  The paper presents a new model for single channel images low-level
interpretation. The image is decomposed into a graph which captures a complete
set of structural features. The description allows to accurately identify every
edge location and its correct connectivity. The key features of the method are:
vector description of the edges, subpixel precision, and parallelism of the
underlying algorithm. The methodology outperforms classical and state of the
art edge detectors at both conceptual and experimental levels. It also enables
graph based algorithms for higher-level feature extraction. Any image
processing pipeline can benefit from such results: e.g., controlled denoising,
edge preserving filtering, upsampling, compression, vector and graph based
pattern matching, neural network training.
