Deriving dimensionless features for color object recognition in different color models

Albrecht Melan; Stephan Rudolph

doi:10.1117/12.487055

25 August 2003 Deriving dimensionless features for color object recognition in different color models

Albrecht Melan, Stephan Rudolph

Proceedings Volume 5096, Signal Processing, Sensor Fusion, and Target Recognition XII; (2003) https://doi.org/10.1117/12.487055
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

Pattern recognition is an important aspect of image processing. Image features are computed from image objects and subsequently used by an object classificator to map (and therefore classify) image objects into their corresponding object classes. To avoid misclassification the image features used should be selected in such a way that they represent the image object similarity appropriately. Similarity however is a well known theoretical concept in physics, where similar phenomena are mathematically expressed as constant dimensionless numbers. These dimensionless numbers are determined from the dimensional representation of the relevant variables by means of a technique called dimensional analysis. In consequence, the concept of dimensional analysis is applied for the derivation of dimensionless features of color images based on various color models. The properties such as color constancy of the resulting dimensionless numbers are studied using analytical and numerical examples. Also the similarity resulting from the different color models is analyzed and discussed.

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Albrecht Melan and Stephan Rudolph "Deriving dimensionless features for color object recognition in different color models", Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); https://doi.org/10.1117/12.487055

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KEYWORDS

RGB color model

Object recognition

Binary data

Image processing

Pattern recognition

Physics

Mathematical modeling

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