IEEE Trans Image Process - A curve evolution approach for unsupervised segmentation of images with low depth of field.

Tópicos

{ imag(2675) segment(2577) method(1081) }
{ featur(1941) imag(1645) propos(1176) }
{ model(2220) cell(1177) simul(1124) }
{ bind(1733) structur(1185) ligand(1036) }
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Resumo

In this paper, we describe a novel algorithm for unsupervised segmentation of images with low depth of field (DOF). First of all, a multi-scale reblurring model is used to detect the object of interest (OOI) in saliency space. Then, to determine the boundary of OOI, an active contour model based on hybrid energy function is proposed. In this model, a global energy item related with the saliency map is adopted to find the global minimum, and a local energy term regarding the low DOF image is used to improve the segmentation precision. In addition, an adaptive parameter is attached to this model to balance the weight of global and local energy. Furthermore, an unsupervised curve initialization method is designed to reduce the number of evolution iterations. Finally, we conduct experiments on various low DOF images, and the results demonstrate the high robustness and precision of the proposed approach.

Resumo Limpo

paper describ novel algorithm unsupervis segment imag low depth field dof first multiscal reblur model use detect object interest ooi salienc space determin boundari ooi activ contour model base hybrid energi function propos model global energi item relat salienc map adopt find global minimum local energi term regard low dof imag use improv segment precis addit adapt paramet attach model balanc weight global local energi furthermor unsupervis curv initi method design reduc number evolut iter final conduct experi various low dof imag result demonstr high robust precis propos approach

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