An Image Analogies Approach for Multi-Scale Contour Detection

TL;DR

This paper introduces a multi-scale contour detection method based on image analogies, utilizing stereo patches to improve accuracy and robustness across different images and lighting conditions.

Contribution

The authors propose a novel multi-scale contour detection approach using stereo patches derived from mathematical analysis, overcoming limitations of traditional analogy-based methods.

Findings

Achieves higher precision and recall than state-of-the-art methods.

Effective across various datasets like BSD 500 and Weizmann Horses.

Robust to lighting variations and different image resolutions.

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.