Colorimetric Characterization of Prints Enhanced with Goniochromatic Pigments

U okviru disertacije predloženo je rešenje za određivanje kolorimetrijskih vrednosti otisaka štampanih goniohromatskim pigmentima na osnovu odziva digitalne kamere. Predmet rada predstavljao je razvoj modela karakterizacije kamere prilagođenog za fitovanje više-ugaonih podataka, kao i ispitivanje uticaja parametara predložene metodologije na tačnost procene vrednosti boja kamerom. Razvijeni model, baziran na veštačkim neuronskim mrežama, omogućio je postizanje zadovoljavajuće preciznosti merenja boja, procenu vrednosti boja svih testiranih mernih geometrija na osnovu snimaka u jednom mernom uglu, a pokazao je i visok stepen adaptivnosti na promenu osvetljenja koje se prilikom merenja koristi. Model je optimizovan primenom genetskog algoritma, čime je njegova efikasnost znatno unapređena.The thesis proposes а solution for colorimetric characterization of prints enhanced with goniochromatic pigments by means of a digital camera. The subject of the research was the development of a camera characterization model adapted to fit multi-angular data and the assessment of the proposed framework parameters impact on the accuracy of camera-based color measurement. The developed model, based on artificial neural networks, enabled accurate color measurement with a satisfactory level of accuracy, estimation of color values of all analyzed measurement geometries on the basis of images obtained in one detection angle, and was proved to be very adaptive to the change of the illuminant used during the measurement. The model was optimized by means of a genetic algorithm, which led to the significant improvement of its efficiency

Colorimetric Characterization of Prints Enhanced with Goniochromatic Pigments

U okviru disertacije predloženo je rešenje za određivanje kolorimetrijskih vrednosti otisaka štampanih goniohromatskim pigmentima na osnovu odziva digitalne kamere. Predmet rada predstavljao je razvoj modela karakterizacije kamere prilagođenog za fitovanje više-ugaonih podataka, kao i ispitivanje uticaja parametara predložene metodologije na tačnost procene vrednosti boja kamerom. Razvijeni model, baziran na veštačkim neuronskim mrežama, omogućio je postizanje zadovoljavajuće preciznosti merenja boja, procenu vrednosti boja svih testiranih mernih geometrija na osnovu snimaka u jednom mernom uglu, a pokazao je i visok stepen adaptivnosti na promenu osvetljenja koje se prilikom merenja koristi. Model je optimizovan primenom genetskog algoritma, čime je njegova efikasnost znatno unapređena.The thesis proposes а solution for colorimetric characterization of prints enhanced with goniochromatic pigments by means of a digital camera. The subject of the research was the development of a camera characterization model adapted to fit multi-angular data and the assessment of the proposed framework parameters impact on the accuracy of camera-based color measurement. The developed model, based on artificial neural networks, enabled accurate color measurement with a satisfactory level of accuracy, estimation of color values of all analyzed measurement geometries on the basis of images obtained in one detection angle, and was proved to be very adaptive to the change of the illuminant used during the measurement. The model was optimized by means of a genetic algorithm, which led to the significant improvement of its efficiency

Colorimetric Characterization of Prints Enhanced with Goniochromatic Pigments

U okviru disertacije predloženo je rešenje za određivanje kolorimetrijskih vrednosti otisaka štampanih goniohromatskim pigmentima na osnovu odziva digitalne kamere. Predmet rada predstavljao je razvoj modela karakterizacije kamere prilagođenog za fitovanje više-ugaonih podataka, kao i ispitivanje uticaja parametara predložene metodologije na tačnost procene vrednosti boja kamerom. Razvijeni model, baziran na veštačkim neuronskim mrežama, omogućio je postizanje zadovoljavajuće preciznosti merenja boja, procenu vrednosti boja svih testiranih mernih geometrija na osnovu snimaka u jednom mernom uglu, a pokazao je i visok stepen adaptivnosti na promenu osvetljenja koje se prilikom merenja koristi. Model je optimizovan primenom genetskog algoritma, čime je njegova efikasnost znatno unapređena.The thesis proposes а solution for colorimetric characterization of prints enhanced with goniochromatic pigments by means of a digital camera. The subject of the research was the development of a camera characterization model adapted to fit multi-angular data and the assessment of the proposed framework parameters impact on the accuracy of camera-based color measurement. The developed model, based on artificial neural networks, enabled accurate color measurement with a satisfactory level of accuracy, estimation of color values of all analyzed measurement geometries on the basis of images obtained in one detection angle, and was proved to be very adaptive to the change of the illuminant used during the measurement. The model was optimized by means of a genetic algorithm, which led to the significant improvement of its efficiency

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Atmospheric Research 2012 Technical Highlights

This annual report, as before, is intended for a broad audience. Our readers include colleagues within NASA, scientists outside the Agency, science graduate students, and members of the general public. Inside are descriptions of atmospheric research science highlights and summaries of our education and outreach accomplishments for calendar year 2012.The report covers research activities from the Mesoscale Atmospheric Processes Laboratory, the Climate and Radiation Laboratory, the Atmospheric Chemistry and Dynamics Laboratory, and the Wallops Field Support Office under the Office of Deputy Director for Atmospheres, Earth Sciences Division in the Sciences and Exploration Directorate of NASAs Goddard Space Flight Center. The overall mission of the office is advancing knowledge and understanding of the Earths atmosphere. Satellite missions, field campaigns, peer-reviewed publications, and successful proposals are essential to our continuing research