An Image Analogies Approach for Multi-Scale Contour Detection

• URL: http://arxiv.org/abs/2007.11047v1
• Date: Tue, 21 Jul 2020 19:14:18 GMT
• Title: An Image Analogies Approach for Multi-Scale Contour Detection
• Authors: Slimane Larabi and Neil M. Robertson
• Abstract summary: We present a new method to locate contours of a query image in the same way that it is done for the reference (i.e. by analogy) 14 derived stereo patches, derived from a mathematical study, are the knowledge used in order to locate contours at different scales independently of the light conditions.
• Score: 4.974890682815778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we deal with contour detection based on the recent image analogy principle which has been successfully used for super-resolution, texture and curves synthesis and interactive editing. Hand-drawn outlines are initially as benchmarks. Given such a reference image, we present a new method based on this expertise to locate contours of a query image in the same way that it is done for the reference (i.e by analogy). Applying a image analogies for contour detection using hand drawn images as leaning images cannot gives good result for any query image. The contour detection may be improved if we increase the number of learning images such that there will be exist similarity between query image and some reference images. In addition of the hardness of contours drawing task, this will increase considerably the time computation. We investigated in this work, how can we avoid this constraint in order to guaranty that all contour pixels will be located for any query image. Fourteen derived stereo patches, derived from a mathematical study, are the knowledge used in order to locate contours at different scales independently of the light conditions. Comprehensive experiments are conducted on different data sets (BSD 500, Horses of Weizmann). The obtained results show superior performance via precision and recall vs. hand-drawn contours at multiple resolutions to the reported state of the art.

Related papers

• ConDL: Detector-Free Dense Image Matching [2.7582789611575897]
  We introduce a deep-learning framework designed for estimating dense image correspondences. Our fully convolutional model generates dense feature maps for images, where each pixel is associated with a descriptor that can be matched across multiple images.
  arXiv  Detail & Related papers  (2024-08-05T18:34:15Z)
• DIAR: Deep Image Alignment and Reconstruction using Swin Transformers [3.1000291317724993]
  We create a dataset that contains images with image distortions. We create perspective distortions with corresponding ground-truth homographies as labels. We use our dataset to train Swin transformer models to analyze sequential image data.
  arXiv  Detail & Related papers  (2023-10-17T21:59:45Z)
• Ensembling with Deep Generative Views [72.70801582346344]
  generative models can synthesize "views" of artificial images that mimic real-world variations, such as changes in color or pose. Here, we investigate whether such views can be applied to real images to benefit downstream analysis tasks such as image classification. We use StyleGAN2 as the source of generative augmentations and investigate this setup on classification tasks involving facial attributes, cat faces, and cars.
  arXiv  Detail & Related papers  (2021-04-29T17:58:35Z)
• Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings [29.412748394892105]
  We propose an interpretable image-embedding that cuts the search in scale space to essentially a lookup. We show how this embedding yields competitive image-matching results, while being simpler, faster, and also interpretable by humans.
  arXiv  Detail & Related papers  (2020-08-13T10:01:07Z)
• Unsupervised Landmark Learning from Unpaired Data [117.81440795184587]
  Recent attempts for unsupervised landmark learning leverage synthesized image pairs that are similar in appearance but different in poses. We propose a cross-image cycle consistency framework which applies the swapping-reconstruction strategy twice to obtain the final supervision. Our proposed framework is shown to outperform strong baselines by a large margin.
  arXiv  Detail & Related papers  (2020-06-29T13:57:20Z)
• High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling [122.06593036862611]
  Existing image inpainting methods often produce artifacts when dealing with large holes in real applications. We propose an iterative inpainting method with a feedback mechanism. Experiments show that our method significantly outperforms existing methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2020-05-24T13:23:45Z)
• Coarse-to-Fine Gaze Redirection with Numerical and Pictorial Guidance [74.27389895574422]
  We propose a novel gaze redirection framework which exploits both a numerical and a pictorial direction guidance. The proposed method outperforms the state-of-the-art approaches in terms of both image quality and redirection precision.
  arXiv  Detail & Related papers  (2020-04-07T01:17:27Z)
• Geometrically Mappable Image Features [85.81073893916414]
  Vision-based localization of an agent in a map is an important problem in robotics and computer vision. We propose a method that learns image features targeted for image-retrieval-based localization.
  arXiv  Detail & Related papers  (2020-03-21T15:36:38Z)
• Image Matching across Wide Baselines: From Paper to Practice [80.9424750998559]
  We introduce a comprehensive benchmark for local features and robust estimation algorithms. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods. We show that with proper settings, classical solutions may still outperform the perceived state of the art.
  arXiv  Detail & Related papers  (2020-03-03T15:20:57Z)
• Fabricated Pictures Detection with Graph Matching [0.36832029288386137]
  Fabricating experimental pictures in research work is a serious academic misconduct. We present a framework to detect similar, or perhaps fabricated, pictures with the graph matching techniques.
  arXiv  Detail & Related papers  (2020-01-16T12:29:16Z)

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.