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Foreign Body Detection in Ear Under the guidance of : Dr. Phalguni Gupta Mr. Aaditya Nigam. Abhra Dasgupta (13111003) Muktinath Vishwakarma (13111034). Problem Statement. Given an image of an ear we are to detect the presence of a foreign body(e.g.: ear-ring) if present. Literary Survey.
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Foreign Body Detection in EarUnder the guidance of : Dr. PhalguniGuptaMr. AadityaNigam AbhraDasgupta(13111003) Muktinath Vishwakarma(13111034)
Problem Statement • Given an image of an ear we are to detect the presence of a foreign body(e.g.: ear-ring) if present.
Literary Survey • ‘Ear Localization from Side Face Images using Distance Transform and Template Matching’ by Surya Prakash, UmaraniJayaraman and PhalguniGupta.
First method • The skin color lies in a limited chromatic range. • We tried to fix the threshold manually by obtaining a range in which skin color lies in the image. • The true positives in this case were actually good. • Quite a few false positives were also detected.
Second method • We here implemented the skin color segmentation to determine the skin likelihood. • Determine the threshold by the method of adaptive thresholding.
Color based skin segmentation • In RGB color space, the triple components (R, G, B) represent not only color but also luminance . • This may vary across a person’s face due to the ambient lighting. • This is not a reliable measure in separating skin from non-skin regions.
Color based skin segmentation • Luminance can be removed from the color representation in the chromatic color space by normalization. r = R/(R + G + B) b = B/(R + G + B) • green color is redundant after the normalization as r+g+b= 1 . • Although skin colorsof different people vary over a wide range, they differ much in brightness than color.
Color based skin segmentation • Since color histogram of skin-color distribution of different people is clustered at one place in the chromatic color space, it can be represented by a Gaussian model N(μ ; C), where mean μand covariance C can be defined as: • where x = and E[x] denotes the expectation of the predicate x. • The likelihood P(r , b) of skin is given by :
Third approach • We observed that the problems in both cases are usually different. • So now we are trying to use an intersection of both the approaches to get a better result.
Analysis till now • We from the obtained results attain that the combining of both the approaches is optimal.
After database collection • We have now collected a database of over 1000 images of ear. • We need to check the performance of the approach on the images of the new database.
Conclusion • Even though the performance of both approaches is not very good independently, the combining of both the methods yields very good results.
