mlsanityData to 14 Oct 2026

Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation

arXiv 2410.21562 ↗First posted 28 Oct 2024cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

In this paper, we propose a new approach to perform supervised texture classification/segmentation. The proposed idea is to feed a Fully Convolutional Network with specific texture descriptors. These texture features are extracted from images by using an empirical curvelet transform. We propose a method to build a unique empirical curvelet filter bank adapted to a given dictionary of textures. We then show that the output of these filters can be used to build efficient texture descriptors utilized to finally feed deep learning networks. Our approach is finally evaluated on several datasets and compare the results to various state-of-the-art algorithms and show that the proposed method dramatically outperform all existing ones.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 85 references that are in the map. First posted on arXiv 28 Oct 2024.

Builds on

19 of 85 references.

References · 19

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2016Pyramid Scene Parsing Networkcited by 2,479
  3. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  4. 2016Fully-Convolutional Siamese Networks for Object Trackingcited by 568
  5. 2018Context Encoding for Semantic Segmentationcited by 290
  6. 2017Deep Visual Attention Predictioncited by 70
  7. 2017Video Salient Object Detection via Fully Convolutional Networkscited by 46
  8. 2018From BoW to CNN: Two Decades of Texture Representation for Texture Classificationcited by 40
  9. 2018Deep Texture Manifold for Ground Terrain Recognitioncited by 32
  10. 2016Using Filter Banks in Convolutional Neural Networks for Texture Classificationcited by 15
  11. 2018Fusion of complex networks and randomized neural networks for texture analysiscited by 8
  12. 2014A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentationcited by 6
7 more
  1. 2017Model-based learning of local image features for unsupervised texture segmentationcited by 4
  2. 2018One-shot Texture Segmentationcited by 3
  3. 2017Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classificationcited by 2
  4. 2017Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrievalcited by 2
  5. 2015Combining local regularity estimation and total variation optimization for scale-free texture segmentationcited by 1
  6. 2018Texture Segmentation Based Video Compression Using Convolutional Neural Networkscited by 1
  7. 2024Review of wavelet-based unsupervised texture segmentation, advantage of adaptive waveletscited by 1

Built on by

Not cited yet.