A class-independent texture-separation method based on a pixel-wise binary classification

3Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.

Abstract

Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder–decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation.

Cite

CITATION STYLE

APA

Soares, L. de A., Côco, K. F., Ciarelli, P. M., & Salles, E. O. T. (2020). A class-independent texture-separation method based on a pixel-wise binary classification. Sensors (Switzerland), 20(18), 1–26. https://doi.org/10.3390/s20185432

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free