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Author Pakzad, K. ♦ Heller, J.
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Scale Space ♦ Automatic Approach ♦ Scale Behaviour ♦ Different Object Type ♦ Digital Image ♦ Semantic Net ♦ Object Shape ♦ Paper First Result ♦ Scale Change ♦ Generalisation Process Scale Change Model ♦ Semantic Net Representation ♦ Automatic Scale Adaptation ♦ Line-type Object ♦ Scale Space Event ♦ Automatic Object Extraction ♦ Natural Language ♦ Object Description ♦ Line-extraction Operator ♦ Different Type ♦ High Resolution Image ♦ Described Methodology ♦ Particular Object Event ♦ High Resolution ♦ Object Model ♦ Object Part ♦ Low Resolution Image ♦ Landscape Object
Description This paper deals with a methodology to derive object models for automatic object extraction in low resolution images from models created manually for high resolution images. The object models are represented by semantic nets, which describe landscape objects explicitly in terms of natural language. Starting from semantic nets for high resolution images the strategy is to first decompose them into parts, which can be handled autonomously. The object parts are then adapted, i.e. generalised, to smaller scale. The adaptation takes into account the object shape, radiometry, and texture. For the generalisation process “scale change models ” are used, which describe how different types of objects evolve over scale mathematically. Finally, all object parts are fused and transferred to a semantic net representation. In this paper first results of the described methodology are presented. Focussing on line-type objects, such as streets, we describe how to create an object description with semantic nets using constraints, which have to be satisfied, in order to be able to adapt the nets to other scales automatically. In addition we show tests of the behaviour of some edge- and line-extraction operators through scale space. These tests are necessary to predict the scale behaviour of different object types. At last, we describe as an example for a particular object events during scale change observed in an image and their impact on a semantic net. This example demonstrates the suitability of the proposed kind of semantic net to follow the scale space events in digital images, and thus, its applicability in an automatic approach. 1.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2004-01-01