Inferring galaxy morphology through texture analysis

This thesis gives an approach to estimate galaxy morphology from digital images. In particular, our algorithm extracts textural information from galaxy images at difference scales. The multiscale information is then merged into a unified representation. By fitting a morphological model based on the textural information, I derive a quantitative and physically meaningful description of galaxy morphology. Such description will help scientists to study how galaxy morphology evolve.