Object-based and image-based object representations

Abstract

An overview is presented of object-based and image-based representations of objects by their interiors. The representations are distinguished by the manner in which they can be used to answer two fundamental queries in database applications: (1) Feature query: given an object, determine its constituent cells (i.e., their locations in space). (2) Location query: given a cell (i.e., a location in space), determine the identity of the object (or objects) of which it is a member as well as the remaining constituent cells of the object (or objects). Regardless of the representation that is used, the generation of responses to the feature and location queries is facilitated by building an index (i.e., the result of a sort) either on the objects or on their locations in space, and implementing it using an access structure that correlates the objects with the locations. Assuming the presence of an access structure, implicit (i.e., image-based) representations are described that are good for finding the objects associated with a particular location or cell (i.e., the location query), while requiring that all cells be examined when determining the locations associated with a particular object (i.e., the feature query). In contrast, explicit (i.e., object-based) representations are good for the feature query, while requiring that all objects be examined when trying to respond to the location query. The goal is to be able to answer both types of queries with one representation and without possibly having to examine every cell. Representations are presented that achieve this goal by imposing containment hierarchies on either space (i.e., the cells in the space in which the objects are found), or objects. In the former case, space is aggregated into successively larger-sized chunks (i.e., blocks), while in the latter, objects are aggregated into successively larger groups (in terms of the number of objects that they contain). The former is applicable to image-based interior-based representations of which the space pyramid is an example. The latter is applicable to object-based interior-based representations of which the R-tree is an example. The actual mechanics of many of these representations are demonstrated in the VASCO JAVA applets found at

Evaluating the Benefits of Octree-based Indexing for Lidar Data

“…A popular space-based alternative is the two-dimensional (2D) quadtree 115 (Samet, 1995) and its 3D extension, the octree (Samet, 2006). 116…”

“…This is beneficial as 125 empty patches of spaces are not stored within the structure and are thus emitted during analysis. Several 126 variations of the quadtree structure have been developed in the literature (Samet, 2006, p.28) for point 127 data and linear data. 128…”

A novel laparoscopic mesh placement part task trainer

“…Collision detection (5,22) reports where and in what manner two objects touch each other. Collision response takes the output of collision detection as its input to determine any necessary repulsive force as its output.…”

“…Collision detection has been a common topic in research areas such as robotic motion planning, Virtual Reality (VR)-based simulation, computer animation, and computer-aided design (5)(6)(7)22). The task of collision detection in a scene is to locate contacts among all the objects which are represented by geometric models.…”

All-nearest-neighbors finding based on the Hilbert curve

“…In other words, it is difficult to preserve the spatial proximity between the spatial objects. Many spatial data access methods (Samet, 2004) for the ONN query have been proposed to provide access paths to the spatial objects. They focused on candidate neighbors only and prune the objects which are too far from the query object.…”