Representation (systemics)

About: Representation (systemics) is a research topic. Over the lifetime, 33821 publications have been published within this topic receiving 475461 citations.

Generating a Three-Dimensional Model Using a Portable Electronic Device Recording

Abstract: Systems and methods are provided for navigating a three-dimensional model using deterministic movement of an electronic device. An electronic device can load and provide an initial display of a three dimensional model (e.g., of an environment or of an object). As the user moves the electronic device, motion sensing components can detect the device movement and adjust the displayed portion of the three-dimensional model to reflect the movement of the device. By walking with the device in the user's real environment, a user can virtually navigate a representation of a three-dimensional environment. In some embodiments, a user can record an object or environment using an electronic device, and tag the recorded images or video with movement information describing the movement of the device during the recording. The recorded information can then be processed with the movement information to generate a three-dimensional model of the recorded environment or object.

Learning Relationships for Multi-View 3D Object Recognition

Abstract: Recognizing 3D object has attracted plenty of attention recently, and view-based methods have achieved best results until now. However, previous view-based methods ignore the region-to-region and view-to-view relationships between different view images, which are crucial for multi-view 3D object representation. To tackle this problem, we propose a Relation Network to effectively connect corresponding regions from different viewpoints, and therefore reinforce the information of individual view image. In addition, the Relation Network exploits the inter-relationships over a group of views, and integrates those views to obtain a discriminative 3D object representation. Systematic experiments conducted on ModelNet dataset demonstrate the effectiveness of our proposed methods for both 3D object recognition and retrieval tasks.

Knowledge Representation: An AI Perspective

Abstract: Most researchers to date in artificial intelligence has been based on the knowledge representation hypothesis, that is, the assumption that in any artificial intelligence (AI) programme there is a separate module which represents the information that the programme has about the world. As a result, a number of so-called knowlege representation formalisms have been developed for representing this kind of information in a computer. This text discusses the most popular knowledge representation languages - logic, production rules, semantics (networked and frames), and also provides a short introduction to AI systems that combine various knowledge representation languages. The knowlege representation hypothesis has been challenged by the re-emergence of a new style of computing, variously called parallel distributed processing, connectionism, or neural networks. These approaches are discussed in a separate chapter, and the arguments in favour of and against parallel distributed processing are reviewed.

The appeal of parallel distributed processing

Abstract: This chapter contains sections titled: Parallel Distributed Processing, Examples Of PDP Models, Representation and Learning in PDP Models, Origins of Parallel Distributed Processing, Acknowledgments