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2020 | OriginalPaper | Chapter

New Method for Extreme Color Detection in Images

Authors : Manuel G. Forero, Julián Ávila-Navarro, Sergio Herrera-Rivera

Published in: Pattern Recognition

Publisher: Springer International Publishing

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Abstract

In image processing and computer vision, it is common to find applications, in which it is necessary to detect reference points characterized by extreme color, i.e., a primary color RGB or complementary CMY with very high saturation. Thus, there are cases in which a certain class of objects can be distinguished according to their characteristic extreme color, which can be used as landmarks or to identify objects. Therefore, there is an interest in identifying landmarks characterized by extreme colors. In this paper, a new method for detecting objects with an extreme color is introduced and compared with other approaches found in the literature. The methods are analyzed and compared using a color palette in which a transition between R, G, B, C, M and Y colors is generated. The results obtained show that the methods studied allow the specific colors to be adequately discriminated, while the proposed method is the only one that allows the full range of extreme colors R, G, B, C, M and Y to be detected, being more selective than the others, by taking practically the areas corresponding to each color separately .

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Literature
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go back to reference Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson (2018) Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson (2018)
2.
go back to reference García-Vanegas, A., Liberato-Tafur, B., Forero, M.G., Gonzalez-Rodríguez, A., Castillo-García, F.: Automatic vision based calibration system for planar cable-driven parallel robots. In: Morales, A., Fierrez, J., Sánchez, J.S., Ribeiro, B. (eds.) IbPRIA 2019, Part I. LNCS, vol. 11867, pp. 600–609. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31332-6_52CrossRef García-Vanegas, A., Liberato-Tafur, B., Forero, M.G., Gonzalez-Rodríguez, A., Castillo-García, F.: Automatic vision based calibration system for planar cable-driven parallel robots. In: Morales, A., Fierrez, J., Sánchez, J.S., Ribeiro, B. (eds.) IbPRIA 2019, Part I. LNCS, vol. 11867, pp. 600–609. Springer, Cham (2019). https://​doi.​org/​10.​1007/​978-3-030-31332-6_​52CrossRef
6.
go back to reference Forero, Manuel G., Herrera-Rivera, S., Ávila-Navarro, J., Franco, C.A., Rasmussen, J., Nielsen, J.: Color classification methods for perennial weed detection in cereal crops. In: Vera-Rodriguez, R., Fierrez, J., Morales, A. (eds.) CIARP 2018. LNCS, vol. 11401, pp. 117–123. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13469-3_14CrossRef Forero, Manuel G., Herrera-Rivera, S., Ávila-Navarro, J., Franco, C.A., Rasmussen, J., Nielsen, J.: Color classification methods for perennial weed detection in cereal crops. In: Vera-Rodriguez, R., Fierrez, J., Morales, A. (eds.) CIARP 2018. LNCS, vol. 11401, pp. 117–123. Springer, Cham (2019). https://​doi.​org/​10.​1007/​978-3-030-13469-3_​14CrossRef
Metadata
Title
New Method for Extreme Color Detection in Images
Authors
Manuel G. Forero
Julián Ávila-Navarro
Sergio Herrera-Rivera
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-49076-8_9

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