The paper describes an innovative image annotation tool, based on a multi-class Support Vector Machine, for classifying image pixels in one of seven classes - sky, skin, vegetation, snow, water, ground, and man-made structures - or as unknown. These visual categories mirror high-level human perception, permitting the design of intuitive and effective color and contrast enhancement strategies. As a pre-processing step, a smart color balancing algorithm is applied, making the overall procedure suitable for uncalibrated images, such as images acquired by unknown systems under unknown lighting conditions.

Schettini, R., Gasparini, F., & Cusano, C. (2005). Image annotation for adaptive enhancement of uncalibrated color images. In 8th International Conference on Visual Information Systems (pp.216-225). Springer [10.1007/11590064_19].

Image annotation for adaptive enhancement of uncalibrated color images

SCHETTINI, RAIMONDO;GASPARINI, FRANCESCA;CUSANO, CLAUDIO
2005

Abstract

The paper describes an innovative image annotation tool, based on a multi-class Support Vector Machine, for classifying image pixels in one of seven classes - sky, skin, vegetation, snow, water, ground, and man-made structures - or as unknown. These visual categories mirror high-level human perception, permitting the design of intuitive and effective color and contrast enhancement strategies. As a pre-processing step, a smart color balancing algorithm is applied, making the overall procedure suitable for uncalibrated images, such as images acquired by unknown systems under unknown lighting conditions.
No
poster + paper
Scientifica
image annotation, adaptive enhancement, uncalibrated color, SVM, pixel classification;
English
8th International Conference on Visual Information Systems (VISUAL 2005) - 5 July 2005 through 5 July 2005
978-3-540-30488-3
Schettini, R., Gasparini, F., & Cusano, C. (2005). Image annotation for adaptive enhancement of uncalibrated color images. In 8th International Conference on Visual Information Systems (pp.216-225). Springer [10.1007/11590064_19].
Schettini, R; Gasparini, F; Cusano, C
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/13590
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