207 resultados para Skin Color Segmentation

em Queensland University of Technology - ePrints Archive


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Background: Ultraviolet radiation exposure during an individuals' lifetime is a known risk factor for the development of skin cancer. However, less evidence is available on assessing the relationship between lifetime sun exposure and skin damage and skin aging. Objectives: This study aims to assess the relationship between lifetime sun exposure and skin damage and skin aging using a non-invasive measure of exposure. Methods: We recruited 180 participants (73 males, 107 females) aged 18-83 years. Digital imaging of skin hyper-pigmentation (skin damage) and skin wrinkling (skin aging) on the facial region was measured. Lifetime sun exposure (presented as hours) was calculated from the participants' age multiplied by the estimated annual time outdoors for each year of life. We analyzed the effects of lifetime sun exposure on skin damage and skin aging. We adjust for the influence of age, sex, occupation, history of skin cancer, eye color, hair color, and skin color. Results: There were non-linear relationships between lifetime sun exposure and skin damage and skin aging. Younger participant's skin is much more sensitive to sun exposure than those who were over 50 years of age. As such, there were negative interactions between lifetime sun exposure and age. Age had linear effects on skin damage and skin aging. Conclusion: The data presented showed that self reported lifetime sun exposure was positively associated with skin damage and skin aging, in particular, the younger people. Future health promotion for sun exposure needs to pay attention to this group for skin cancer prevention messaging. (C) 2012 Elsevier B.V. All rights reserved.

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This paper presents an efficient face detection method suitable for real-time surveillance applications. Improved efficiency is achieved by constraining the search window of an AdaBoost face detector to pre-selected regions. Firstly, the proposed method takes a sparse grid of sample pixels from the image to reduce whole image scan time. A fusion of foreground segmentation and skin colour segmentation is then used to select candidate face regions. Finally, a classifier-based face detector is applied only to selected regions to verify the presence of a face (the Viola-Jones detector is used in this paper). The proposed system is evaluated using 640 x 480 pixels test images and compared with other relevant methods. Experimental results show that the proposed method reduces the detection time to 42 ms, where the Viola-Jones detector alone requires 565 ms (on a desktop processor). This improvement makes the face detector suitable for real-time applications. Furthermore, the proposed method requires 50% of the computation time of the best competing method, while reducing the false positive rate by 3.2% and maintaining the same hit rate.

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MC1R gene variants have previously been associated with red hair and fair skin color, moreover skin ultraviolet sensitivity and a strong association with melanoma has been demonstrated for three variant alleles that are active in influencing pigmentation: Arg151Cys, Arg160Trp, and Asp294His. This study has confirmed these pigmentary associations with MC1R genotype in a collection of 220 individuals drawn from the Nambour community in Queensland, Australia, 111 of whom were at high risk and 109 at low risk of basal cell carcinoma and squamous cell carcinoma. Comparative allele frequencies for nine MC1R variants that have been reported in the Caucasian population were determined for these two groups, and an association between prevalence of basal cell carcinoma, squamous cell carcinoma, solar keratosis and the same three active MC1R variant alleles was demonstrated [odds ratio = 3.15 95% CI (1.7, 5.82)]. Three other commonly occurring variant alleles: Val60Leu, Val92Met, and Arg163Gln were identified as having a minimal impact on pigmentation phenotype as well as basal cell carcinoma and squamous cell carcinoma risk. A significant heterozygote effect was demonstrated where individuals carrying a single MC1R variant allele were more likely to have fair and sun sensitive skin as well as carriage of a solar lesion when compared with those individuals with a consensus MC1R genotype. After adjusting for the effects of pigmentation on the association between MC1R variant alleles and basal cell carcinoma and squamous cell carcinoma risk, the association persisted, confirming that presence of at least one variant allele remains informative in terms of predicting risk for developing a solar-induced skin lesion beyond that information wained through observation of pigmentation phenotype.

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Anthocyanin accumulation is coordinated in plants by a number of conserved transcription factors. In apple (Malus × domestica), an R2R3 MYB transcription factor has been shown to control fruit flesh and foliage anthocyanin pigmentation (MYB10) and fruit skin color (MYB1). However, the pattern of expression and allelic variation at these loci does not explain all anthocyanin-related apple phenotypes. One such example is an open-pollinated seedling of cv Sangrado that has green foliage and develops red flesh in the fruit cortex late in maturity. We used methods that combine plant breeding, molecular biology, and genomics to identify duplicated MYB transcription factors that could control this phenotype. We then demonstrated that the red-flesh cortex phenotype is associated with enhanced expression of MYB110a, a paralog of MYB10. Functional characterization of MYB110a showed that it was able to up-regulate anthocyanin biosynthesis in tobacco (Nicotiana tabacum). The chromosomal location of MYB110a is consistent with a whole-genome duplication event that occurred during the evolution of apple within the Maloideae family. Both MYB10 and MYB110a have conserved function in some cultivars, but they differ in their expression pattern and response to fruit maturity.

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In absolute terms, there have been improvements in social resources for all racial and ethnic groups in the United States. The rise in education levels among blacks and Hispanics, for instance, suggests a lessening of the gap between classes, beginning in the later part of the 1960’s (Kao & Thompson, 2003). Yet the divide in income and to a lesser extent education between peoples who differ in gender, skin color and ethnic origin continues and in many ways is greater now than ever (Danziger & Gottschalk, 1997); (Gottschalk, 1997). The psychological distance between those high and those low in social-economic status continues unabated and threatens to undermine the capacity of communities to foster the positive architecture of hope, optimism and equal opportunity that holds us together as a nation...

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The automatic extraction of road features from remote sensed images has been a topic of great interest within the photogrammetric and remote sensing communities for over 3 decades. Although various techniques have been reported in the literature, it is still challenging to efficiently extract the road details with the increasing of image resolution as well as the requirement for accurate and up-to-date road data. In this paper, we will focus on the automatic detection of road lane markings, which are crucial for many applications, including lane level navigation and lane departure warning. The approach consists of four steps: i) data preprocessing, ii) image segmentation and road surface detection, iii) road lane marking extraction based on the generated road surface, and iv) testing and system evaluation. The proposed approach utilized the unsupervised ISODATA image segmentation algorithm, which segments the image into vegetation regions, and road surface based only on the Cb component of YCbCr color space. A shadow detection method based on YCbCr color space is also employed to detect and recover the shadows from the road surface casted by the vehicles and trees. Finally, the lane marking features are detected from the road surface using the histogram clustering. The experiments of applying the proposed method to the aerial imagery dataset of Gympie, Queensland demonstrate the efficiency of the approach.

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Purpose: Computer vision has been widely used in the inspection of electronic components. This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions. Design/methodology/approach: An illumination normalization approach is applied to an image, which can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image the same as in the corresponding image under normal lighting conditions. Consequently special lighting and instrumental setup can be reduced in order to detect solder joints. These normalised images are insensitive to illumination variations and are used for the subsequent solder joint detection stages. In the segmentation approach, the PCB image is transformed from an RGB color space to a YIQ color space for the effective detection of solder joints from the background. Findings: The segmentation results show that the proposed approach improves the performance significantly for images under varying illumination conditions. Research limitations/implications: This paper proposes a front-end system for the automatic detection, localisation, and segmentation of solder joint defects. Further research is required to complete the full system including the classification of solder joint defects. Practical implications: The methodology presented in this paper can be an effective method to reduce cost and improve quality in production of PCBs in the manufacturing industry. Originality/value: This research proposes the automatic location, identification and segmentation of solder joints under different illumination conditions.

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Object segmentation is one of the fundamental steps for a number of robotic applications such as manipulation, object detection, and obstacle avoidance. This paper proposes a visual method for incorporating colour and depth information from sequential multiview stereo images to segment objects of interest from complex and cluttered environments. Rather than segmenting objects using information from a single frame in the sequence, we incorporate information from neighbouring views to increase the reliability of the information and improve the overall segmentation result. Specifically, dense depth information of a scene is computed using multiple view stereo. Depths from neighbouring views are reprojected into the reference frame to be segmented compensating for imperfect depth computations for individual frames. The multiple depth layers are then combined with color information from the reference frame to create a Markov random field to model the segmentation problem. Finally, graphcut optimisation is employed to infer pixels belonging to the object to be segmented. The segmentation accuracy is evaluated over images from an outdoor video sequence demonstrating the viability for automatic object segmentation for mobile robots using monocular cameras as a primary sensor.

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Advances in mobile telephone technology and available dermoscopic attachments for mobile telephones have created a unique opportunity for consumer-initiated mobile teledermoscopy. At least 2 companies market a dermoscope attachment for an iPhone (Apple), forming a mobile teledermoscope. These devices and the corresponding software applications (apps) enable (1) lesion magnification (at least ×20) and visualization with polarized light; (2) photographic documentation using the telephone camera; (3) lesion measurement (ruler); (4) adding of image and lesion details; and (5) e-mail data to a teledermatologist for review. For lesion assessment, the asymmetry-color (AC) rule has 94% sensitivity and 62 specificity for melanoma identification by consumers [1]. Thus, consumers can be educated to recognize asymmetry and color patterns in suspect lesions. However, we know little about consumers' use of mobile teledermoscopy for lesion assessment.

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Background Some apple (Malus × domestica Borkh.) varieties have attractive striping patterns, a quality attribute that is important for determining apple fruit market acceptance. Most apple cultivars (e.g. 'Royal Gala') produce fruit with a defined fruit pigment pattern, but in the case of 'Honeycrisp' apple, trees can produce fruits of two different kinds: striped and blushed. The causes of this phenomenon are unknown. Results Here we show that striped areas of 'Honeycrisp' and 'Royal Gala' are due to sectorial increases in anthocyanin concentration. Transcript levels of the major biosynthetic genes and MYB10, a transcription factor that upregulates apple anthocyanin production, correlated with increased anthocyanin concentration in stripes. However, nucleotide changes in the promoter and coding sequence of MYB10 do not correlate with skin pattern in 'Honeycrisp' and other cultivars differing in peel pigmentation patterns. A survey of methylation levels throughout the coding region of MYB10 and a 2.5 Kb region 5' of the ATG translation start site indicated that an area 900 bp long, starting 1400 bp upstream of the translation start site, is highly methylated. Cytosine methylation was present in all three contexts, with higher methylation levels observed for CHH and CHG (where H is A, C or T) than for CG. Comparisons of methylation levels of the MYB10 promoter in 'Honeycrisp' red and green stripes indicated that they correlate with peel phenotypes, with an enrichment of methylation observed in green stripes. Conclusions Differences in anthocyanin levels between red and green stripes can be explained by differential transcript accumulation of MYB10. Different levels of MYB10 transcript in red versus green stripes are inversely associated with methylation levels in the promoter region. Although observed methylation differences are modest, trends are consistent across years and differences are statistically significant. Methylation may be associated with the presence of a TRIM retrotransposon within the promoter region, but the presence of the TRIM element alone cannot explain the phenotypic variability observed in 'Honeycrisp'. We suggest that methylation in the MYB10 promoter is more variable in 'Honeycrisp' than in 'Royal Gala', leading to more variable color patterns in the peel of this cultivar.