A fast hyperspectral hit-or-miss transform with integrated projection-based dimensionality reduction

Research output: Contribution to conferencePaper

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Abstract

The Hit-or-Miss transform (HMT) is a common tool in Mathematical Morphology (MM) used in template matching and object detection and subsequent classification applications. The HMT probes a query image with a pair of structuring elements (SEs) which are designed to detect specific objects of interest. The relative size of hyperspectral image data in particular provides a wealth of information on a scene however, it also makes object detection via a HMT a computationally expensive process. We aim to solve this problem through employing both spatial and spectral dimensionality reduction (DR) techniques to transform a hyperspectral image and its associated SEs designed for the HMT into a reduced space.
Original languageEnglish
Number of pages2
Publication statusPublished - 10 Oct 2018
EventHyperspectral Imaging Applications (HSI) 2018 -
Duration: 10 Oct 201811 Oct 2018
https://www.hsi2018.com

Conference

ConferenceHyperspectral Imaging Applications (HSI) 2018
Period10/10/1811/10/18
Internet address

Fingerprint

Mathematical morphology
Template matching
Object detection

Keywords

  • Hit-or-Miss Transform
  • Mathematical Morphology
  • structuring elements
  • hyperspectral image data

Cite this

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title = "A fast hyperspectral hit-or-miss transform with integrated projection-based dimensionality reduction",
abstract = "The Hit-or-Miss transform (HMT) is a common tool in Mathematical Morphology (MM) used in template matching and object detection and subsequent classification applications. The HMT probes a query image with a pair of structuring elements (SEs) which are designed to detect specific objects of interest. The relative size of hyperspectral image data in particular provides a wealth of information on a scene however, it also makes object detection via a HMT a computationally expensive process. We aim to solve this problem through employing both spatial and spectral dimensionality reduction (DR) techniques to transform a hyperspectral image and its associated SEs designed for the HMT into a reduced space.",
keywords = "Hit-or-Miss Transform, Mathematical Morphology, structuring elements, hyperspectral image data",
author = "Fraser Macfarlane and Paul Murray and Stephen Marshall and Henry White",
year = "2018",
month = "10",
day = "10",
language = "English",
note = "Hyperspectral Imaging Applications (HSI) 2018 ; Conference date: 10-10-2018 Through 11-10-2018",
url = "https://www.hsi2018.com",

}

Macfarlane, F, Murray, P, Marshall, S & White, H 2018, 'A fast hyperspectral hit-or-miss transform with integrated projection-based dimensionality reduction' Paper presented at Hyperspectral Imaging Applications (HSI) 2018, 10/10/18 - 11/10/18, .

A fast hyperspectral hit-or-miss transform with integrated projection-based dimensionality reduction. / Macfarlane, Fraser; Murray, Paul; Marshall, Stephen; White, Henry.

2018. Paper presented at Hyperspectral Imaging Applications (HSI) 2018, .

Research output: Contribution to conferencePaper

TY - CONF

T1 - A fast hyperspectral hit-or-miss transform with integrated projection-based dimensionality reduction

AU - Macfarlane, Fraser

AU - Murray, Paul

AU - Marshall, Stephen

AU - White, Henry

PY - 2018/10/10

Y1 - 2018/10/10

N2 - The Hit-or-Miss transform (HMT) is a common tool in Mathematical Morphology (MM) used in template matching and object detection and subsequent classification applications. The HMT probes a query image with a pair of structuring elements (SEs) which are designed to detect specific objects of interest. The relative size of hyperspectral image data in particular provides a wealth of information on a scene however, it also makes object detection via a HMT a computationally expensive process. We aim to solve this problem through employing both spatial and spectral dimensionality reduction (DR) techniques to transform a hyperspectral image and its associated SEs designed for the HMT into a reduced space.

AB - The Hit-or-Miss transform (HMT) is a common tool in Mathematical Morphology (MM) used in template matching and object detection and subsequent classification applications. The HMT probes a query image with a pair of structuring elements (SEs) which are designed to detect specific objects of interest. The relative size of hyperspectral image data in particular provides a wealth of information on a scene however, it also makes object detection via a HMT a computationally expensive process. We aim to solve this problem through employing both spatial and spectral dimensionality reduction (DR) techniques to transform a hyperspectral image and its associated SEs designed for the HMT into a reduced space.

KW - Hit-or-Miss Transform

KW - Mathematical Morphology

KW - structuring elements

KW - hyperspectral image data

M3 - Paper

ER -