Welcome to the Hidekata Hontani's site.

My research interests include Model-to-image registration, Medical image analysis, Shape Representation, Multi-scale analysis of images, Early vision, and Calibration of sensor networks.


We started a research of real-time analysis of high-speed motions, for which the time-correlation image sensor is used.


We proposed a robust non-rigid registration method that is based on sparsity-regularization. This technique can automatically detect outliers and ignore them to register the model accurately.


This year, we focused on the structure of the graphical model that represents the statistical shape model of a point distribution model.


I was involved in UCLA vision lab as a visiting researcher this year. Robust surface registration was one of my main research topics in the lab.


The project Computational Anatomy started this year. The followings is the abstract of this research project.

This project aims to establish a new discipline "Computational Anatomy", which provides a mathematical framework to deal with human anatomy. The challenges consist of (1) development of theories for representation of anatomical models that cover inter-individual variability in shape and topology and its construction through statistical analysis of population data, (2) investigation of methodologies for precise and robust retrieval of anatomical information from medical images, virtually equivalent to real human body dissection, and (3) development of innovative technologies assisting medical diagnosis and interventions based on computational anatomy. The outcomes are expected to contribute to advanced medicine, basic biomedical research, medical education, and information science.