A convolutional Riemannian texture model with differential entropic active contours for unsupervised pest detection

Shuanglu Dai, Hong Man

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Pest camouflages in grains or natural environment cause significant difficulties in pest detection using imaging technologies. This paper proposes a convolutional Riemannian texture with differential entropic active contours to distinguish the background regions and expose pest regions. An image texture model is firstly introduced on the Riemannian manifold. A convolutional Riemannian texture structure is then explored to reduce the environmental background textures and highlight potential pest textures. Subsequently, a differential entropic active contour model is developed to estimate the foreground and background distributions. Finally, the estimated foreground and background distributions are used to distinguish pest textures and environmental textures. The final detected regions are obtained by maximizing pixel-wise posterior probabilities on the estimated distributions. Experimental results show that effective detections can be achieved by the proposed method on forestry pests imaging datasets.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Pages1028-1032
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - 16 Jun 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country/TerritoryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • Active Contours
  • Riemannian texture
  • Unsupervised pest detection

Fingerprint

Dive into the research topics of 'A convolutional Riemannian texture model with differential entropic active contours for unsupervised pest detection'. Together they form a unique fingerprint.

Cite this