@conference {13609,
	title = {No-reference image quality assessment based on visual codebook},
	booktitle = {Image Processing (ICIP), 2011 18th IEEE International Conference on},
	year = {2011},
	month = {2011/09//},
	pages = {3089 - 3092},
	abstract = {In this paper, we propose a new learning based No-Reference Image Quality Assessment (NR-IQA) algorithm, which uses a visual codebook consisting of robust appearance descriptors extracted from local image patches to capture complex statistics of natural image for quality estimation. We use Gabor filter based local features as appearance descriptors and the codebook method encodes the statistics of natural image classes by vector quantizing the feature space and accumulating histograms of patch appearances based on this coding. This method does not assume any specific types of distortion and experimental results on the LIVE image quality assessment database show that this method provides consistent and reliable performance in quality estimation that exceeds other state-of-the-art NR-IQA approaches and is competitive with the full reference measure PSNR.},
	keywords = {assessment;quality, codebook;Gabor, descriptors;complex, estimation;visual, extraction;image, filter;appearance, filters;feature, gabor, image, image;no-reference, patches;natural, QUALITY, statistics;local, texture;},
	doi = {10.1109/ICIP.2011.6116318},
	author = {Ye,Peng and David Doermann}
}
@conference {12509,
	title = {How would you look as you age ?},
	booktitle = {Image Processing (ICIP), 2009 16th IEEE International Conference on},
	year = {2009},
	month = {2009/11//},
	pages = {53 - 56},
	abstract = {Facial appearances change with increase in age. While generic growth patterns that are characteristic of different age groups can be identified, facial growth is also observed to be influenced by individual-specific attributes such as one{\textquoteright}s gender, ethnicity, life-style etc. In this paper, we propose a facial growth model that comprises of transformation models for facial shape and texture. We collected empirical data pertaining to facial growth from a database of age-separated face images of adults and used the same in developing the aforementioned transformation models. The proposed model finds applications in predicting one{\textquoteright}s appearance across ages and in performing face verification across ages.},
	keywords = {age-separated, appearances;facial, database;face, Face, growth, image, model;face, model;facial, models;facial, recognition;image, SHAPE, TEXTURE, texture;, transformation, verification;facial},
	doi = {10.1109/ICIP.2009.5413998},
	author = {Ramanathan,N. and Chellapa, Rama}
}
@conference {12546,
	title = {Stochastic fusion of multi-view gradient fields},
	booktitle = {Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on},
	year = {2008},
	month = {2008/10//},
	pages = {1324 - 1327},
	abstract = {Image gradients form powerful cues in a host of vision and graphics applications. In this paper, we consider multiple views of a textured planar scene and consider the problem of estimating the scene texture map using these multi-view inputs. Modeling each camera view as a projective transformation of the scene, we show that the problem is equivalent to that of studying the effect of noise (and the projective imaging) on the gradient fields induced by this texture map. We show that these noisy gradient fields can be modeled as complete observers of the scene radiance. Further, the corrupting noise can be shown to be additive and linear, although spatially varying. However, the specific form of the noise term can be exploited to design linear estimators that fuse the gradient fields obtained from each of the individual views. The fused gradient field forms a robust estimate of the scene gradients and can be used for scene reconstruction.},
	keywords = {application;brightness;cameras;gradient, application;image, camera, estimators;multiview, fields;projective, fusion;image, fusion;textured, gradient, gradients;linear, gradients;scene, imaging;scene, map;stochastic, methods;image, noise;graphics, planar, radiance;scene, reconstruction;image, reconstruction;scene, scene;vision, TEXTURE, texture;, view;corrupting},
	doi = {10.1109/ICIP.2008.4712007},
	author = {Sankaranarayanan,A. C and Chellapa, Rama}
}
@conference {13136,
	title = {Segmentation using Meta-texture Saliency},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We address segmentation of an image into patches that have an underlying salient surface-roughness. Three intrinsic images are derived: reflectance, shading and meta- texture images. A constructive approach is proposed for computing a meta-texture image by preserving, equalizing and enhancing the underlying surface-roughness across color, brightness and illumination variations. We evaluate the performance on sample images and illustrate quantitatively that different patches of the same material, in an image, are normalized in their statistics despite variations in color, brightness and illumination. Finally, segmentation by line-based boundary-detection is proposed and results are provided and compared to known algorithms.},
	keywords = {analysis;image, colour, enhancement;image, image, image;salient, patches;image, segmentation;image, segmentation;meta-texture, surface-roughness;image, texture;},
	doi = {10.1109/ICCV.2007.4408930},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {12724,
	title = {View synthesis of articulating humans using visual hull},
	booktitle = {Multimedia and Expo, 2003. ICME {\textquoteright}03. Proceedings. 2003 International Conference on},
	volume = {1},
	year = {2003},
	month = {2003/07//},
	pages = {I - 489-92 vol.1 - I - 489-92 vol.1},
	abstract = {In this paper, we present a method, which combines image-based visual hull and human body part segmentation for overcoming the inability of the visual hull method to reconstruct concave regions. The virtual silhouette image corresponding to the given viewing direction is first produced with image-based visual hull. Human body part localization technique is used to segment the input images and the rendered virtual silhouette image into convex body parts. The body parts in the virtual view are generated separately from the corresponding body parts in the input views and then assembled together. The previously rendered silhouette image is used to locate the corresponding body parts in input views and avoid the unconnected or squeezed regions in the assembled final view. Experiments show that this method can improve the reconstruction of concave regions for human postures and texture mapping.},
	keywords = {analysis;, body, convex, gesture, hull;, human, image, image-based, image;, mapping;, MOTION, part, parts;, postures;, recognition;, reconstruction;, segmentation;, silhouette, synthesis;, TEXTURE, texture;, view, virtual, visual},
	doi = {10.1109/ICME.2003.1220961},
	author = {Yue,Zhanfeng and Liang Zhao and Chellapa, Rama}
}
