Automatic Recognition of DNA Pliers in Atomic Force Microscopy Images
- 173 Downloads
Bottom-up fabrication techniques are expected to alleviate the limitations of top-down fabrication. Bottom-up fabrication requires self-assembling facilities to construct complex structures including DNA nanostructures, DNA robots, further molecular robots, and so on. DNA origami is one of buttom-up fabrication techniques. In this paper, we focus on the automatic recognition of flexible DNA origami named “DNA pliers” in AFM (atomic force microscopy) image. Auto recognition of DNA pliers is challenging and necessary since DNA pliers can have several forms: parallel, cross, and anti-parallel forms, depending on hinge angles. Our method uses the information of the curvature scale space method and convexity-concavity detection extracted from DNA pliers. The experiments show that the combination of the curvature scale space method and convexity-concavity detection can work well for DNA pliers recognition if appropriate contour information for the DNA pliers is available from an AFM image.
KeywordsAtomic Force Microscope (AFM) Image Analysis Curvature Scale Space Technique Convexity-concavity Detection DNA Nanostructures Molecular Robotics
Unable to display preview. Download preview PDF.
- 5.Kuzuya, A., Sakai, Y., Yamazaki, T., Xu, Y. and Komiyama, M., “Nanomechanical DNA origami ‘single-molecule beacons’ directly imaged by atomic force microscopy,” Nature Communications, 2, 449, doi:10.1038/ncomms1452, 2011.
- 9.Mokhtarian, F. and Mackworth, A., “A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 8, pp. 789–805, 1992.Google Scholar
- 10.Mokhtarian, F. and Bober, M., Curvature Scale Space Representation: Theory, Applications, and Mpeg-7 Standardization (Computational Imaging and Vision), Kluwer Academic Pub, United States, 2003.07.Google Scholar
- 11.Han, Y., Koike, H. and Idesawa, M., “Recognizing Objects with Multiple Configurations,” Pattern Analysis and Applications, 17, 1, pp. 195–209, 2014.Google Scholar
- 12.Bay, H., Ess, A., Tuytelaars, T. and Gool, L. V., “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding (CVIU), 110, 3, pp. 346–359, 2008.Google Scholar
- 14.Han, Y., “Recognize Objects with Three Kinds of Information in Landmarks,” Pattern Recognition, 46, 11, pp. 2860–2873, 2013.Google Scholar
- 15.Ando, N., Suehiro, T., Kitagaki, K., Kotoku, T. and Yoon, W., “RT-Middleware: Distributed Component Middleware for RT (Robot Technology),” 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2005), pp. 3933–3938, 2005.08.Google Scholar
- 16.Ando, N., Suehiro, T. and Kotoku, T., “A Software Platform for Component Based RT-System Development: OpenRTM-Aist,” International Conference on SIMULATION, MODELING and PROGRAMMING for AUTONOMOUS ROBOTS (SIMPAR 2008), Venice, Italy, ISSN 0302-9743, pp.87–98, 2008.Google Scholar
- 17.Han, Y., Sumi, Y., Matsumoto, Y. and Ando, N., “Acquisition of Object Pose from Barcode for Robot Manipulation,” Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR 2012), Tsukuba, Japan, Date: 5-8 Nov. 2012, pp. 299–310, 2012.Google Scholar