Functionality of an object defines its applicability in a task. In this paper we introduce a representation for functionality and present a methodology for its recovery. In addition, we introduce force-shape maps as means to integrate and classify the recovered functionality. Since functionality describes an interaction, the representation for an object must include not only its intrinsic (material) but also its functional (how it is used) properties. In order to recover the functionality of an object, we have developed a formalism based on discrete event system theory and on the paradigm of Active Perception. This formalism allows us to express an investigating task in the form of a finite automaton for controlling and observing the interactions in our task. Our current investigation focuses on manipulatory interactions, and thus far, the functionality of piercing. We envision an expandable system having at its disposal a score of investigative procedures and through them to...

Functionality-based object recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function of the category. Such systems naturally associate a "measure of goodness" or "membership value" with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value for a primitive evaluation of a particular physical property of an object. In previous versions of a recognition system known as GRUFF, the membership function for each of the primitive evaluations had to be hand-crafted by the system designer. In this paper, we provide a learning component for the GRUFF system, called Omlet, that automatically learns primitive evaluation membership functions given a set of example objects labeled with their desired category measure. T...

Representation systems which support \generic " object recognition o er promising advantages over current model-based vision. Systems applying function-based reasoning are one such approach. In this approach, speci c geometric or structural models are disregarded, in favor of analyzing the shape to determine functional requirements for category membership. This paper presents an explanation of the ideas behind function based modeling and a description of the extensions made to create the Generic Representation Using Form and Function-3 (GRUFF-3) system. This system analyzes the 3-D shape of an object and classi es the object according to its possible function as some (sub)category of the superordinate category dishes. The initial GRUFF system implementation was restricted to the furniture domain and required ve knowledge primitives (clearance, relative orientation, proximity, dimensions and stability) to realize the functional requirements of the categories represented. The important contribution of our current work is that a signi cantly larger domain of objects can now be recognized with the addition of just one new knowledge primitive, enclosure. Anevaluation of the performance of the system is presented for a database of over 200 3-D shapes.

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Tool use is an important characteristic of intelligent human behavior. Rep-resenting, classifying and recognizing tools by their functionality can provide us new opportunities for understanding and eventually improving an agent's interac-tion with the physical world. Techniques have been developed in a wide range of areas within artificial intelligence and other disciplines to represent and automatically reason about the functionality of tools. This article surveys past approaches to reasoning about functionality in the literature and attempts to give an overviewof the strengths and weaknesses of previous techniques. A number of issues that needs to be addressed are also reviewed.

Abstract — We propose a novel scheme for fusion between two types of modalities to support function-based classification. While the first modality targets functional classification from sounds registered at impact, the second one aims classification of objects in 3D images. Using audio one can answer functional questions such as what is the material the analyzed objects are built of, if the objects are full or hollow, if they are heavy, and if they are rigidly linked to their supports. Audio based signatures are used to label parts of the object under analysis. Different parts of any object can be partitioned in generic multi-level hierarchical descriptions of functional components. Functionality, in the visual modality reasoning scheme, is derived from a large set of geometric attributes and relationships between object parts. These geometric properties represent labeling signatures to the primitive and functional parts of the analyzed classes. The fusion between both of the modalities relies on a shared cooperation among audio and visual signatures of the functional and primitive parts. The scheme does not require a-priori knowledge about any class. We tested the proposed scheme on a database of about one thousand different 3D objects. The results show high accuracy in classification. I.

In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in which classes are verified using a functional class graph in which functional parts and their realization hypotheses are explored. The validation tree is efficiently searched. Some functional requirements are validated in a final procedure for more efficient separation of objects from non-objects. The search employs a knowledge repository mechanism that monotonically adds knowledge during the search and speeds up the classification process. Finally, we describe our implementation and present results of experiments on a database that comprises about one-hundred-and-fifty real raw range images of object instances from ten classes. 1

Identification of Functional Features through Observations and Interactions Luca Bogoni Supervised by Ruzena Bajcsy Functionality of an object defines its purpose in an environment. This thesis introduces a representation for functionality and presents a methodology for the recovery of functional features. The representation for a object must include not only its intrinsic (material) properties but also its functional (how it is used) properties and is defined in terms of control and observation strategies. In order to recover the functional properties of an object, we have developed a formalism based on Discrete Event System Theory and on the paradigm of Active Perception. This formalism expresses an investigating task in the form of a finite automaton for controlling and observing interactions. The system developed here employs interactive and observational procedures for the extraction of functional features. The experiment performed involve manipulatory interactions for the investi...

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