Abstract
Active Contour Model for Image Segmentation With Dilated Convolution Filter
Highlights
W ITH the advancement of the field of computer vision, image segmentation is becoming more important
The Chan and Vese (CV) model [17] that is derived from the study in [18] is the earliest and most prominent active contour models (ACMs)
The online region-based ACM (ORACM) is based on a user-defined initial contour and level set function (LSF) instead of the gradient of the LSF, which gets updated after each iteration
Summary
W ITH the advancement of the field of computer vision, image segmentation is becoming more important. The ORACM is based on a user-defined initial contour and level set function (LSF) instead of the gradient of the LSF, which gets updated after each iteration This method uses morphological operations (opening and closing) to smooth the LSF instead of Gaussian filters, such as SBGFRLS and LIF models. In [25], dilated convolution (atrous convolution) filtering is proposed; it was shown that the context module increases the efficiency of segmentation models of deep convolutional neural networks To this end, we propose a new ACM method to enhance the segmentation results of inhomogeneous images, referred to as the active contours with local dilated convolution filter (ACLD), yielding higher accuracy and less computational complexity.
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