Generalized Shortest Path-based Superpixels for Accurate Segmentation of Spherical Images

• URL: http://arxiv.org/abs/2004.07394v3
• Date: Tue, 14 Jul 2020 13:00:43 GMT
• Title: Generalized Shortest Path-based Superpixels for Accurate Segmentation of Spherical Images
• Authors: R\'emi Giraud, Rodrigo Borba Pinheiro, Yannick Berthoumieu
• Abstract summary: We introduce a new superpixel method for spherical images called SphSPS (for Spherical Shortest Path-based Superpixels) Our approach respects the spherical geometry and generalizes the notion of shortest path between a pixel and a superpixel center on the 3D spherical acquisition space. We show that the feature information on such path can be efficiently integrated into our clustering framework and jointly improves the respect of object contours and the shape regularity.
• Score: 2.4063592468412267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most of existing superpixel methods are designed to segment standard planar images as pre-processing for computer vision pipelines. Nevertheless, the increasing number of applications based on wide angle capture devices, mainly generating 360{\deg} spherical images, have enforced the need for dedicated superpixel approaches. In this paper, we introduce a new superpixel method for spherical images called SphSPS (for Spherical Shortest Path-based Superpixels). Our approach respects the spherical geometry and generalizes the notion of shortest path between a pixel and a superpixel center on the 3D spherical acquisition space. We show that the feature information on such path can be efficiently integrated into our clustering framework and jointly improves the respect of object contours and the shape regularity. To relevantly evaluate this last aspect in the spherical space, we also generalize a planar global regularity metric. Finally, the proposed SphSPS method obtains significantly better performance than both planar and recent spherical superpixel approaches on the reference 360{\deg} spherical panorama segmentation dataset.

Related papers

• Hierarchical Superpixel Segmentation via Structural Information Theory [48.488598357738674]
  Superpixel segmentation is a foundation for many higher-level computer vision tasks. We present SIT-HSS, a hierarchical superpixel segmentation method based on structural information theory. We show that SIT-HSS performs better than state-of-the-art unsupervised superpixel segmentation algorithms.
  arXiv  Detail & Related papers  (2025-01-13T05:39:43Z)
• Splatter-360: Generalizable 360$^{\circ}$ Gaussian Splatting for Wide-baseline Panoramic Images [52.48351378615057]
  textitSplatter-360 is a novel end-to-end generalizable 3DGS framework to handle wide-baseline panoramic images. We introduce a 3D-aware bi-projection encoder to mitigate the distortions inherent in panoramic images. This enables robust 3D-aware feature representations and real-time rendering capabilities.
  arXiv  Detail & Related papers  (2024-12-09T06:58:31Z)
• Deep Spherical Superpixels [1.104960878651584]
  We introduce the first deep learning-based superpixel segmentation approach tailored for omnidirectional images called DSS (for Deep Spherical Superpixels) Our methodology leverages on spherical CNN architectures and the differentiable K-means clustering paradigm for superpixels, to generate superpixels that follow the spherical geometry.
  arXiv  Detail & Related papers  (2024-07-24T15:27:21Z)
• Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images [64.72242126879503]
  Hyperspectral images (HSI) clustering is an important but challenging task. We first use 3-D and 2-D hybrid convolutional neural networks to extract the high-order spatial and spectral features of HSI. We then design a superpixel graph contrastive clustering model to learn discriminative superpixel representations.
  arXiv  Detail & Related papers  (2024-03-04T07:40:55Z)
• Superpixel Transformers for Efficient Semantic Segmentation [32.537400525407186]
  We propose a solution by leveraging the idea of superpixels, an over-segmentation of the image, and applying them with a modern transformer framework. Our method achieves state-of-the-art performance in semantic segmentation due to the rich superpixel features generated by the global self-attention mechanism.
  arXiv  Detail & Related papers  (2023-09-28T23:09:30Z)
• SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks [21.523846989009886]
  Most urban applications necessitate building footprints in the form of concise vector graphics with sharp boundaries. This paper introduces a semi-automatic approach for building footprint extraction through semantically-sensitive superpixels and neural graph networks.
  arXiv  Detail & Related papers  (2023-04-12T07:39:20Z)
• Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolution [75.24345439401166]
  This paper focuses on how to embed the high-dimensional spatial-spectral information of hyperspectral (HS) images efficiently and effectively. We formulate HS embedding as an approximation of the posterior distribution of a set of carefully-defined HS embedding events. Then, we incorporate the proposed feature embedding scheme into a source-consistent super-resolution framework that is physically-interpretable. Experiments over three common benchmark datasets demonstrate that PDE-Net achieves superior performance over state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-30T06:59:01Z)
• Efficient Multiscale Object-based Superpixel Framework [62.48475585798724]
  We propose a novel superpixel framework, named Superpixels through Iterative CLEarcutting (SICLE) SICLE exploits object information being able to generate a multiscale segmentation on-the-fly. It generalizes recent superpixel methods, surpassing them and other state-of-the-art approaches in efficiency and effectiveness according to multiple delineation metrics.
  arXiv  Detail & Related papers  (2022-04-07T15:59:38Z)
• Superpixel Segmentation Based on Spatially Constrained Subspace Clustering [57.76302397774641]
  We consider each representative region with independent semantic information as a subspace, and formulate superpixel segmentation as a subspace clustering problem. We show that a simple integration of superpixel segmentation with the conventional subspace clustering does not effectively work due to the spatial correlation of the pixels. We propose a novel convex locality-constrained subspace clustering model that is able to constrain the spatial adjacent pixels with similar attributes to be clustered into a superpixel.
  arXiv  Detail & Related papers  (2020-12-11T06:18:36Z)
• Superpixel Segmentation via Convolutional Neural Networks with Regularized Information Maximization [11.696069523681178]
  We propose an unsupervised superpixel segmentation method by optimizing a randomly-d convolutional neural network (CNN) in inference time. Our method generates superpixels via CNN from a single image without any labels by minimizing a proposed objective function for superpixel segmentation in inference time.
  arXiv  Detail & Related papers  (2020-02-17T04:32:03Z)

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.