.png){kind=icon}
.png){kind=icon}
Return
Efficient Image-Warping Framework for Content-Adaptive Superpixels Generation
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2021.3106586.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We address the problem of efficient content-adaptive superpixel segmentation. Instead of adapting the size and/or amount of superpixels to the image content, we propose a warpingtransform that makes the image content more suitable to be segmented into regular superpixels. Regular superpixels in the warped image induce content-adaptive superpixels in the original image with improved segmentation accuracy. To efficiently compute the warping transform, we develop an iterative coarse-to-fine optimization procedure and employ a parallelization strategy allowing for a speedy GPU-based implementation. This solution works as a simple 'add-on' framework over an underlying segmentation algorithm and requires no additional parameters. Compared to the state-of-the-art methods, our approach provides competitive quality results and achieves a better time-accuracy trade-off. We further demonstrate the effectiveness of our method with an application to disparity estimation.
Keywords:
Image segmentation
Optimization
Transforms
Signal processing algorithms
Image edge detection
Image resolution
Time complexity
Content-adaptive superpixels
efficiency
GPU
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W