Advertising images increasingly require attractive faces to attract the public's attention. Several studies have been conducted to enhance facial attractiveness in images. While some researchers suggest changes in...
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Advertising images increasingly require attractive faces to attract the public's attention. Several studies have been conducted to enhance facial attractiveness in images. While some researchers suggest changes in geometrical shape, others advocate modifying the appearance of the facial skin, however, there have been few attempts to explore the possibility of combining both techniques. this paper sets out a novel method of doing this: facial geometry and skin texture modifications. Our method, which is based on supervised machine learning techniques, is able to improve the attractiveness of faces in images while preserving the original features of the picture. We also demonstrate the effectiveness of this combination by carrying out two different evaluations. Accordingly, we analyze the significance of each change that is designed to improve attractiveness by comparing the original image with a) the image in which only the facial geometry has been modified, b) the image in which only the texture skin has been modified and finally c) the image with both modifications. Our results reveal that the combination of geometric and skin texture modifications results in the most significant enhancement. It also demonstrates that modifications to the skin texture can be regarded as more important to obtain an attractive face than changes to the facial geometry. Additionally, evaluations are provided to quantify the gain in facial attractiveness and it should be pointed out that our method is the first to employ these, since there are no references to such tests in the literature.
this paper presents a half toning-based watermarking method. this method enables the embedding of a color image into a binary black-and-white halftone, while maintaining the image quality. the proposed technique is ca...
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this paper presents a half toning-based watermarking method. this method enables the embedding of a color image into a binary black-and-white halftone, while maintaining the image quality. the proposed technique is capable of embedding watermarks of three color channels into a binary halftone. To achieve high quality halftones, the method maps colors to halftone channels with homogeneous dot patterns which in turn use different binary texture orientations to carry the watermark. they are obtained by solving a minimization problem in which the objective function is the binary distance between the original binary halftone and the available patterns. To restore the color information, we scan the printed halftone image and compute the inverse information (considering the dot pattern). Using the mapped information, we restore the original color channels from the halftone images using a high-quality inverse half toning algorithm. Experimental results show that the method produces restorations with a superior quality than other methods found in the literature and increases the embedding capacity.
this book contains five survey papers written on the topics of the tutorials presented at the 25thsibgrapi - conference on graphics, patterns and images, held in Ouro Preto, Minas Gerais, Brazil from August 22-25, 20...
this book contains five survey papers written on the topics of the tutorials presented at the 25thsibgrapi - conference on graphics, patterns and images, held in Ouro Preto, Minas Gerais, Brazil from August 22-25, 2012. this is the fourth year that tutorial papers from sibgrapi are published by IEEE CPS. the authors of accepted tutorials are invited to write survey papers about the topics and concepts presented during the tutorial sessions. this selection includes survey papers on exciting topics in computer graphics, image processing and computer vision. We hope this material will inspire new exciting research in our fields.
the emergence of low cost sensors capable of providing texture and depth information of a scene is enabling the deployment of several applications such as gesture and object recognition and three-dimensional reconstru...
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the emergence of low cost sensors capable of providing texture and depth information of a scene is enabling the deployment of several applications such as gesture and object recognition and three-dimensional reconstruction of environments. However, commercially available sensors output low resolution data, which may not be suitable when more detailed information is necessary. Withthe purpose of increasing data resolution, at the same time reducing noise and filling the holes in the depth maps, in this work we propose a method that combines depth fusion and image reconstruction in a super-resolution framework. By joining low-resolution intensity images and depth maps in an optimization process, our methodology creates new images and depth maps of higher resolution and, at the same time, minimizes issues related withthe absence of information (holes) in the depth map. Our experiments show that the proposed approach has increased the resolution of the images and depth maps without significant spawning of artifacts. Considering three different evaluation metrics, our methodology outperformed other three techniques commonly used to increase the resolution of combined images and depth maps acquired with low resolution, commercially available sensors.
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