Statistical color models with application to skin detection
International Journal of Computer Vision
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of skin-color modeling and detection methods
Pattern Recognition
Unsupervised colour image segmentation applied to printing quality assessment
Image and Vision Computing
Effect of colorspace transformation, the illuminance component, and color modeling on skin detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Original paper: Automatic segmentation of relevant textures in agricultural images
Computers and Electronics in Agriculture
Expert Systems with Applications: An International Journal
IEEE Transactions on Consumer Electronics
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Crop segmentation from the images taken in the outdoor fields is a complex task. In this paper, a new morphology modeling method is utilized to establish the crop color model in the CIE L^*a^*b^* (or Lab for simplification) color space and to realize the crop image segmentation. In the supervised learning stage, morphology modeling is applied to deal with the color characteristics of the crop with respect to the pixel lightness component and establish the crop color model. To verify the performance of the proposed method, 56 test images which in size of 601x601 and taken from April 27, 2011 to May 21, 2011 are utilized to compare the proposed method with eight other famous approaches. Experiment shows that the segmentation quality of the proposed method is approximately 87.2% for the Automatic Target Recognition Working Group (ATRWG) evaluation method and 96.0% for another evaluation method. Moreover, the segmentation performance for images taken on cloudy, overcast and sunny days is analyzed. Experiment demonstrates that our method is robust to the variation of illumination in the field and performed better than eight other approaches. Furthermore, the impact of different structuring element types to the proposed method is compared. Overall, the proposed crop segmentation method can be used to crop segmentation in the field effectively.