30,665 research outputs found
Region-based Skin Color Detection.
Skin color provides a powerful cue for complex computer vision applications. Although skin color detection
has been an active research area for decades, the mainstream technology is based on the individual pixels.
This paper presents a new region-based technique for skin color detection which outperforms the current
state-of-the-art pixel-based skin color detection method on the popular Compaq dataset (Jones and Rehg,
2002). Color and spatial distance based clustering technique is used to extract the regions from the images,
also known as superpixels. In the first step, our technique uses the state-of-the-art non-parametric pixel-based
skin color classifier (Jones and Rehg, 2002) which we call the basic skin color classifier. The pixel-based skin
color evidence is then aggregated to classify the superpixels. Finally, the Conditional Random Field (CRF)
is applied to further improve the results. As CRF operates over superpixels, the computational overhead is
minimal. Our technique achieves 91.17% true positive rate with 13.12% false negative rate on the Compaq
dataset tested over approximately 14,000 web images
Approximate Lesion Localization in Dermoscopy Images
Background: Dermoscopy is one of the major imaging modalities used in the
diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty
and subjectivity of human interpretation, automated analysis of dermoscopy
images has become an important research area. Border detection is often the
first step in this analysis. Methods: In this article, we present an
approximate lesion localization method that serves as a preprocessing step for
detecting borders in dermoscopy images. In this method, first the black frame
around the image is removed using an iterative algorithm. The approximate
location of the lesion is then determined using an ensemble of thresholding
algorithms. Results: The method is tested on a set of 428 dermoscopy images.
The localization error is quantified by a metric that uses dermatologist
determined borders as the ground truth. Conclusion: The results demonstrate
that the method presented here achieves both fast and accurate localization of
lesions in dermoscopy images
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