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Traditional theories of vision assume that object segregation occurs before access to object memories. Yet, behavioral evidence shows that familiar configuration is a prior for segregation, and electrophysiological experiments demonstrate these memories are accessed rapidly. A behavioral index of the speed of access is lacking, however. Here we asked how quickly behavior is influenced by object memories that are accessed in the course of object segregation. We investigated whether access to object memories on the groundside of a border can slow behavior during a rapid categorization task. Participants viewed two silhouettes that depicted a real-world and a novel object. Their task was to saccade toward the real-world object as quickly as possible. Half of the nontarget novel objects were ambiguous in that a portion of a real-world object was suggested, but not consciously perceived, on the groundside of their borders. The rest of the nontargets were unambiguous. We tested whether saccadic reaction times were perturbed by the real-world objects suggested on the groundside of ambiguous novel silhouettes. In Experiments 1 and 2, saccadic reaction times were slowed when nontargets were ambiguous rather than unambiguous. Experiment 2 set an upper limit of 190 ms on the time required for object memories in grounds to influence behavior. Experiment 3 ruled out factors that could have produced longer latencies other than access to object memories. These results provide the first behavioral index of how quickly memories of objects suggested in grounds can influence behavior, placing the upper limit at 190 ms.

Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in the presence of sensor error are studied. Models and scene data are represented as local geometric features and robust hypothesis of feature matchings and transformations is considered. Bounds on the error in the image feature geometry are assumed constraining possible matchings and transformations. Transformation sampling is introduced as a simple, robust, polynomial-time, and highly parallel method of searching the space of transformations to hypothesize feature matchings. Key to the approach is that error in image feature measurement is explicitly accounted for. A Connection Machine implementation and experiments on real images are presented.

We present a component-based approach for recognizing objects under large pose changes. From a set of training images of a given object we extract a large number of components which are clustered based on the similarity of their image features and their locations within the object image. The cluster centers build an initial set of component templates from which we select a subset for the final recognizer. In experiments we evaluate different sizes and types of components and three standard techniques for component selection. The component classifiers are finally compared to global classifiers on a database of four objects.

Thesis (M. Sc(Computer Science))--University of Pretoria, 2009. / Includes bibliographical references (leaves [180]-191).

SUMMARY Thesis presents object-relational mismatch research as actual problem in the information technology systems targeted for business domain applications. Programmers are working with objects, whereas they use relational databases to make the data persistent. This leads for the need to unite the two theories – object and relational. The aim of the thesis is to overview object-relational mismatch. Advantages and disadvantages are overviewed and the solutions are given. Requirements for the state-of-the-art object-relational mapping layer will be presented. Those object-relational mapping layers are used in current business processes based system. Practical aim of the thesis is to propose object-relational mapping framework for real client-server system, based on current the most popular object-relational mapping layers in the market. In the practical part of the thesis feature based analysis of selected object-relational mapping frameworks is being performed. Those object relational mapping layers are being used for tests on real insurance industry based software system. Theoretical propositions and the results discovered are illustrated with 16 pictures and 6 tables. The thesis is based on 15 different literature sources in the Lithuanian and English languages. Two appendices are given and the end of the work.

As some new computer applications, such as computer-aided design, multimedia systems, and knowledge-based systems, require more complex data structures, the traditional database system seems to be unable to support these new requirements. A recently developed database technology, object-oriented database, provides a solution to these problems.The purpose of this thesis is to investigate what is object-oriented database, especially in its internal organization such as object persistency. Two object-oriented database systems, EXODUS and ODE, are discussed in detail. In addition, a comparison between the relational databases and object-oriented databases is made. / Department of Computer Science

Report (M.I.S.)--Virginia Polytechnic Institute and State University, 1993. / Includes bibliographical references (leaf 88). Also available via the Internet.

We present a component-based approach for recognizing objectsunder large pose changes. From a set of training images of a givenobject we extract a large number of components which are clusteredbased on the similarity of their image features and their locations withinthe object image. The cluster centers build an initial set of componenttemplates from which we select a subset for the final recognizer.In experiments we evaluate different sizes and types of components andthree standard techniques for component selection. The component classifiersare finally compared to global classifiers on a database of fourobjects.

Numerous theories have been developed in explanation of object perception, such as Feature Integration Theory, which posits that an object is perceived after two stages: a pre-attentive stage and a focused attention stage. It is during the focused attention stage that a representation of the perceived object is formed. Theories such as object file theory account for the maintenance of these object representations following their creation. Evidence for object file theory has been provided by studies of the object specific preview benefit. This thesis seeks to examine the effect that dividing attention has on the maintenance of object representations. Using the tenets of object file theory and the cortical field hypothesis for dual task interference, it is hypothesized that by presenting participants with two simultaneous tasks which make use of overlapping cortical areas the object representation initially formed will be lost resulting in the loss of the object specific preview benefit. Whereas presenting participants with two simultaneous tasks which are associated with spatially separate, or non-overlapping, cortical regions will not result in the loss of the object specific preview benefit.