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Object (computer science)

About: Object (computer science) is a research topic. Over the lifetime, 106024 publications have been published within this topic receiving 1360115 citations. The topic is also known as: obj & Rq.


Papers
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ReportDOI
01 Feb 1992
TL;DR: It is proved that for any bilaterally symmetric 3D object one non- accidental 2D model view is sufficient for recognition and linear transformations can be learned exactly from a small set of examples in the case of "linear object classes".
Abstract: In this note we discuss how recognition can be achieved from a single 2D model view exploiting prior knowledge of an object''s structure (e.g. symmetry). We prove that for any bilaterally symmetric 3D object one non- accidental 2D model view is sufficient for recognition. Symmetries of higher order allow the recovery of structure from one 2D view. Linear transformations can be learned exactly from a small set of examples in the case of "linear object classes" and used to produce new views of an object from a single view.

192 citations

Patent
17 Jul 1995
TL;DR: An authoring environment for producing content for an on-line system is described in this paper, which includes a story editor which can save files in a Multimedia Document Format (MDF) file.
Abstract: An authoring environment for producing content for an on-line system is described. This environment includes a story editor which can save files in a Multimedia Document Format (MDF) file. A MDF file is an OLE storage wherein one storage object holds text of the content in a Multimedia Publishing Markup Language. Other parts of the MDF file include storages for holding content search terms and storages for embedded objects.

192 citations

Patent
01 Mar 1996
TL;DR: In this paper, a distributed debugger system for a distributed target computer application is described, where the programmer/developer of the application can be at one host machine and the application being developed makes use of objects and object implementations which may be located on a different host machine which is unknown to the programmer or developer.
Abstract: A system and method for providing a distributed debugger system for a distributed target computer application are disclosed wherein the programmer/developer of the application can be at one host machine and wherein the application being developed makes use of objects and object implementations which may be located on a different host machine which is unknown to the programmer/developer. The system and method provides solutions to problems which are encountered in trying to debug a new application which is associated with the use of objects in a widely distributed, object oriented, client-server system. In a distributed object environment, requests and replies are made through an Object Request Broker (ORB) that is aware of the locations and status of objects. One architecture which is suitable for implementing such an ORB is provided by the Common Object Request Broker Architecture (CORBA) specification. The distributed debugger system disclosed herein is designed to function in a CORBA compliant distributed system.

191 citations

Posted Content
TL;DR: The proposed decision tree is a decision tree, which clarifies the specific reason for each prediction made by the CNN at the semantic level, and organizes all potential decision modes in a coarse-to-fine manner to explain CNN predictions at different fine-grained levels.
Abstract: This paper aims to quantitatively explain rationales of each prediction that is made by a pre-trained convolutional neural network (CNN) We propose to learn a decision tree, which clarifies the specific reason for each prediction made by the CNN at the semantic level Ie, the decision tree decomposes feature representations in high conv-layers of the CNN into elementary concepts of object parts In this way, the decision tree tells people which object parts activate which filters for the prediction and how much they contribute to the prediction score Such semantic and quantitative explanations for CNN predictions have specific values beyond the traditional pixel-level analysis of CNNs More specifically, our method mines all potential decision modes of the CNN, where each mode represents a common case of how the CNN uses object parts for prediction The decision tree organizes all potential decision modes in a coarse-to-fine manner to explain CNN predictions at different fine-grained levels Experiments have demonstrated the effectiveness of the proposed method

191 citations

Patent
17 Aug 2007
TL;DR: In this paper, a haptic interface device is used to manipulate a virtual tool in a virtual environment to interact and modify the virtual object in a sculpting mode. And the user can set geometric constraints such as a constraint point, constraint line, or constraint surface to limit or guide the movement of the virtual tool.
Abstract: A user of a virtual object or computer model uses a haptic interface device in the real world to manipulate a virtual tool in a virtual environment to interact and modify the virtual object. The user uses the virtual tool in a sculpting mode to modify the shape of the virtual object by adding, removing, or modifying the material of the object. The user feels an interaction force from the virtual tool as it interacts with and modifies the virtual object. The designer can set geometric constraints, such as a constraint point, constraint line, or constraint surface, to limit or guide the movement of the virtual tool.

191 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202238
20213,087
20205,900
20196,540
20185,940
20175,046