Summary

Our resaearch theme is Computational Forensics(CF). CF requires joint efforts by forensics and computational scientists with benefits to both. To complement the lack of knowledge among forensic and computer experts, we collaborate with forensics domain experts(from the University of Yamanashi, etc.). The goal of “Computational XXXX” is to realize the intellectual activities that human beings originally wanted to do using computer graphics(CG) and computational resources.

The essential thing in forensic science is to justify the severity of the crime. CF can numerically quantify how much malice the attacker harbored when he attacked the victim, the concept of “malice index.” Malice index would be a quantitative indicator, tailored specially for the jury, calculated from the severity of injuries or other factors such as the angle of the blade. Besides, it also could be used to compare with past precedents to determine the appropriate punishment. In our opinion, this is one of the CF ways forward. 

We are developing efficient user interfaces for investigations, forensic analysis, and sentencing in court, with the concept of LMML(Legal Medicine Markup Language).

LMML:Leagal Medicine Markup Language

Because the forensic reports collecting the data are written manually, the recording of data collected through traditional autopsy is still a cumbersome, time-consuming task. Our framework, based on LMML to store, describe and arrange forensic data, aims at overcoming those issues. It allows quicker, smoother input of forensic data, for better automation and visualization thereof, so that they can be used by medical examiners, investigators, as well as judicial courts.

THEMIS:THeoritical Estimation of Meaning of InSults

THEMIS (Theotretical Estimation of Meaning of InSults) as a sub-system of computational forensics, It is used to stakeholders for analyses and presents similarity between photographs recorded in LMML files.

Mathematical Model of Meaning

The mathematical model of meaning is a method proposed by Professor Kiyoki et al., who is the local of this project, to calculate the semantic similarity and described in 1995 SIGMOD Record. For example, the word “green” has multiple meanings associated with it, but considering that it is a “road”-related context, “green” means “go” It is possible to show that it is close to Furthermore, not only for documents but also for multimedia, it is possible to calculate the semantic similarity regardless of modal by extracting the semantic elements possessed by the content and applying meaning.

Detecting means of wound

In order to apply a mathematical model of meaning to a wound image, THEMIS makes a meaning of the wound. There are two major characteristics of “morphology” and “color” as the items of meaning to be extracted. The outline of the wound is extracted from the wound image by image processing, and the morphological features (whether it is an open wound or a sharp wound) are extracted from the outline. On the other hand, for the color characteristics, the representative color of the wound is extracted using the color system of L*a*b* as the characteristic of the wound. By expressing the extracted features as a vector, similarity calculation is performed using a mathematical model of meaning.

Members

NameAffiliationWeb site
Baoqing WangKeio university
Yume AsayamaKeio university
Masanori NakayamaKeio univercity
Yasushi KiyokiKeio university
Xiaoyang MaoUniversity of YamanashiMao laboratory
Yuriko TakeshimaTokyo university of technologyTokyo University of Technology staff profile
Noboru AdachiUniversity of YamanashiUniversity of Yamanashi Department of Legal Medicine staff profile
Hideki ShojoUniversity of YamanashiUniversity of Yamanashi Department of Legal Medicine staff profile

Publications

Conferences

  1. Malik Olivier Boussejra, Noboru Adachi, Hideki Shojo, Ryohei Takahashi, Issei Fujishiro: “LMML: Initial Developments of an Integrated Environment for Forensic Data Visualization,” in EuroVis 2016 – Short Papers, pp. 31-pp. 35, 2016. (pdf)
  2. Malik Olivier Boussejra, Noboru Adachi, Hideki Shojo, Ryohei Takahashi, Issei Fujishiro: “LMML: Describing injuries for forensic data visualization,” in 2016 Nicograph International (NicoLin.), pp. 153, 2016. Best poster award (pdf)

Presentations

  1. Yume Asayama, Baoqing Wang, Masanori Nakayama, Hideki Shojo, Noboru Adachi, Yasushi Kiyoki, Issei Fujishiro: “Visual analysis of wound similarity using mathematical model of meaning,” in Proceedings of the 48th Symposium on Visualization in Japan, Online,  September 24―26, 2020(in Japanese).
  2. Yume Asayama, Baoqing Wang, Malik Olivier Boussejra, Masanori Nakayama, Hideki Shojo, Noboru Adachi, Yasushi Kiyoki, Issei Fujishiro: “Visual analysis of wound similarity using mathematical model of meaning,” in Proceedings of the 82th National Convention of International Processing Society of Japan, Vol. 2, pp. 155―156, Kanazawa Institute of Technology, Ishikawa, Online, March 5―7, 2016 (in Japanese). Student encouragement award
  3. Malik Olivier Boussejra, Noboru Adachi, Hideki Shojo, Ryohei Takahashi, Issei Fujishiro: “LMML: Developing the environment of the LMML mark-up language for forensic data visualization,” in The Journal of the Institute of Image Electronics Engineers of Japan, vol.45(1), pp. 127, 2016. Malik Olivier Boussejra, Noboru Adachi, Hideki Shojo, Ryohei Takahashi, Issei Fujishiro: “LMML: Developing the environment of the LMML mark-up language for forensic data Visualization,” in The Journal of the Institute of Image Electronics Engineers of Japan, vol.45(1), pp. 127, 2016.

Grants

  1. Grant-in-Aid for Scientific Research (A): 21H04916 (2021—2026)
  2. Grant-in-Aid for Scientific Research (A): 17H00737 (2017—2021)
  3. Grant-in-Aid for Scientific Research (A): 26240015 (2014—2017)

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