Prof. Giuseppe Bonifazi
Full Professor at Sapienza Univ di Roma
SPIE Involvement:
Author | Instructor
Publications (49)

PROCEEDINGS ARTICLE | May 15, 2018
Proc. SPIE. 10665, Sensing for Agriculture and Food Quality and Safety X
KEYWORDS: Hyperspectral imaging, Short wave infrared radiation, MATLAB, Principal component analysis, Data modeling, Sensors, Calibration, Inspection, Reflectivity, Hyperspectral sensing

PROCEEDINGS ARTICLE | May 15, 2018
Proc. SPIE. 10665, Sensing for Agriculture and Food Quality and Safety X
KEYWORDS: Near infrared, Principal component analysis, Data modeling, Sensors, Calibration, Spectroscopy, Reflectivity, Nondestructive evaluation, Solids, Near infrared spectroscopy

PROCEEDINGS ARTICLE | May 14, 2018
Proc. SPIE. 10662, Smart Biomedical and Physiological Sensor Technology XV
KEYWORDS: Near infrared, Principal component analysis, Tissues, Sensors, Calibration, Reflectivity, Near infrared spectroscopy, Chemometrics, In vivo imaging, Analytical research

PROCEEDINGS ARTICLE | February 21, 2018
Proc. SPIE. 10489, Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis
KEYWORDS: Near infrared, Principal component analysis, Data modeling, Tissues, Sensors, Calibration, Reflectivity, Near infrared spectroscopy, Chemometrics, Analytical research

PROCEEDINGS ARTICLE | May 1, 2017
Proc. SPIE. 10217, Sensing for Agriculture and Food Quality and Safety IX
KEYWORDS: Hyperspectral imaging, Near infrared, Visible radiation, Optical sensors, Principal component analysis, Tissues, Cameras, Remote sensing, Reflectivity, Vegetation, Multispectral imaging, Image sensors, Image classification, Chemometrics

PROCEEDINGS ARTICLE | May 1, 2017
Proc. SPIE. 10217, Sensing for Agriculture and Food Quality and Safety IX
KEYWORDS: Hyperspectral imaging, Near infrared, Principal component analysis, Cameras, Reflectivity, Nondestructive evaluation, Solids, Neodymium, Liquids, RGB color model

Showing 5 of 49 publications
Course Instructor
SC511: Applied Imaging Based Morphology
This course provides the attendee with basic working knowledge to perform morphological and morphometrical based characterization of closed domains (objects, cells, biological tissues, particles, etc.). It gives the fundamentals of digital morphology and shows its great potential for use in many research and industrial sectors. Case studies are presented and evaluated. Attendees will become fluent in the selection and design of analytical tools and architectures used to perform a morphological and morphometrical characterization of electronic images content.
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