Maurizio Tucci

Automating visual language generation

A system to generate and interpret customized visual languages in given application areas is presented. The generation is highly automated. The user presents a set of sample visual sentences to the generator. The generator uses inference grammar techniques to produce a grammar that generalizes the initial set of sample sentences, and exploits general semantic information about the application area to determine the meaning of the visual sentences in the inferred language. The interpreter is modeled on an attribute grammar. A knowledge base, constructed during the generation of the system, is then consulted to construct the meaning of the visual sentence. The architecture of the system and its use in the application environment of visual text editing (inspired by the Heidelberg icon set) enhanced with file management features are reported. >

Symbol-Relation Grammars

A common approach to the formal description of pictorial and visual languages makes use of formal grammars and rewriting mechanisms. The present paper is concerned with the formalism of Symbol?Relation Grammars (SR grammars, for short). Each sentence in an SR language is composed of a set of symbol occurrences representing visual elementary objects, which are related through a set of binary relational items. The main feature of SR grammars is the uniform way they use context-free productions to rewrite symbol occurrences as well as relation items. The clearness and uniformity of the derivation process for SR grammars allow the extension of well-established techniques of syntactic and semantic analysis to the case of SR grammars. The paper provides an accurate analysis of the derivation mechanism and the expressive power of the SR formalism. This is necessary to fully exploit the capabilities of the model. The most meaningful features of SR grammars as well as their generative power are compared with those of well-known graph grammar families. In spite of their structural simplicity, variations of SR grammars have a generative power comparable with that of expressive classes of graph grammars, such as the edNCE and the N-edNCE classes.

FIRE: fractal indexing with robust extensions for image databases

As already documented in the literature, fractal image encoding is a family of techniques that achieves a good compromise between compression and perceived quality by exploiting the self-similarities present in an image. Furthermore, because of its compactness and stability, the fractal approach can be used to produce a unique signature, thus obtaining a practical image indexing system. Since fractal-based indexing systems are able to deal with the images in compressed form, they are suitable for use with large databases. We propose a system called FIRE, which is then proven to be invariant under three classes of pixel intensity transformations and under geometrical isometries such as rotations by multiples of /spl pi//2 and reflections. This property makes the system robust with respect to a large class of image transformations that can happen in practical applications: the images can be retrieved even in the presence of illumination and/or color alterations. Additionally, the experimental results show the effectiveness of FIRE in terms of both compression and retrieval accuracy.

A methodology and interactive environment for iconic language design

We describe a design methodology for iconic languages based upon the theory of icon algebra to derive the meaning of iconic sentences. The design methodology serves two purposes. First of all, it is a descriptive model for the design process of the iconic languages used in the Minspeak? systems for augmentative communication. Second, it is also a prescriptive model for the design of other iconic languages for human-machine interface. An interactive design environment based upon this methodology is described. This investigation raises a number of interesting issues regarding iconic languages and iconic communications.

A predictive parser for visual languages specified by relation grammars

We define a class of relation grammars that satisfy the context-freeness property, which is an essential condition to solve the membership problem in polynomial time. The context-freeness property is used to design a predictive parsing algorithm for such grammars. The algorithm has a polynomial time behaviour when applied to grammars which generate languages having the additional properties of connections and degree-boundedness. One remarkable result is that a polynomial time complexity is obtained without imposing (total or partial) ordering on the symbols of input sentences.<<ETX>>

Relation grammars and their application to multi-dimensional languages

Relation grammars are introduced as a powerful formalism for specifying the syntax of visual languages and, more generally, of multi-dimensional languages. Textual languages use only the implicit relation of sequential concatenation of symbols. The proposed extension relax this limitation and allows the introduction of any number of relations. By analogy with textual grammars, relation grammars make it easier to recognize the purpose of the lexical analysis phase and that of the syntactic one for parsing multi-dimensional structures.

Virtual images for similarity retrieval in image databases

We introduce the virtual image, an iconic index suited for pictorial information access in a pictorial database, and a similarity retrieval approach based on virtual images to perform content-based retrieval. A virtual image represents the spatial information contained in a real image in explicit form by means of a set of spatial relations. This is useful to efficiently compute the similarity between a query and an image in the database. We also show that virtual images support real-world applications that require translation, reflection, and/or rotation invariance of image representation.

Efficient parsing of multidimensional structures

Visual languages have motivated growing interests in the investigation of grammatical formalisms and parsing algorithms for modelling and recognizing multidimensional structures. The effectiveness of visual languages require that some efforts must be accomplished to obtain efficient parsing techniques. A general parsing scheme for relation grammars is presented. The class RG/1 of grammars is characterized which seems to be well suited for modelling visual languages of practical use. An efficient O(n log n) parsing algorithm is also given.<<ETX>>

IME: an image management environment with content-based access

Abstract The article describes an experimental visual environment to handle digital images by contents. A suitable spatial index is used to organize the images in a spatial access structure for efficient storage and retrieval. An image is indexed according to both the spatial arrangement of its objects and the morphological and geometrical measures of these objects. Therefore, in the database population phase a user identifies the objects that characterize the visual content of each image by a user-friendly interface. In order to let the system retrieve images based on the presence of given patterns, it is necessary to define similarity matching criteria between a query and an image. To efficiently perform such a match, each image is stored together with a collection of metadata that are a very compact representation of the visual contents of the image. These metadata form the index of the image. The system implements a Spatial Access Method based on k-d-trees to achieve a significant speedup over sequential search. We prove the effectiveness and the efficiency of the system by performing standard tests on a database containing a large number of medical images, namely lung CT scans.

Parsing nonlinear languages

The diagrammatic approach to user interfaces for computer-aided software development toolkits, visual query systems, and visual programming environments, is based on the use of diagrams and charts traditionally drawn on paper. In particular, the VLG system (Visual Language Generator) has been proposed to generate icon-oriented visual languages customized for given applications. The syntactical model underlying the interpretation of a visual language in VLG has been designed to describe icon-oriented visual languages. In order to enable the VLG system to apply to any kind of graphical languages, like diagrammatic ones, it is necessary to find a more general syntactical model able to support both their generation and interpretation. This paper addresses the comprehension of the features that a grammatical formalism for nonlinear languages must have to match any requirement for an efficient parsing. To this aim, relation grammars support an easy implementation of a general parsing algorithm for multidimensional languages, parametric with respect to the rewriting rules of the grammar. We compare the expressive power of relation grammars to grammatical formalisms for graph grammars. >