An Object model for engineering design

Applications requiring sophisticated modeling techniques raise challenging issues to software designers. CAD/CAM and genetics are example of applications that call for powerful modeling techniques. Existing approaches seem limited in their ability to supports their demands. Relational database systems for example support only simple tables. The need to enhance their capabilities led to non-normalized relational data models. Object-oriented programming languages and databases propose new solutions to the problem of complex and composite object modeling and manipulation. Yet, severe shortcomings impede their practicability, e.g., their inability to model multiple object representations and complex semantic relationships. This paper is an informal overview of a data model called SHOOD implements sophisticated features, such as : o object persistence, multi-methods along a specific specialization hierarchy (which is independent of the class hierarchy), o sophisticated semantic relationships, e.g., dependency relationships between objects (which are totally independent of the composition relationship), o multiple object representations, allowing the users to manipulate the objects from several points of views simultaneously, o the systematic use of a powerful meta-object kernel, which is used to implement a reflexive architecture. The paper focuses on the last two issues

Learning viewpoint invariant perceptual representations from cluttered images

In order to perform object recognition, it is necessary to form perceptual representations that are sufficiently specific to distinguish between objects, but that are also sufficiently flexible to generalize across changes in location, rotation, and scale. A standard method for learning perceptual representations that are invariant to viewpoint is to form temporal associations across image sequences showing object transformations. However, this method requires that individual stimuli be presented in isolation and is therefore unlikely to succeed in real-world applications where multiple objects can co-occur in the visual input. This paper proposes a simple modification to the learning method that can overcome this limitation and results in more robust learning of invariant representations

The contribution of fMRI in the study of visual categorization and expertise

No description supplie

Representational information: a new general notion and measure\ud of information

In what follows, we introduce the notion of representational information (information conveyed by sets of dimensionally defined objects about their superset of origin) as well as an\ud original deterministic mathematical framework for its analysis and measurement. The framework, based in part on categorical invariance theory [Vigo, 2009], unifies three key constructsof universal science – invariance, complexity, and information. From this unification we define the amount of information that a well-defined set of objects R carries about its finite superset of origin S, as the rate of change in the structural complexity of S (as determined by its degree of categorical invariance), whenever the objects in R are removed from the set S. The measure captures deterministically the significant role that context and category structure play in determining the relative quantity and quality of subjective information conveyed by particular objects in multi-object stimuli

Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic