we first describe how to “fuzzify” the estimated binary columns to create a [0,1]-valued column. werefer to this [0,1] -valued column as the soft segmentation column of the noisy spectrogram column.similarly to the collection of soft segmentation columns as the soft segmentation image, or simply asthe soft segmentation. the band-dependent posterior probability that the hard segmentation columnv...

In this work, we present multi-atlas based techniques for both segmentation and landmark detection. We focus on modality and anatomy independent techniques to be applied to a wide range of input images, in contrast to methods customized to a specific anatomy or image modality. For segmentation, we use label propagation from several atlases to a target image via a Markov random field (MRF) based...

Multi-atlas based segmentation has been applied widely in medical image analysis. For label fusion, previous studies show that image similarity-based local weighting techniques produce the most accurate results. However, these methods ignore the correlations between results produced by different atlases. Furthermore, they rely on pre-selected weighting models and ad hoc methods to choose model ...

We develop a novel deformable atlas method for multistructure segmentation that seamlessly combines the advantages of image-based and atlas-based methods. The method formulates a probabilistic framework that combines prior anatomical knowledge with image-based cues that are specific to the subject's anatomy, and solves it using expectation-maximization method. It improves the segmentation over ...

Traditional motion segmentation techniques generally depend on a pre-estimated optical flow. Unfortunately, the lack of precision over edges of most popular motion estimation methods makes them unsuited to recover the exact shape of moving objects. In this contribution, we present an original motion segmentation technique using a K-nearest-neighbor-based fusion of spatial and temporal label cue...

Abstract Image segmentation of heterogeneous comparable objects lying beneath the earth’s surface is a fundamental but challenging research area in remote sensing. Learning approaches are used sensing image to improve accuracy at expense time and large amount data, their performance need be finely classified due information diversity constraints. In this work, we proposed an novel feature based...

Convolutional neural networks (CNNs) have strong ability to extract local features, but it is slightly lacking in extracting global contexts. In contrast, transformers are good at long-distance modelling due the self-attention mechanisms while its performance localization limited. On other hand, feature gap between an encoder and decoder also challenging for a U-shaped network, which adopts pla...

introduction this research addresses the automatic extraction of alluvial fans using four methods of segmentation from satellite data. this segmentation method divides images into partitions. it is typically used to recognize objects or other relevant purposes in digital images (fu, 2013:3260). alluvial fans have always been a landform that attracts human because they are suitable areas for liv...

Atlas-based image labeling is a fundamental tool in medical image segmentation. Recent years have seen extension of single-atlas warping to multi-atlas warping and fusion, which has clearly demonstrated the advantage of consensus-based segmentation. Herein, we further extend this concept, by leveraging upon morphological appearance manifolds (MAMs), which have been previously used to represent ...