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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.


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TL;DR: The Neural Physics Engine (NPE) as mentioned in this paper is a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene configurations, and it can be trained via stochastic gradient descent to adapt to specific object properties and dynamics of different worlds.
Abstract: We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene configurations. We propose a factorization of a physical scene into composable object-based representations and a neural network architecture whose compositional structure factorizes object dynamics into pairwise interactions. Like a symbolic physics engine, the NPE is endowed with generic notions of objects and their interactions; realized as a neural network, it can be trained via stochastic gradient descent to adapt to specific object properties and dynamics of different worlds. We evaluate the efficacy of our approach on simple rigid body dynamics in two-dimensional worlds. By comparing to less structured architectures, we show that the NPE's compositional representation of the structure in physical interactions improves its ability to predict movement, generalize across variable object count and different scene configurations, and infer latent properties of objects such as mass.

239 citations

Proceedings ArticleDOI
21 Jul 2017
TL;DR: An Instance Segmentation system that produces a segmentation map where each pixel is assigned an object class and instance identity label, and far more precise segmentations are achieved, as shown by substantial improvements at high APr thresholds.
Abstract: Semantic segmentation and object detection research have recently achieved rapid progress. However, the former task has no notion of different instances of the same object, and the latter operates at a coarse, bounding-box level. We propose an Instance Segmentation system that produces a segmentation map where each pixel is assigned an object class and instance identity label. Most approaches adapt object detectors to produce segments instead of boxes. In contrast, our method is based on an initial semantic segmentation module, which feeds into an instance subnetwork. This subnetwork uses the initial category-level segmentation, along with cues from the output of an object detector, within an end-to-end CRF to predict instances. This part of our model is dynamically instantiated to produce a variable number of instances per image. Our end-to-end approach requires no post-processing and considers the image holistically, instead of processing independent proposals. Therefore, unlike some related work, a pixel cannot belong to multiple instances. Furthermore, far more precise segmentations are achieved, as shown by our substantial improvements at high APr thresholds.

239 citations

Patent
25 Jun 2010
TL;DR: In this paper, the authors proposed a method for predicting properties of a target object by combining multivariate statistical analysis and principal component analysis in combination with content-based image retrieval for providing two-dimensional attributes of three dimensional objects, for example, via preferential image segmentation using a tree of shapes.
Abstract: Method and apparatus for predicting properties of a target object comprise application of a search manager for analyzing parameters of a plurality of databases for a plurality of objects, the databases comprising an electrical, electromagnetic, acoustic spectral database (ESD), a micro-body assemblage database (MAD) and a database of image data whereby the databases store data objects containing identifying features, source information and information on site properties and context including time and frequency varying data. The method comprises application of multivariate statistical analysis and principal component analysis in combination with content-based image retrieval for providing two-dimensional attributes of three dimensional objects, for example, via preferential image segmentation using a tree of shapes and to predict further properties of objects by means of k-means clustering and related methods. By way of example, one of a criminal activity and a fraudulent activity event, an intrusion event and a fire event and residual objects may be predicted and located and qualified such that, for example, properties of the residual objects may be qualified, for example, via black body radiation and micro-body databases including charcoal assemblages.

238 citations

Journal ArticleDOI
TL;DR: All graphical objects and behaviors of those objects are explicitly represented at run time, so the system can provide a number of high level built-in functions, including automatic display and editing of objects, and external analysis and control of interfaces.
Abstract: The Amulet user interface development environment makes it easier for programmers to create highly interactive, graphical user interface software for Unix, Windows and the Macintosh. Amulet uses new models for objects, constraints, animation, input, output, commands, and undo. The object system is a prototype instance model in which there is no distinction between classes and instances or between methods and data. The constraint system allows any value of any object to be computed by arbitrary code and supports multiple constraint solvers. Animations can be attached to existing objects with a single line of code. Input from the user is handled by "interactor" objects which support reuse of behavior objects. The output model provides a declarative definition of the graphics and supports automatic refresh. Command objects encapsulate all of the information needed about operations, including support for various ways to undo them. A key feature of the Amulet design is that all graphical objects and behaviors of those objects are explicitly represented at run time, so the system can provide a number of high level built-in functions, including automatic display and editing of objects, and external analysis and control of interfaces. Amulet integrates these capabilities in a flexible and effective manner.

238 citations

Journal ArticleDOI
TL;DR: An object-level multiversioning approach to consistency maintenance in real-time collaborative graphic editors is proposed, which is novel in achieving intention preservation and convergence, in preserving the work concurrently produced by multiple users in the face of conflict, and in minimizing the number of object versions for conflict resolution.
Abstract: Real-time collaborative graphics editing systems allow a group of users to view and edit the same graphics document at the same time from geographically dispersed sites connected by communication networks Consistency maintenance in the face of concurrent accesses to shared objects is one of the core issues in the design of these types of systems In this article, we propose an object-level multiversioning approach to consistency maintenance in real-time collaborative graphic editors This approach is novel in achieving intention preservation and convergence, in preserving the work concurrently produced by multiple users in the face of conflict, and in minimizing the number of object versions for conflict resolution Major technical contributions of this work include a formal specification of a unique combined effect for an arbitrary group of conflict and compatible operations, a distributed algorithm for incremental creation of multiple object versions, a consistent object identification scheme for multiple object versions, and a convergent layering scheme for overlapping objects All algorithms and schemes presented in this article have been implemented in an Internet-based GRACE (graphics collaborative editing) system

238 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