In this paper, we introduce and study a novel segmentation method for 4D images based on surface evolution governed by nonlinear partial differential equation, the generalized subjective equation. The new uses digital image information from thresholded in local neighborhood. Thus, is accomplished defining edge detector function’s input as weighted sum of norm gradients presmoothed Additionally,...

Almost all existing amodal segmentation methods make the inferences of occluded regions by using features corresponding to whole image. This is against human's perception, where human uses visible part and shape prior knowledge target infer region. To mimic behavior solve ambiguity in learning, we propose a framework, it firstly estimates coarse mask mask. Then based on prediction, our model in...

A novel livewire algorithm for image segmentation is proposed. Based on a generalized graph formulation, our algorithm incorporates the internal energy of the boundary curve while preserving the principle of optimality, resulting in better segmentation results in noisy images.

The segmentation of the acoustic speech signal is a fundamental for the processing of spoken language. The paper at hand provides a survey of studies conducted in our lab concerning the detection of segmentation cues in the speech signal and associated perception of prosodic boundaries. The first two studies presented here employ the methodology of Event-Related Potentials (ERP) to study online...

This artificial language learning (ALL) study investigates how multiple sublexical cues contribute simultaneously to lexical segmentation in French. Previous research [9] has explored segmentation in lexical identification by presenting conflicting sublexical cues in the same experiment. We argue that this method of pitting conflicting cues against each other pairwise does not allow the researc...

This chapter discusses the task of topic segmentation: automatically dividing single long recordings or transcripts into shorter, topically coherent segments. First, we look at the task itself, the applications which require it, and some ways to evaluate accuracy. We then explain the most influential approaches – generative and discriminative, supervised and unsupervised – and discuss their app...

Computationally intensive segmentation algorithms often operate on an image pre-segmented into small regions referred to as “superpixels”. We investigate the effect of the choice of the pre-segmentation algorithm and its parameters on the outcome of the final segmentation. Three pre-segmentation algorithms are compared. To avoid the particularities of sophisticated segmentation algorithms, the ...