Ms. Jie Yang Profile

Jie Yang

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Publications (5)

SPIE Journal Paper | 7 March 2018

Bloodstain detection and discrimination impacted by spectral shift when using an interference filter-based visible and near-infrared multispectral crime scene imaging system

Jie Yang, David W. Messinger, Roger R. Dube

OE, Vol. 57, Issue 03, 033101, (March 2018) https://doi.org/10.1117/12.10.1117/1.OE.57.3.033101

KEYWORDS: Transmittance, Multispectral imaging, Iterated function systems, Imaging systems, Blood, Optical filters, Calibration, Absorbance, Forensic science, Visible radiation

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Proceedings Article | 5 May 2017 Presentation + Paper

Fixed Pattern Noise pixel-wise linear correction for crime scene imaging CMOS sensor

Jie Yang, David Messinger, Roger Dube, Emmett Ientilucci

Proceedings Volume 10198, 1019802 (2017) https://doi.org/10.1117/12.2262035

KEYWORDS: Image sensors, Image enhancement, Digital imaging, Interference (communication), Error analysis, Iterated function systems, Calibration, Imaging systems, CMOS sensors, Sensors, Cameras, Target detection, Optical sensors, Optical filters

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Proceedings Article | 17 May 2016 Paper

Spectral feature characterization methods for blood stain detection in crime scene backgrounds

Jie Yang, Jobin Mathew, Roger Dube, David Messinger

Proceedings Volume 9840, 98400E (2016) https://doi.org/10.1117/12.2224099

KEYWORDS: Blood, Forensic science, Near infrared, Absorption, Liquids, Bidirectional reflectance transmission function, Hyperspectral imaging, Target detection, Spectroscopy, Cameras

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Blood stains are one of the most important types of evidence for forensic investigation. They contain valuable DNA information, and the pattern of the stains can suggest speciﬁcs about the nature of the violence that transpired at the scene. Blood spectral signatures containing unique reﬂectance or absorption features are important both for forensic on-site investigation and laboratory testing. They can be used for target detection and identiﬁcation applied to crime scene hyperspectral imagery, and also be utilized to analyze the spectral variation of blood on various backgrounds. Non-blood stains often mislead the detection and can generate false alarms at a real crime scene, especially for dark and red backgrounds. This paper measured the reﬂectance of liquid blood and 9 kinds of non-blood samples in the range of 350 nm - 2500 nm in various crime scene backgrounds, such as pure samples contained in petri dish with various thicknesses, mixed samples with diﬀerent colors and materials of fabrics, and mixed samples with wood, all of which are examined to provide sub-visual evidence for detecting and recognizing blood from non-blood samples in a realistic crime scene. The spectral diﬀerence between blood and non-blood samples are examined and spectral features such as “peaks” and “depths” of reﬂectance are selected. Two blood stain detection methods are proposed in this paper. The ﬁrst method uses index to denote the ratio of “depth” minus “peak” over“depth” add“peak” within a wavelength range of the reﬂectance spectrum. The second method uses relative band depth of the selected wavelength ranges of the reﬂectance spectrum. Results show that the index method is able to discriminate blood from non-blood samples in most tested crime scene backgrounds, but is not able to detect it from black felt. Whereas the relative band depth method is able to discriminate blood from non-blood samples on all of the tested background material types and colors.

Proceedings Article | 17 May 2016 Paper

Comparison of algorithms for blood stain detection applied to forensic hyperspectral imagery

Jie Yang, David Messinger, Jobin Mathew, Roger Dube

Proceedings Volume 9840, 98400X (2016) https://doi.org/10.1117/12.2223293

KEYWORDS: Hyperspectral imaging, Detection and tracking algorithms, Blood, Forensic science, Principal component analysis, Imaging systems, Scene simulation, Cameras, RGB color model, Visualization

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Proceedings Article | 21 May 2015 Paper

The development of a DIRSIG simulation environment to support instrument trade studies for the SOLARIS sensor

Aaron Gerace, Adam Goodenough, Matthew Montanaro, Jie Yang, Joel McCorkel

Proceedings Volume 9472, 947214 (2015) https://doi.org/10.1117/12.2177507

KEYWORDS: Sensors, Bidirectional reflectance transmission function, RGB color model, Data modeling, Calibration, Atmospheric modeling, MODIS, Device simulation, Environmental sensing, Image sensors

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