Bridging the gap between the Wold random process decomposition theory and practical texture modeling, this research establishes Wold-based texture modeling as an important method for a wide range of applications that benefit from efficient and effective characterization of textural information.

• A robust and efficient algorithm is developed for spectral 2-D Wold decomposition of homogeneous or near homogeneous random fields.
• A psychophysical study is conducted to show that the Wold component energy of a texture pattern is a good computational measure for the most salient human texture perception dimension of repetitiveness vs. randomness.
• A shift, rotation, and scale invariant Wold-based texture model is presented. This model provides efficient and perceptually sensible features that are robust to many natural texture inhomogeneities.
• For model perspective invariance, a linear system characterization and a decomposition of image perspective transformations are proposed to form a basis for future algorithms to infer image perspective parameters from a single sample of harmonic texture data.
• Based on the Wold texture model, an algorithm is developed for textured image database retrieval. Compared to the state-of-the-art texture models, the new model appears to offer perceptually more satisfying retrieval results while matching or surpassing the best recognition performance of the others.
• A K-means-based image segmentation method is presented to demonstrate the use of Wold-based modeling in characterizing textured regions in natural scene images.
• Applying the principle of Wold decomposition to temporal texture modeling, a robust and efficient algorithm is developed for detecting and segmenting periodic motion. The use of periodicity templates is proposed for characterizing periodicity in space and time.