imprecision) as compared to all the other methods for the test images. Broadly speaking, the median accuracy of our method is between 80-90% and the range of precision lies between 70-75%. It performs slightly better for the spherical HUVECs as compared to the irregular-shaped dicty cells. It also performs substantially better than the other methods for the tightly clustered beads.