Browsing by Subject "Texture Synthesis"

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Open Access
Generalization and localization based style imitation for grayscale images
(Springer, Berlin, Heidelberg, 2003) Nar, F.; Çetin, Atılım
An example based rendering (EBR) method based on generalization and localization that uses artificial neural networks (ANN) and k-Nearest Neighbor (k-NN) is proposed. The method involves learning phase and application phase, which means that once a transformation filter is learned, it can be applied to any other image. In learning phase, error back-propagation learning algorithm is used to learn general transformation filter using unfiltered source image and filtered output image. ANNs are usually unable to learn filter-generated textures and brush strokes hence these localized features are stored in a feature instance table for using with k-NN during application phase. In application phase, for any given grayscale image, first ANN is applied then k-NN search is used to retrieve local features from feature instances considering texture continuity to produce desired image. Proposed method is applied up to 40 image filters that are collection of computer-generated and human-generated effects/styles. Good results are obtained when image is composed of localized texture/style features that are only dependent to intensity values of pixel itself and its neighbors.
Open Access
Surface reflectance estimation from spatio-temporal subband statistics of moving object videos
(2012) Külçe, Onur
Image motion can convey a broad range of object properties including 3D structure (structure from motion, SfM), animacy (biological motion), and its material. Our understanding of how the visual system may estimate complex properties such as surface reflectance or object rigidity from image motion is still limited. In order to reveal the neural mechanisms underlying surface material understanding, a natural point to begin with is to study the output of filters that mimic response properties of low level visual neurons to different classes of moving textures, such as patches of shiny and matte surfaces. To this end we designed spatio-temporal bandpass filters whose frequency response is the second order derivative of the Gaussian function. Those filters are generated towards eight orientations in three scales in the frequency domain. We computed responses of these filters to dynamic specular and matte textures. Specifically, we assessed the statistics of the resultant filter output histograms and calculated the mean, standard deviation, skewness and kurtosis of those histograms. We found that there were substantial differences in standard deviation and skewness of specular and matte texture subband histograms. To formally test whether these simple measurements can in fact predict surface material from image motion we developed a computer-assisted classifier based on these statistics. The results of the classification showed that, 75% of all movies are classified correctly, where the correct classification rate of shiny object movies is around 77% and the correct classification rate of matte object movies is around 71%. Next, we synthesized dynamic textures which resembled the subband statistics of videos of moving shiny and matte objects. Interestingly the appearance of these synthesized textures were neither shiny nor matte. Taken together our results indicate that there are differences in the spatio-temporal subband statistics of image motion generated by rotating matte and specular objects. While these differences may be utilized by the human brain during the perceptual process, our results on the synthesized textures suggest that the statistics may not be sufficient to judge the material qualities of an object.