A natural vision inspired approach to line and curve pattern analysis

Show simple item record

dc.contributor.advisor Coghill, G. en
dc.contributor.advisor Sivaswamy, J. en
dc.contributor.author Chan, Woei en
dc.date.accessioned 2020-06-02T04:32:22Z en
dc.date.available 2020-06-02T04:32:22Z en
dc.date.issued 2000 en
dc.identifier.uri http://hdl.handle.net/2292/51031 en
dc.description Full text is available to authenticated members of The University of Auckland only. en
dc.description.abstract It is demonstrated that the Local Energy at each image location is rich in information where a variety of new and useful pattern analysis algorithms may be applied. In particular, several different types of line features may be detected and extracted by analysing the energy information in various orientation and frequency channels. The different types of line features are building blocks to form more complex patterns giving rise to a particular texture, such as text. Local energy was first proposed (Morrone and Burr 1988) as a generalised model for feature detection in human vision and has been shown to work well in texture analysis. This thesis focuses on developing biologically plausible algorithms of various pre-attentive feature detection tasks such as straight-line and curved-line feature discrimination and curvature discrimination. In biological vision, the term pre-attentive is used to describe visual tasks that may be performed without the need to engage the attention mechanism, or high level processing. The straight-line and curved-line feature discrimination algorithm involves filtering the input image with a set of directional filters covering the entire image space and computing the filter response energy. Features are extracted by analysing the energy information in various orientation channels where a straight-line will generate energy in a single orientation channel. In contrast, the energy generated by a curved-line feature is spread over multiple adjacent neighbouring channels. In addition to the discrimination of straightlines and curved-lines, the curvature of a curved-line feature can be obtained by analysing the energy distribution along the orientation dimension. Furthermore, the detection algorithm can segregate open and closed curved-line (e.g. circles) features. This thesis also presents a biologically inspired texture-based algorithm using the energy approach for (1) segmenting text embedded in clutter and (2) classifying text scripts without any explicit knowledge of the type of text present. The algorithm for text segmentation utilises the energy information in both the orientation and frequency dimensions. Similarly, energy analysis is applied to the task of text script classification using a set of descriptors derived from the energy information. The resulting algorithms are invariant to scale, rotation and position changes of text and are robust under noisy conditions. The algorithms detailed in this thesis offer a suitable biologically plausible solution in computer vision for various pattern analysis tasks such as line feature detection and classification. Finally it is demonstrated that a massively parallel implementation of the algorithms using Artificial Neural Networks is possible. en
dc.publisher ResearchSpace@Auckland en
dc.relation.ispartof PhD Thesis - University of Auckland en
dc.relation.isreferencedby UoA99114650014002091 en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. en
dc.rights Restricted Item. Full text is available to authenticated members of The University of Auckland only. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title A natural vision inspired approach to line and curve pattern analysis en
dc.type Thesis en
thesis.degree.discipline Electrical and Electronic Engineering en
thesis.degree.grantor The University of Auckland en
thesis.degree.level Doctoral en
thesis.degree.name PhD en
dc.rights.holder Copyright: The author en


Files in this item

Find Full text

This item appears in the following Collection(s)

Show simple item record

Share

Search ResearchSpace


Browse

Statistics