A model-based method for rotation invariant texture classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Approaches to Image Understanding
ACM Computing Surveys (CSUR)
Time series: data analysis and theory
Time series: data analysis and theory
Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Shape from a Shaded and Textural Surface Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Textured Images Using Generalized Long Correlation Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Synthesis of Color Textures for Multimedia Applications
Multimedia Tools and Applications
Texture Roughness Analysis and Synthesis via Extended Self-Similar (ESS) Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Long correlation Gaussian random fields: Parameter estimation and noise reduction
Digital Signal Processing
| Hi-index | 0.14 |
The problem of detecting texture boundaries without assuming any knowledge on the number of regions or the types of textures is considered. Texture boundaries are often regarded as better features than intensity edges, because a large class of images can be considered a composite of several different texture regions. An algorithm is developed that detects texture boundaries at reasonably high resolution without assuming any prior knowledge on the texture composition of the image. The algorithm utilizes the long correlation texture model with a small number of parameters to characterize textures. The parameters of the model are estimated by a least-squares method in the frequency domain. The existence and the location of texture boundary is estimated by the maximum-likelihood method. The algorithm is applied to several different images, and its performance is shown by examples. Experimental results show that the algorithm successfully detects texture boundaries without knowing the number of types of textures in the image.