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.

Object oriented control of real-time processing

Abstract: A method of controlling processing in a computer, particularly real-time processing, is performed using computer data objects. Real-time or other input data received from data sources is classified according to pre-stored control data. The control data defines which data source provides the real-time data, how the real-time data is to be processed, where the real-time data is to be stored and what reports the real-time data will be used in. The classified real-time data becomes a computer data object with its associated control data and all subsequent processing is performed on the computer data object.

Multimedia synchronization system

Abstract: A method and system for providing synchronization of the timing of various multimedia events is disclosed. Clock objects are defined in the storage and associated with an internal or external source of current time. The clock objects are able to be displayed on the display, but can be hidden once their linkages are defined. One or more multimedia objects representative of audio, visual or other multimedia events are defined and linked to a particular clock object or clock objects. Then, a processor synchronizes the multimedia objects with the associated clock object or objects. Finally, the various multimedia events are performed in synchronization with their associated clocks.

Sharing objects in runtime systems

Abstract: Methods and apparatus, including computer systems and program products, for sharing data objects in runtime systems. An identification of a first data object in a first runtime system is received. The first data object references zero or more referenced data objects. A shared closure of data objects, which consists of the first data object and a transitive closure of the referenced data objects, is identified, and a determination is made as to whether the shared closure of data objects is usable in a second runtime system. In some implementations, determining whether a shared closure is usable in a second runtime system includes determining whether each data object in the shared closure is serializable without execution of custom code, or determining whether the runtime class of each object instance in the shared closure is shareable. Using shared closures to share objects between runtime systems can provide isolation between user sessions.

Image processing for vehicular applications applying image comparisons

Abstract: Vehicle including a compartment receivable of an object and a system for tracking the object includes at least one imaging device each arranged to receive an image of a portion of the compartment containing the object and a control unit coupled to each imaging device and which controls the imaging device to obtain a first set of images without the object and at least one second image including the object. The control unit analyzes the second image(s) in consideration of the first set of images to derive information about the object. This information may be the type, size and/or position of the object or a part thereof. The information may be used to control vehicular components which have a variable use based on the type, size or position of the object or part thereof.

Non-maximum Suppression for Object Detection by Passing Messages Between Windows

Abstract: Non-maximum suppression (NMS) is a key post-processing step in many computer vision applications. In the context of object detection, it is used to transform a smooth response map that triggers many imprecise object window hypotheses in, ideally, a single bounding-box for each detected object. The most common approach for NMS for object detection is a greedy, locally optimal strategy with several hand-designed components (e.g., thresholds). Such a strategy inherently suffers from several shortcomings, such as the inability to detect nearby objects. In this paper, we try to alleviate these problems and explore a novel formulation of NMS as a well-defined clustering problem. Our method builds on the recent Affinity Propagation Clustering algorithm, which passes messages between data points to identify cluster exemplars. Contrary to the greedy approach, our method is solved globally and its parameters can be automatically learned from training data. In experiments, we show in two contexts – object class and generic object detection – that it provides a promising solution to the shortcomings of the greedy NMS.