Gaussian Filter (gaussian + filter)

Distribution by Scientific Domains


Selected Abstracts


Interactive shadowing for 2D Anime

COMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 2-3 2009
Eiji Sugisaki
Abstract In this paper, we propose an instant shadow generation technique for 2D animation, especially Japanese Anime. In traditional 2D Anime production, the entire animation including shadows is drawn by hand so that it takes long time to complete. Shadows play an important role in the creation of symbolic visual effects. However shadows are not always drawn due to time constraints and lack of animators especially when the production schedule is tight. To solve this problem, we develop an easy shadowing approach that enables animators to easily create a layer of shadow and its animation based on the character's shapes. Our approach is both instant and intuitive. The only inputs required are character or object shapes in input animation sequence with alpha value generally used in the Anime production pipeline. First, shadows are automatically rendered on a virtual plane by using a Shadow Map1 based on these inputs. Then the rendered shadows can be edited by simple operations and simplified by the Gaussian Filter. Several special effects such as blurring can be applied to the rendered shadow at the same time. Compared to existing approaches, ours is more efficient and effective to handle automatic shadowing in real-time. Copyright © 2009 John Wiley & Sons, Ltd. [source]


Fast High-Dimensional Filtering Using the Permutohedral Lattice

COMPUTER GRAPHICS FORUM, Issue 2 2010
Andrew Adams
Abstract Many useful algorithms for processing images and geometry fall under the general framework of high-dimensional Gaussian filtering. This family of algorithms includes bilateral filtering and non-local means. We propose a new way to perform such filters using the permutohedral lattice, which tessellates high-dimensional space with uniform simplices. Our algorithm is the first implementation of a high-dimensional Gaussian filter that is both linear in input size and polynomial in dimensionality. Furthermore it is parameter-free, apart from the filter size, and achieves a consistently high accuracy relative to ground truth (> 45 dB). We use this to demonstrate a number of interactive-rate applications of filters in as high as eight dimensions. [source]


Source Camera Identification for Heavily JPEG Compressed Low Resolution Still Images,

JOURNAL OF FORENSIC SCIENCES, Issue 3 2009
Erwin J. Alles M.Sc.
Abstract:, In this research, we examined whether fixed pattern noise or more specifically Photo Response Non-Uniformity (PRNU) can be used to identify the source camera of heavily JPEG compressed digital photographs of resolution 640 × 480 pixels. We extracted PRNU patterns from both reference and questioned images using a two-dimensional Gaussian filter and compared these patterns by calculating the correlation coefficient between them. Both the closed and open-set problems were addressed, leading the problems in the closed set to high accuracies for 83% for single images and 100% for around 20 simultaneously identified questioned images. The correct source camera was chosen from a set of 38 cameras of four different types. For the open-set problem, decision levels were obtained for several numbers of simultaneously identified questioned images. The corresponding false rejection rates were unsatisfactory for single images but improved for simultaneous identification of multiple images. [source]


Reduction of errors in ASL cerebral perfusion and arterial transit time maps using image de-noising

MAGNETIC RESONANCE IN MEDICINE, Issue 3 2010
Jack A. Wells
Abstract In this work, the performance of image de-noising techniques for reducing errors in arterial spin labeling cerebral blood flow and arterial transit time estimates is investigated. Simulations were used to show that the established arterial spin labeling cerebral blood flow quantification method exhibits the bias behavior common to nonlinear model estimates, and as a result, the reduction of random errors using image de-noising can improve accuracy. To assess the effect on precision, multiple arterial spin labeling data sets acquired from the rat brain were processed using a variety of common de-noising methods (Wiener filter, anisotropic diffusion filter, gaussian filter, wavelet decomposition, and independent component analyses). The various de-noising schemes were also applied to human arterial spin labeling data to assess the possible extent of structure degradation due to excessive spatial smoothing. The animal experiments and simulated data show that noise reduction methods can suppress both random and systematic errors, improving both the precision and accuracy of cerebral blood flow measurements and the precision of transit time maps. A number of these methods (and particularly independent component analysis) were shown to achieve this aim without compromising image contrast. Magn Reson Med, 2010. © 2010 Wiley-Liss, Inc. [source]