Spatial point processes provide a statistical framework for modeling random arrangements of objects, which is of relevance in a variety of scientific disciplines, including ecology, spatial epidemiology and material science. Describing systematic spatial variations within this framework and developing methods for estimating parameters from empirical data constitute an active area of research. Image analysis, in particular, provides a range of scenarios to which point process models are applicable. Typical examples are images of trees in remote sensing, cells in biology, or composite structures in material science. Due to its real-world orientation and versatility, the class of the recently developed locally scaled point processes appears particularly suitable for the modeling of spatial object patterns. An unknown normalizing constant in the likelihood, however, makes inference complicated and requires elaborate techniques. This work presents an efficient Bayesian inference concept for locally scaled point processes. The suggested optimization procedure is applied to images of cross-sections through the stems of maize plants, where the goal is to accurately describe and classify different genotypes based on the spatial arrangement of their vascular bundles. A further spatial point process framework is specifically provided for the estimation of shape from texture. Texture learning and the estimation of surface orientation are two important tasks in pattern analysis and computer vision. Given the image of a scene in three-dimensional space, a frequent goal is to derive global geometrical knowledge, e.g. information on
camera positioning and angle, from the local textural characteristics in the image. The statistical framework proposed comprises locally scaled point process strategies as well as the draft of a Bayesian marked point process model for inferring shape from texture
