Texture Discrimination via Hilbert Curve Path Based Information Quantifiers

• URL: http://arxiv.org/abs/2409.15327v1
• Date: Fri, 6 Sep 2024 22:51:54 GMT
• Title: Texture Discrimination via Hilbert Curve Path Based Information Quantifiers
• Authors: Aurelio F. Bariviera, Roberta Hansen, Verónica E. Pastor,
• Abstract summary: This paper proposes a texture classification method that extracts data from images using the Hilbert curve. Three information theory quantifiers are then computed: permutation entropy, permutation complexity, and Fisher information measure.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The analysis of the spatial arrangement of colors and roughness/smoothness of figures is relevant due to its wide range of applications. This paper proposes a texture classification method that extracts data from images using the Hilbert curve. Three information theory quantifiers are then computed: permutation entropy, permutation complexity, and Fisher information measure. The proposal exhibits some important properties: (i) it allows to discriminate figures according to varying degrees of correlations (as measured by the Hurst exponent), (ii) it is invariant to rotation and symmetry transformations, (iii) it can be used either in black and white or color images. Validations have been made not only using synthetic images but also using the well-known Brodatz image database.

Related papers

• Symmetry Discovery for Different Data Types [52.2614860099811]
  Equivariant neural networks incorporate symmetries into their architecture, achieving higher generalization performance. We propose LieSD, a method for discovering symmetries via trained neural networks which approximate the input-output mappings of the tasks. We validate the performance of LieSD on tasks with symmetries such as the two-body problem, the moment of inertia matrix prediction, and top quark tagging.
  arXiv  Detail & Related papers  (2024-10-13T13:39:39Z)
• Explicit Correspondence Matching for Generalizable Neural Radiance Fields [49.49773108695526]
  We present a new NeRF method that is able to generalize to new unseen scenarios and perform novel view synthesis with as few as two source views. The explicit correspondence matching is quantified with the cosine similarity between image features sampled at the 2D projections of a 3D point on different views. Our method achieves state-of-the-art results on different evaluation settings, with the experiments showing a strong correlation between our learned cosine feature similarity and volume density.
  arXiv  Detail & Related papers  (2023-04-24T17:46:01Z)
• Neural Color Operators for Sequential Image Retouching [62.99812889713773]
  We propose a novel image retouching method by modeling the retouching process as performing a sequence of newly introduced trainable neural color operators. The neural color operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar. Our method consistently achieves the best results compared with SOTA methods in both quantitative measures and visual qualities.
  arXiv  Detail & Related papers  (2022-07-17T05:33:19Z)
• Detecting Recolored Image by Spatial Correlation [60.08643417333974]
  Image recoloring is an emerging editing technique that can manipulate the color values of an image to give it a new style. In this paper, we explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring. Our method achieves the state-of-the-art detection accuracy on multiple benchmark datasets and exhibits well generalization for unknown types of recoloring methods.
  arXiv  Detail & Related papers  (2022-04-23T01:54:06Z)
• Multiscale Analysis for Improving Texture Classification [62.226224120400026]
  This paper employs the Gaussian-Laplacian pyramid to treat different spatial frequency bands of a texture separately. We aggregate features extracted from gray and color texture images using bio-inspired texture descriptors, information-theoretic measures, gray-level co-occurrence matrix features, and Haralick statistical features into a single feature vector.
  arXiv  Detail & Related papers  (2022-04-21T01:32:22Z)
• 3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching [24.41451985857662]
  We address the problem of 3D shape registration and we propose a novel technique based on spectral graph theory and probabilistic matching. The main contribution of this chapter is to extend the spectral graph matching methods to very large graphs by combining spectral graph matching with Laplacian embedding.
  arXiv  Detail & Related papers  (2021-06-21T15:02:31Z)
• The Geometry of Deep Generative Image Models and its Applications [0.0]
  Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets. These networks are trained to map random inputs in their latent space to new samples representative of the learned data. The structure of the latent space is hard to intuit due to its high dimensionality and the non-linearity of the generator.
  arXiv  Detail & Related papers  (2021-01-15T07:57:33Z)
• Learning Equivariant Representations [10.745691354609738]
  Convolutional neural networks (CNNs) are successful examples of this principle. We propose equivariant models for different transformations defined by groups of symmetries. These models leverage symmetries in the data to reduce sample and model complexity and improve generalization performance.
  arXiv  Detail & Related papers  (2020-12-04T18:46:17Z)
• Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild [96.09941542587865]
  We present a novel 3D pose refinement approach based on differentiable rendering for objects of arbitrary categories in the wild. In this way, we precisely align 3D models to objects in RGB images which results in significantly improved 3D pose estimates. We evaluate our approach on the challenging Pix3D dataset and achieve up to 55% relative improvement compared to state-of-the-art refinement methods in multiple metrics.
  arXiv  Detail & Related papers  (2020-07-17T12:34:38Z)
• Face Phylogeny Tree Using Basis Functions [13.164846772893455]
  Photometric transformations can be applied to a face image repeatedly creating a set of near-duplicate images. Identifying the original image from a set of such near-duplicates and deducing the relationship between them are important in the context of digital image forensics. In this work, we utilize three different families of basis functions to model pairwise relationships between near-duplicate images.
  arXiv  Detail & Related papers  (2020-02-21T00:13:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.