.png){kind=icon}
Return
A multiscale image segmentation method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2015.10.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents a novel image segmentation framework that combines image segmentation and feature extraction into a unified model. The proposed model consists of two parts: the segmentation part and the multiscale decomposition part. In the model, the segmentation part relies on the image intensities in the regions of interest while the multiscale decomposition part depends on the features in different scales. The multiscale decomposition facilitates the process of segmentation since the region of interest can be easily detected from a proper scale. The total variation projection regularization (TVPR) is used to preserve geometric shape of the segmented regions. According to the geometric significance of TVPR parameters, an adaptive TVPR parameters selection method is presented and edges of different region can be well preserved. The proposed method is able to deal with intensity inhomogeneities and mixed noises often occurred in real-world images, which present challenges in image segmentation. Numerical examples on synthetic and real images are given to demonstrate the effectiveness of the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Image segmentation
Multiscale decomposition
Variational model
Total variation
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveOptimization of the PID-PD parameters of the overhead crane control system by using PSO algorithm
OAAI
OAAIShareSaveShareSaveShareSave
View more