Color segmentation robust to brightness variations by using B-spline curve modeling

Kim, Chiho; You, Bum-Jae; Jeong, Mun-Ho; Kim, Hagbae

doi:10.1016/j.patcog.2007.04.011

Cited by 32 publications

(12 citation statements)

References 24 publications

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“…Strong correlations among CIEXYZ components make it an unfavorable color space for any image segmentation application including skin and face detection; however some researchers including Chen and Chiang [ 16 ], Brown et al [ 12 ] and Gonzalez et al [ 17 ] used this color space for skin detection purposes. HSV and HSI are two popular cylindrical color space that have been employed by many researchers including Khan et al [ 18 ], Zarit et al [ 6 ], Gonzalez et al [ 17 ], Juang and Shiu [ 19 ], Kim et al[ 20 ], Singh et al [ 21 ], Zainuddin et al[ 22 ], Oliveira and Conci [ 23 ], Kovac et al [ 24 ] for skin and face detection aims. Since these color spaces separate the illumination and chrominance, they might be suitable for skin and face detection under uncontrolled illumination condition.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

et al. 2015

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Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

et al. 2015

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“…Kim et al . propose a color modeling in HSI color space considering intensity information by adopting the B‐spline curve fitting to make a mathematical model for statistical characteristics of a color with respect to intensity . The statistical characteristics contain the mean and standard deviation of hue and saturation with respect to intensity.…”

Section: Previous Workmentioning

confidence: 99%

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

Alvarado-Cervantes

Felipe-Riveron

Khartchenko

et al. 2016

Color Research & Application

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In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of the a* and b* color channels rejecting L* and (2) a probabilistic approach on a* and b* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017

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“…Since the color of single-color object distributed on the hue-saturation(HS) plane changes with luminance [21], the green pixel model applied in HSI color space shall adapt to the changes in external light condition. However, according to the image statistics of crops, the histogram of hue is similar to Gaussian distribution in certain strength (I).…”

Section: ) Hi Color Modelmentioning

confidence: 99%

Automatic recognition of rape seeding emergence stage based on computer vision technology

Fang

Chang

Zhai

et al. 2014

2014 the Third International Conference on Agro-Geoinformatics

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Acquisiton of crop growth stage information can not only help to analyze the relation between the crop growth process and environmental condition, but also to guide the field operation effectively. Therefore, different growth stages of rape crops are monitored with the visual system constructed in this paper, and the first critical growth stage of rape is detected automatically, which is seeding emergence stage. The rape should be first extracted from the image. Considering the the impacts of the complicated environment and climatic changes, HI color segmentation method is adopted to segment the crops from the background. Then, two limited conditions, cotyledon area and density, are applied to judge whether it is at seeding emergence stage. Eventually, the experimental results are compared to the ones from other mature methodologies and manual observation, and it shows that the proposed methodology is effective and feasible, and it can provide support for precision agriculture.

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Color segmentation robust to brightness variations by using B-spline curve modeling

Cited by 32 publications

References 24 publications

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

Automatic recognition of rape seeding emergence stage based on computer vision technology

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