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

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

Temperature, recreational fishing and diapause egg connections : dispersal of spiny water fleas (Bythotrephes longimanus)

© The Author(s), 2011. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License. The definitive version was published in Biological Invasions 13 (2011): 2513-2531, doi:10.1007/s10530-011-0078-8.The spiny water flea (Bythotrephes longimanus) is spreading from Great Lakes coastal waters into northern inland lakes within a northern temperature-defined latitudinal band. Colonization of Great Lakes coastal embayments is assisted by winds and seiche surges, yet rapid inland expansion across the northern states comes through an overland process. The lack of invasions at Isle Royale National Park contrasts with rapid expansion on the nearby Keweenaw Peninsula. Both regions have comparable geology, lake density, and fauna, but differ in recreational fishing boat access, visitation, and containment measures. Tail spines protect Bythotrephes against young of the year, but not larger fish, yet the unusual thick-shelled diapausing eggs can pass through fish guts in viable condition. Sediment traps illustrate how fish spread diapausing eggs across lakes in fecal pellets. Trillions of diapausing eggs are produced per year in Lake Michigan and billions per year in Lake Michigamme, a large inland lake. Dispersal by recreational fishing is linked to use of baitfish, diapausing eggs defecated into live wells and bait buckets, and Bythothephes snagged on fishing line, anchor ropes, and minnow seines. Relatively simple measures, such as on-site rinsing of live wells, restricting transfer of certain baitfish species, or holding baitfish for 24 h (defecation period), should greatly reduce dispersal.Study of Lakes Superior and Michigan was funded from NSF OCE-9726680 and OCE-9712872 to W.C.K., OCE-9712889 to J. Churchill. Geographic survey sampling and Park studies in the national parks during 2008-2010 were funded by a grant from the National Park Service Natural Resource Preservation Program GLNF CESU Task Agreement No. J6067080012

Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure.

Numerous genetic loci have been associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP) in Europeans. We now report genome-wide association studies of pulse pressure (PP) and mean arterial pressure (MAP). In discovery (N = 74,064) and follow-up studies (N = 48,607), we identified at genome-wide significance (P = 2.7 × 10(-8) to P = 2.3 × 10(-13)) four new PP loci (at 4q12 near CHIC2, 7q22.3 near PIK3CG, 8q24.12 in NOV and 11q24.3 near ADAMTS8), two new MAP loci (3p21.31 in MAP4 and 10q25.3 near ADRB1) and one locus associated with both of these traits (2q24.3 near FIGN) that has also recently been associated with SBP in east Asians. For three of the new PP loci, the estimated effect for SBP was opposite of that for DBP, in contrast to the majority of common SBP- and DBP-associated variants, which show concordant effects on both traits. These findings suggest new genetic pathways underlying blood pressure variation, some of which may differentially influence SBP and DBP

An optimized skin texture model using gray-level co-occurrence matrix

Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group of neighbor pixels in the image instead of relying on color information of individual pixels solely. Due to decent performance of the gray-level co-occurrence matrix (GLCM) in texture analysis of natural objects, this study employs this technique to analyze the human skin texture characteristics. The main goal of this study is to investigate the impact of major GLCM parameters including quantization level, displacement magnitudes, displacement direction and GLCM features on skin segmentation and classification performance. Each of these parameters has been assessed and optimized using an exhaustive supervised search from a fairly large initial feature space. Three supervised classifiers including Random Forest, Support Vector Machine and Multilayer Perceptron have been employed to evaluate the performance of the feature space subsets. Evaluation results using Edith Cowan University (ECU) dataset showed that the proposed texture-assisted skin detection model outperformed pixelwise skin detection by significant margin. The proposed method generates an F-score of 91.98, which is satisfactory, considering the challenging scenario in ECU dataset. Comparison of the proposed texture-assisted skin detection model with some state-of-the-art skin detection models indicates high accuracy and F-score of the proposed model. The findings of this study can be used in various disciplines, such as face recognition, skin disorder and lesion recognition, and nudity detection