## 4 Discussion  
In this paper, we present an accurate and robust method to perceive the two dimensional shapes and locations of both biological and colloidal microscale objects from low contrast bright field images. We leverage a smudge mark removal technique that works well in outdoor operations if the images are taken by an unclean camera lens. More generally, we combine existing image contrast enhancement, convolution neural nets (CNNs) and edge detection algorithms in a novel manner to detect and distinguish between cells and beads in microenvironments of varying fluid compositions even when the beads are close to the cells, the cells change shapes dynamically, and the beads are densely clustered. Our method demonstrates consistently better performance than other state-of-the-art methods on time-lapse images comprising spherical human endothelial cells of varying sizes and irregular-shaped amoeba cells. It is also able to identify the individual silica beads within clusters inside polymerizing Matrigel media, when all the other methods completely fail to do so.  
Thus, we believe that our perception method will play a foundational role in enabling automated optical manipulation of multiple cells concurrently using indirect robotic grasping strategies that minimize the adverse effects on cell viabilities. It is also expected to serve as a generic template for any form of imaging-guided micromanipulation regardless of the type of actuation. In the future, we plan to implement our method on a GPU platform to reduce the processing time by an order of magnitude. We also intend to train our CNN classification model to handle more complicated scenarios involving heterogeneous clusters of beads and cells robustly. Our recently-developed sparse topological data analysis (Sparse-TDA) method [52] could be then used to extract the optimal set of sparse features to be fed to the CNN model for facilitating multi-way classification. Moreover, we aim to extend the method for perceiving objects in three dimensions. Such perception would fuse information from multiple images taken at different \(z\)-depths by a moving camera, and would, therefore, require more extensive use of supervised machine learning techniques to learn not just the positions but also the shapes of different microscale objects.  
Figure 26: Performance comparison of different perception methods for clustered beads in Matrigel.  
**Acknowledgments:** We gratefully acknowledge the support of the College of Engineering at the University of Washington in covering the open access publication costs. We would also like to thank [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their assistance in preparing the cell samples.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed materials/analysis tools; [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funding sponsor had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.