FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W659101412> ?p ?o ?g. }

• W659101412 abstract "Hyperspectral images contain vast amounts of data which can provide crucial information to applications within a variety of scientific fields. Increasingly powerful computer hardware has made it possible to efficiently treat and process hyperspectral images. This thesis is interdisciplinary and focuses on applying known image processing algorithms to a new problem domain, involving bruises on human skin in hyperspectral images. Currently, no research regarding image detection of bruises on human skin have been uncovered. However, several articles have been written on hyperspectral bruise detection on fruits and vegetables. Ratio, difference and principal component analysis (PCA) were commonly applied enhancement algorithms within this field. The three algorithms, in addition to K-means clustering and the watershed segmentation algorithm, have been implemented and tested through a batch application developed in C# and MATLAB. The thesis seeks to determine if the enhancement algorithms can be applied to improve bruise visibility in hyperspectral images for visual inspection. In addition, it also seeks to answer if the enhancements provide a better segmentation basis. Known spectral characteristics form the experimentation basis in addition to identification through visual inspection. To this end, a series of experiments were conducted. The tested algorithms provided a better description of the bruises, the extent of the bruising, and the severity of damage. However, the algorithms tested are not considered robust for consistency of results. It is therefore recommended that the image acquisition setup is standardised for all future hyperspectral images. A larger, more varied data set would increase the statistical power of the results, and improve test conclusion validity. Results indicate that the ratio, difference, and principal component analysis (PCA) algorithms can enhance bruise visibility for visual analysis. However, images that contained weakly visible bruises did not show significant improvements in bruise visibility. Non-visible bruises were not made visible using the enhancement algorithms. Results from the enhancement algorithms were segmented and compared to segmentations of the original reflectance images. The enhancement algorithms provided results that gave more accurate bruise regions using K-means clustering and the watershed segmentation. Both segmentation algorithms gave the overall best results using principal components as input. Watershed provided less accurate segmentations of the input from the difference and ratio algorithms." @default.
• W659101412 created "2016-06-24" @default.
• W659101412 creator A5022454021 @default.
• W659101412 creator A5069804536 @default.
• W659101412 date "2007-01-01" @default.
• W659101412 modified "2023-10-16" @default.
• W659101412 title "An Application of Image Processing Techniques for Enhancement and Segmentation of Bruises in Hyperspectral Images" @default.
• W659101412 hasPublicationYear "2007" @default.
• W659101412 type Work @default.
• W659101412 sameAs 659101412 @default.
• W659101412 citedByCount "0" @default.
• W659101412 crossrefType "dissertation" @default.
• W659101412 hasAuthorship W659101412A5022454021 @default.
• W659101412 hasAuthorship W659101412A5069804536 @default.
• W659101412 hasConcept C115961682 @default.
• W659101412 hasConcept C141071460 @default.
• W659101412 hasConcept C153180895 @default.
• W659101412 hasConcept C154945302 @default.
• W659101412 hasConcept C159078339 @default.
• W659101412 hasConcept C2781424195 @default.
• W659101412 hasConcept C31972630 @default.
• W659101412 hasConcept C41008148 @default.
• W659101412 hasConcept C71924100 @default.
• W659101412 hasConcept C73555534 @default.
• W659101412 hasConcept C89600930 @default.
• W659101412 hasConcept C9417928 @default.
• W659101412 hasConceptScore W659101412C115961682 @default.
• W659101412 hasConceptScore W659101412C141071460 @default.
• W659101412 hasConceptScore W659101412C153180895 @default.
• W659101412 hasConceptScore W659101412C154945302 @default.
• W659101412 hasConceptScore W659101412C159078339 @default.
• W659101412 hasConceptScore W659101412C2781424195 @default.
• W659101412 hasConceptScore W659101412C31972630 @default.
• W659101412 hasConceptScore W659101412C41008148 @default.
• W659101412 hasConceptScore W659101412C71924100 @default.
• W659101412 hasConceptScore W659101412C73555534 @default.
• W659101412 hasConceptScore W659101412C89600930 @default.
• W659101412 hasConceptScore W659101412C9417928 @default.
• W659101412 hasLocation W6591014121 @default.
• W659101412 hasOpenAccess W659101412 @default.
• W659101412 hasPrimaryLocation W6591014121 @default.
• W659101412 hasRelatedWork W1514290832 @default.
• W659101412 hasRelatedWork W1578359847 @default.
• W659101412 hasRelatedWork W1954726557 @default.
• W659101412 hasRelatedWork W1971842130 @default.
• W659101412 hasRelatedWork W1985345361 @default.
• W659101412 hasRelatedWork W1990726526 @default.
• W659101412 hasRelatedWork W2058624884 @default.
• W659101412 hasRelatedWork W2095372921 @default.
• W659101412 hasRelatedWork W2129601604 @default.
• W659101412 hasRelatedWork W2139596778 @default.
• W659101412 hasRelatedWork W2163367160 @default.
• W659101412 hasRelatedWork W2332128461 @default.
• W659101412 hasRelatedWork W2901799301 @default.
• W659101412 hasRelatedWork W2954881697 @default.
• W659101412 hasRelatedWork W2999709373 @default.
• W659101412 hasRelatedWork W3042352987 @default.
• W659101412 hasRelatedWork W3104713581 @default.
• W659101412 hasRelatedWork W312631809 @default.
• W659101412 hasRelatedWork W3186810044 @default.
• W659101412 hasRelatedWork W3202775074 @default.
• W659101412 isParatext "false" @default.
• W659101412 isRetracted "false" @default.
• W659101412 magId "659101412" @default.
• W659101412 workType "dissertation" @default.