The paper introduces hierarchical image transformations that can be used for detecting various image features of gradually increased complexity. The major prospective application of the method is in (semi-) autonomous vision-guided robotic systems and, therefore, the local operators that can be prospectively hardware-implemented are the core component of the proposed algorithms. A feature map is a grey-level digital image with a vector attached to each pixel. Pixel intensities represent "the feature intensity", i.e. the estimated confidence that a feature of interest is located at the pixel. The vector components are characterizing the feature configuration. The low-level "intensity map" is the original grey-level image with the "feature intensity" being just the brightness value. A transformation from the current feature map to the map of a higher level is obtained by applying a local operator (with a circular scanning window). For each location of the window, the operator determines the template instance of a higher-level feature prospectively existing at this location. Then, the template is matched to the actual content of the window and - based on their similarity - the feature intensity value for the higher-level map pixel is determine. The associated vectors are containing the configuration parameters of the templates extracted by the operator. The paper contains the theoretical foundations of the proposed method, but exemplary results illustrating the method?s principles are also provided.