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Variation in skin pigmentation can be affected by both environmental factors and intrinsic factors such as age, gender, and genetic variation. Recent GWASs revealed that genetic variants of genes functionally related to a pigmentation pathway were associated with skin pigmentary traits. However, these GWASs focused on populations with European ancestry, and only a few studies have been performed on Asian populations, limiting our understanding of the genetic basis of skin pigmentary traits in Asians. To evaluate the genetic variants associated with facial pigmented spots, we conducted a GWAS analysis of objectively measured facial pigmented spots in 17,019 Korean women. This large-scale GWAS identified several genomic loci that were significantly associated with facial pigmented spots (five previously reported loci and two previously unreported loci, to our knowledge), which were detected by UV light: BNC2 at 9p22 (rs16935073; P-value = 2.11 × 10−46), PPARGC1B at 5q32 (rs32579; P-value = 9.04 × 10−42), 10q26 (rs11198112; P-value = 9.66 × 10−38), MC1R at 16q24 (rs2228479; P-value = 6.62 × 10−21), lnc01877 at 2q33 (rs12693889; P-value = 1.59 × 10−11), CDKN2B-AS1 at 9p21 (rs643319; P-value = 7.76 × 10−9), and MFSD12 at 19p13 (rs2240751; P-value = 9.70 × 10−9). Further functional characterization of the candidate genes needs to be done to fully evaluate their contribution to facial pigmented spots.

This paper presents a new document image dewarping method that removes geometric distortions in camera-captured document images. The proposed method does not directly use the text-line which has been the most widely used feature for the document dewarping. Instead, we use the discrete representation of text-lines and text-blocks which are the sets of connected components. Also, we model the geometric distortions caused by page curl and perspective view as the generalized cylindrical surfaces and camera rotation respectively. With these distortion models and the discrete representation of the features, we design a cost function whose minimization yields the parameters of the distortion model. In the cost function, we encode the properties of the pages such as text-block alignment, line-spacing, and the straightness of text-lines. By describing the text features using the sets of discrete points, the cost function can be easily defined and efficiently solved by Levenberg-Marquadt algorithm. Experiments show that the proposed method works well for the various layouts and curved surfaces, and compares favorably with the conventional methods on the standard dataset. HighlightsDocument dewarping is an important problem in camera-based OCR.We formulate the dewarping as an optimization problem.The proposed method performs dewarping in a fully automatic manner.The proposed method can handle various layouts of documents.Our method yields the improved OCR performances compared with other methods.