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Despite the dramatic growth of digital image and video data in recent years, many challenges remain in enabling computers to interpret visual content. Visual information is inherently ambiguous and semantically impoverished. There consequently exists a wide semantic gap between human interpretations of image and video data and those currently derivable by a computer. My research demonstrates how this gap can be narrowed through the use of ontologies which represent task-specific attributes, objects, and relations, and relate these to the processing modules available for their detection and recognition. Terms in the ontology therefore carry meaning directly related to the appearance of real world objects. Tasks such as image retrieval, automated visual surveillance, and visually mediated human computer interaction can then be carried out by processing sentences in a visual language defined over the ontology. The efficacy of the proposed approach is demonstrated through the development and analysis of solutions to a range of challenging visual analysis problems. Content-based image analysis and ontology-based information modeling are starting to revolutionise professional image search and can also be used to greatly speed up time consuming tasks such as image annotation and optical character recognition.
I am also actively working on pattern matching algorithms for the biological sciences. A generic image processing and identification tool has been developed by me and is available for free use by the academic community. It has already been deployed for research in various fields of ecology and zoology, for examples Manta Matcher and NPM.
Despite the dramatic growth of digital image and video data in recent years, many challenges remain in enabling computers to interpret visual content. Visual information is inherently ambiguous and semantically impoverished. There consequently exists a wide semantic gap between human interpretations of image and video data and those currently derivable by a computer. My research demonstrates how this gap can be narrowed through the use of ontologies which represent task-specific attributes, objects, and relations, and relate these to the processing modules available for their detection and recognition. Terms in the ontology therefore carry meaning directly related to the appearance of real world objects. Tasks such as image retrieval, automated visual surveillance, and visually mediated human computer interaction can then be carried out by processing sentences in a visual language defined over the ontology. The efficacy of the proposed approach is demonstrated through the development and analysis of solutions to a range of challenging visual analysis problems. Content-based image analysis and ontology-based information modeling are starting to revolutionise professional image search and can also be used to greatly speed up time consuming tasks such as image annotation and optical character recognition.
I am also actively working on pattern matching algorithms for the biological sciences. A generic image processing and identification tool has been developed by me and is available for free use by the academic community. It has already been deployed for research in various fields of ecology and zoology, for examples Manta Matcher and NPM.
研究兴趣
论文共 35 篇作者统计合作学者相似作者
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Evolution; international journal of organic evolutionno. 10 (2023): 2224-2233
Nature ecology & evolutionno. 12 (2023): 1978-1982
2022 12TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION SYSTEMS (ICPRS) (2022)
arXiv (Cornell University) (2021)
arXivorg (2020)
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FOLIA MORPHOLOGICAno. 2 (2018): 179-193
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#Papers: 35
#Citation: 715
H-Index: 13
G-Index: 26
Sociability: 4
Diversity: 2
Activity: 6
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