Visual calculating in shape grammars aligns with art and design, bridging the gap between seeing (Coleridge's “imagination”) and combinatoric play (Coleridge's “fancy”). In Shapes of Imagination, George Stiny runs visual calculating in shape grammars through art and design—incorporating Samuel Taylor Coleridge's poetic imagination and Oscar Wilde's corollary to see things as they aren't. Many assume that calculating limits art and design to suit computers, but shape grammars rely on seeing to prove otherwise. Rules that change what they see extend calculating to overtake what computers can do, in logic and with data and learning. Shape grammars bridge the divide between seeing (Coleridge's “imagination, or esemplastic power”) and combinatoric play (Coleridge's “fancy”). Stiny shows that calculating without seeing excludes art and design. Seeing is key for calculating to augment creative activity with aesthetic insight and value. Shape grammars go by appearances, in a full-fledged aesthetic enterprise for the inconstant eye; they answer the question of what calculating would be like if Turing and von Neumann were artists instead of logicians. Art and design are calculating in all their splendid detail.

How design is calculating with shapes: formal details and design applications.

A Stochastic Grammar of Images is the first book to provide a foundational review and perspective of grammatical approaches to computer vision. In its quest for a stochastic and context sensitive grammar of images, it is intended to serve as a unified frame-work of representation, learning, and recognition for a large number of object categories. It starts out by addressing the historic trends in the area and overviewing the main concepts: such as the and-or graph, the parse graph, the dictionary and goes on to learning issues, semantic gaps between symbols and pixels, dataset for learning and algorithms. The proposal grammar presented integrates three prominent representations in the literature: stochastic grammars for composition, Markov (or graphical) models for contexts, and sparse coding with primitives (wavelets). It also combines the structure-based and appearance based methods in the vision literature. At the end of the review, three case studies are presented to illustrate the proposed grammar. A Stochastic Grammar of Images is an important contribution to the literature on structured statistical models in computer vision.