dc.contributor.advisor | Zucchini, Walter Prof. Dr. | de |
dc.contributor.author | Majer, Peter | de |
dc.date.accessioned | 2013-01-31T08:22:37Z | de |
dc.date.available | 2013-01-31T08:22:37Z | de |
dc.date.issued | 2000-12-13 | de |
dc.identifier.uri | http://hdl.handle.net/11858/00-1735-0000-000D-F281-A | de |
dc.identifier.uri | http://dx.doi.org/10.53846/goediss-3692 | |
dc.format.mimetype | ContentType:text/html Size:3428 | de |
dc.language.iso | eng | de |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ | de |
dc.title | A Statistical Approach to Feature Detection and Scale Selection in Images | de |
dc.type | doctoralThesis | de |
dc.title.translated | Eine Statistische Methode zur Merkmalsextraktion und Skalenselektion in Bildern. | de |
dc.contributor.referee | Zucchini, Walter Prof. Dr. | de |
dc.date.examination | 2000-07-07 | de |
dc.subject.dnb | 28 Informatik, Datenverarbeitung | de |
dc.subject.gok | AHI40 | de |
dc.description.abstracteng | In computer vision "feature detection"
refers to some procedure that determines candidate positions at
which a particular feature, e.g. an edge or a line, could be
located. "Scale selection" aims to find the scale or size of the
feature of interest. Applied together, feature detection and scale
selection allow to determine for example both the position and the
width of linelike structures such as blood vessels in medical
images. A method for automatic scale selection was proposed in 1993
by Lindeberg. This method requires choosing a parameter called the
gamma-parameter. How to choose the gamma-parameter and why to do
scale selection according to Lindebergs proposal are the main
question addressed by this thesis. A statistical approach is
described that defines any "particularly informative parameters" of
an operator response, be they positions, scales or other, in
conceptually the same way. The idea is to evaluate an operator
response relative to the response of the same operator to random
images that contain by construction or definition less structural
information than the observed image. From this point of view
different choices of gamma parameter in Lindebergs method for scale
selection correspond to different distributions of random images
relative to which the operator response is evaluated. The detection
of linelike structures, ridges, is considered in detail and serves
to illustrate the implications of the choice of gamma parameter. It
is demonstrated that a suitable choice of gamma allows a second
derivative of Gaussian operator to detect ridges and "escape"
edges. At fixed scales this detector frequently produces false
responses to edges. At variable scales, however, edges are not
detected if gamma is chosen greater than the critical gamma value
of an edge, gamma=1. Some examples of ridges computed with this
second derivative of Gaussian detector and scale selection are
shown and the algorithms used for the computation are described.
Another contribution of the thesis is concerned with "local
entropies" and a monotonicity property of local entropies in
scale-space. This property captures in a mathematically rigorous
way the intuitive idea that smoothing simplifies images both
globally and, more importantly, also locally. | de |
dc.contributor.coReferee | Lindeberg, Tony Prof. Dr. | de |
dc.subject.topic | Economics and Management Science | de |
dc.subject.ger | computer vision | de |
dc.subject.ger | machine vision | de |
dc.subject.ger | image analysis | de |
dc.subject.ger | image processing | de |
dc.subject.ger | scale-space | de |
dc.subject.ger | scale selection | de |
dc.subject.ger | feature detection | de |
dc.subject.ger | ridge detection | de |
dc.subject.ger | local entropy | de |
dc.subject.bk | 54.74 | de |
dc.identifier.urn | urn:nbn:de:gbv:7-webdoc-916-8 | de |
dc.identifier.purl | webdoc-916 | de |
dc.identifier.ppn | 326778640 | |