Generalization and localization based style imitation for grayscale images
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol.2869, pp.465-473, 2003 (Scopus)
- Publication Type: Article / Article
- Volume: 2869
- Publication Date: 2003
- Doi Number: 10.1007/978-3-540-39737-3_58
- Journal Name: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Journal Indexes: Scopus
- Page Numbers: pp.465-473
- Ankara Yıldırım Beyazıt University Affiliated: Yes
Abstract
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 filtergenerated 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. © Springer-Verlag Berlin Heidelberg 2003.