''What is the difference between a cup and a door?'' These kinds of questions have to be answered in the context of digital libraries. This semantic information, which describes an object on a high, abstract level, is needed in order to provide digital library services such as indexing, markup and retrieval. In this paper we present a new approach to encode and to extract such semantic information. We use generative modeling techniques to describe a class of objects: each class is represented by one algorithm; and each object is one set of high-level parameters, which reproduces the object if passed to the algorithm. Furthermore, the algorithm is annotated with semantic information, i.e. a human-readable description of the object class it represents. We use such an object description to recognize objects in real-world data e.g. laser scans. Using an algorithmic object description, we are able to identify 3D subparts, which can be described and generated by the algorithm. Furthermore, we can determine the needed input parameters. In this way, we can classify objects, recognize them semantically and we can determine their parameters (cup's height, radius, etc.).
[Show abstract][Hide abstract] ABSTRACT: The paper presents new techniques and processing results for automatic
segmentation, shape classification, generic pose estimation, and
model-based identification of naval vessels in laser radar imagery. The
special characteristics of focal plane array laser radar systems such as
multiple reflections and intensity-dependent range measurements are
incorporated into the algorithms. The proposed 3D model matching
technique is probabilistic, based on the range error distribution,
correspondence errors, the detection probability of potentially visible
model points and false alarm errors. The match algorithm is robust
against incomplete and inaccurate models, each model having been
generated semi-automatically from a single range image. A classification
accuracy of about 96% was attained, using a maritime database with over
8000 flash laser radar images of 146 ships at various ranges and
orientations together with a model library of 46 vessels. Applications
include military maritime reconnaissance, coastal surveillance, harbor
security and anti-piracy operations.
Proceedings of SPIE - The International Society for Optical Engineering 05/2012; 8391:9-. DOI:10.1117/12.920264 · 0.20 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: A procedural model is a script, which generates a geometric object. The script's input parameters offer a simple way to specify and modify the scripting output. Due to its algorithmic character, a procedural model is perfectly suited to describe geometric shapes with well-organized structures and repetitive forms.
In this paper, we interpret a generative script as a function, which is nested into an objective function. Thus, the script's parameters can be optimized according to an objective. We demonstrate this approach using architectural examples: each generative script creates a building with several free parameters.The objective function is an energy-efficiency-simulation that approximates a building's annual energy consumption. Consequently, the nested objective function reads a set of building parameters and returns the energy needs for the corresponding building. This nested function is passed to a minimization and optimization process. Outcome is the best building (within the family of buildings described by its script) concerning energy efficiency. Our contribution is a new way of modeling. The generative approach separates design and engineering: the complete design is encoded in a script and the script ensures that all parameter combinations (within a fixed range) generate a valid design. Then the design can be optimized numerically.
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