Efficient Computation of Higher Order 2D Image Moments using the Discrete Radon Transform

• URL: http://arxiv.org/abs/2009.09898v1
• Date: Fri, 4 Sep 2020 15:26:03 GMT
• Title: Efficient Computation of Higher Order 2D Image Moments using the Discrete Radon Transform
• Authors: William Diggin and Michael Diggin
• Abstract summary: We extend an efficient algorithm based on the Discrete Radon Transform to generate moments greater than the 3rd order. Results of scaling the algorithm based on image area and its computational comparison with a standard method demonstrate the efficacy of the approach.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Geometric moments and moment invariants of image artifacts have many uses in computer vision applications, e.g. shape classification or object position and orientation. Higher order moments are of interest to provide additional feature descriptors, to measure kurtosis or to resolve n-fold symmetry. This paper provides the method and practical application to extend an efficient algorithm, based on the Discrete Radon Transform, to generate moments greater than the 3rd order. The mathematical fundamentals are presented, followed by relevant implementation details. Results of scaling the algorithm based on image area and its computational comparison with a standard method demonstrate the efficacy of the approach.

Related papers

• Linear Anchored Gaussian Mixture Model for Location and Width Computations of Objects in Thick Line Shape [1.7205106391379021]
  3D image gray level representation is considered as a finite mixture model of a statistical distribution. Expectation-Maximization algorithm (Algo1) using the original image as input data is used to estimate the model parameters. modified EM algorithm (Algo2) is detailed.
  arXiv  Detail & Related papers  (2024-04-03T20:05:00Z)
• GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization [62.13932669494098]
  This paper presents a 3D Gaussian Inverse Rendering (GIR) method, employing 3D Gaussian representations to factorize the scene into material properties, light, and geometry. We compute the normal of each 3D Gaussian using the shortest eigenvector, with a directional masking scheme forcing accurate normal estimation without external supervision. We adopt an efficient voxel-based indirect illumination tracing scheme that stores direction-aware outgoing radiance in each 3D Gaussian to disentangle secondary illumination for approximating multi-bounce light transport.
  arXiv  Detail & Related papers  (2023-12-08T16:05:15Z)
• Learning Unorthogonalized Matrices for Rotation Estimation [83.94986875750455]
  Estimating 3D rotations is a common procedure for 3D computer vision. One form of representation -- rotation matrices -- is popular due to its continuity. We propose unorthogonalized Pseudo' Rotation Matrices (PRoM)
  arXiv  Detail & Related papers  (2023-12-01T09:56:29Z)
• GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting [51.96353586773191]
  We introduce textbfGS-SLAM that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping system. Our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets.
  arXiv  Detail & Related papers  (2023-11-20T12:08:23Z)
• Dense Visual Odometry Using Genetic Algorithm [0.0]
  In this paper, a new algorithm is developed for visual odometry using a sequence of RGB-D images. The proposed iterative genetic algorithm searches using particles to estimate the optimal motion. We prove the efficiency of our innovative algorithm on a large set of images.
  arXiv  Detail & Related papers  (2023-11-10T16:09:01Z)
• $PC^2$: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction [97.06927852165464]
  Reconstructing the 3D shape of an object from a single RGB image is a long-standing and highly challenging problem in computer vision. We propose a novel method for single-image 3D reconstruction which generates a sparse point cloud via a conditional denoising diffusion process.
  arXiv  Detail & Related papers  (2023-02-21T13:37:07Z)
• Data-Driven Interpolation for Super-Scarce X-Ray Computed Tomography [1.3535770763481902]
  We train shallow neural networks to combine two neighbouring acquisitions into an estimated measurement at an intermediate angle. This yields an enhanced sequence of measurements that can be reconstructed using standard methods. Results are obtained for 2D and 3D imaging, on large biomedical datasets.
  arXiv  Detail & Related papers  (2022-05-16T15:42:41Z)
• Self-supervised Geometric Perception [96.89966337518854]
  Self-supervised geometric perception is a framework to learn a feature descriptor for correspondence matching without any ground-truth geometric model labels. We show that SGP achieves state-of-the-art performance that is on-par or superior to the supervised oracles trained using ground-truth labels.
  arXiv  Detail & Related papers  (2021-03-04T15:34:43Z)
• Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging [52.42007686600479]
  We propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. We show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters.
  arXiv  Detail & Related papers  (2020-12-07T09:27:16Z)
• On Box-Cox Transformation for Image Normality and Pattern Classification [0.6548580592686074]
  This paper revolves around the utility of such a tool as a pre-processing step to transform two-dimensional data. We compare the effect of this light-weight Box-Cox transformation with well-established state-of-the-art low light image enhancement techniques. We also demonstrate the effectiveness of our approach through several test-bed data sets for generic improvement of visual appearance of images.
  arXiv  Detail & Related papers  (2020-04-15T17:10:18Z)
• A level set representation method for N-dimensional convex shape and applications [2.294014185517203]
  We present a new efficient method for convex shape representation, which is regardless of the dimension of the concerned objects. In this paper, we prove that the convexity of the considered object is equivalent to the convexity of the associated signed distance function. We apply this new method to two applications: object segmentation with convexity prior and convex hull problem (especially with outliers)
  arXiv  Detail & Related papers  (2020-03-21T07:37:44Z)

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.