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I am interested in software and computing technologies that are theoretically-sound and practically-applicable in civilian, military, healthcare and multimedia applications. To that end, I am particularly interested in perception, sensor fusion, learning methods that can make these technologies as realities. I have been working on single or multi-camera visual information processing for detecting, tracking, and recognizing objects. These research activities lead to algorithms and software capable of understanding object behaviors in biomedical and natural scene images.
Biomedical. The data explosion by recording microscopy images during high-throughput experiments makes the labor-intensive manual analysis prohibitive. I have been working on a large-scale cell tracking system for stem cell engineering and discovery. The goal is to analyze time-lapse microscope images, recover the complete individual spatiotemporal histories of all the cells that undergo migration (movement) and proliferation (division), and output a comprehensive lineage tree of cell growth that records the history of divisions, movement trajectories, and change of characteristics (such as shape and appearance).
Natural scenes. Different types of sensors such as radar and color/thermal/depth cameras allow us to measure 3D scene structures, infer the identity and location of objects, and recognize the dynamic activities and events. An active system with robotic actuators can response to the perception based on the high-level cognition. My interests in these research topics include: designing algorithms capable of analyzing object motion and segmenting the object from the scene background; creating object representations and search techniques for persistent object tracking; and developing methods to model and recognize objects.
Wenchao Jiang and Zhaozheng Yin, “Seeing the Invisible in Differential Interference Contrast Microscopy Images,” Medical Image Analysis. 34: 65-81. December 2016.
Hang Su, Zhaozheng Yin, Seungil Huh, Takeo Kanade, and Jun Zhu, “Interactive Cell Segmentation based on Active and Semi-supervised Learning,” IEEE Transactions on Medical Imaging. 35(3):762-777, March 2016.
Haohan Li, Zhaozheng Yin, and Yingke Xu, “A Deep Learning Framework for Automated Vesicle Fusion Detection,” IEEE International Symposium on Biomedical Imaging (ISBI), 2017.
Hang Su, Jun Zhu, Zhaozheng Yin, Yinpeng Dong, and Bo Zhang, “Efficient and Robust Semi-supervised Learning over a Sparse-Regularized Graph,” the 14th European Conference on Computer Vision (ECCV), 2016.
Yunxiang Mao and Zhaozheng Yin “A Hierarchical Convolutional Neural Network for Mitosis Detection in Phase-Contrast Microscopy Images,” the 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2016.
CPS: Synergy: Collaborative Research: Cyber-Physical Sensing, Modeling, and Control with Augmented Reality for Smart Manufacturing Workforce Training and Operations Management. National Science Foundation. $505,287. 2/2017-1/2020.
The Missouri Transect: Climate, Plants and Community. National Science Foundation. $661,431. 08/2014-08/2019.
CAREER: Microscopy Image Analysis to Aid Biological Discovery: Optics, Algorithms and Community. National Science Foundation. $488,148 + $16,000 (REU Supplement). 05/2014-04/2019.
Computer Vision, Biomedical Imaging, Machine Learning, Signal Processing, and Robotics.