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: Short wave infrared radiation, Reflectivity, Principal component analysis, Calibration, Data modeling, Sensors, Hyperspectral imaging, Hyperspectral sensing, MATLAB, Inspection

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

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

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

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

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

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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