No abstract

In this paper, a multi-scale particle filter (MSPF) based algorithm and multi-feature-based surround inhibition (MFBSI) algorithm are proposed for contour detection in natural images. The contour detection algorithms are jointly tracks at two edgelets as edge elements. This multi-scale edgelet structure gathers the semi-local information as Bayesian modelling. The model is further approximate by using Monte Carlo algorithm. The individual surround inhibition weights of the feature as, including luminance, luminance contrast, and orientation. The scale-guided strategy is used for features together. The final surround inhibition is modulated using the combined weights in the neuron. The luminance contrast and luminance are proven as an excellent contour extraction capability. The multiscale particle filter (MSPF) based approach provides a better contour for the object segmentation.