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.

Dimensions of object-based language design

Abstract: The design space of object-based languages is characterized in terms of objects, classes, inheritance, data abstraction, strong typing, concurrency, and persistence. Language classes (paradigms) associated with interesting subsets of these features are identified and language design issues for selected paradigms are examined. Orthogonal dimensions that span the object-oriented design space are related to non-orthogonal features of real languages. The self-referential application of object-oriented methodology to the development of object-based language paradigms is demonstrated.Delegation is defined as a generalization of inheritance and design alternatives such as non-strict, multiple, and abstract inheritance are considered. Actors and prototypes are presented as examples of classless (delegation based) languages. Processes are classified by their degree of internal concurrency. The potential inconsistency of object-oriented sharing and distributed autonomy is discussed, suggesting that compromises between sharing and autonomy will be necessary in designing strongly typed object-oriented distributed database languages.

Estimation of Object Motion Parameters from Noisy Images

Abstract: An approach is presented for the estimation of object motion parameters based on a sequence of noisy images. The problem considered is that of a rigid body undergoing unknown rotational and translational motion. The measurement data consists of a sequence of noisy image coordinates of two or more object correspondence points. By modeling the object dynamics as a function of time, estimates of the model parameters (including motion parameters) can be extracted from the data using recursive and/or batch techniques. This permits a desired degree of smoothing to be achieved through the use of an arbitrarily large number of images. Some assumptions regarding object structure are presently made. Results are presented for a recursive estimation procedure: the case considered here is that of a sequence of one dimensional images of a two dimensional object. Thus, the object moves in one transverse dimension, and in depth, preserving the fundamental ambiguity of the central projection image model (loss of depth information). An iterated extended Kalman filter is used for the recursive solution. Noise levels of 5-10 percent of the object image size are used. Approximate Cramer-Rao lower bounds are derived for the model parameter estimates as a function of object trajectory and noise level. This approach may be of use in situations where it is difficult to resolve large numbers of object match points, but relatively long sequences of images (10 to 20 or more) are available.

Automatic storage of persistent ASN.1 objects in a relational schema

Abstract: A translator for translating objects defined in Abstract Syntax Notation such as ASN.1 to a relational database schema permits persistent storage of object instances as records in a relational database. Object classes are mapped to entity tables with object instances represented by entity records. Simple attributes are mapped to primitive typed attribute columns and package or group attributes are mapped to separate dependent entity tables. Derived attributes are represented by joins of the parent and child entity records.

Statistical methods for research workers

Abstract: The prime object of this book is to put into the hands of research workers, and especially of biologists, the means of applying statistical tests accurately to numerical data accumulated in their own laboratories or available in the literature.

Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image

Abstract: In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.