TY - GEN
T1 - A convolutional Riemannian texture model with differential entropic active contours for unsupervised pest detection
AU - Dai, Shuanglu
AU - Man, Hong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - 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.
AB - 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.
KW - Active Contours
KW - Riemannian texture
KW - Unsupervised pest detection
UR - http://www.scopus.com/inward/record.url?scp=85023757243&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023757243&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7952312
DO - 10.1109/ICASSP.2017.7952312
M3 - Conference contribution
AN - SCOPUS:85023757243
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1028
EP - 1032
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -