Abstract. In this paper, we present a mirror reflection invariant de-scriptor which is inspired from SIFT. While preserving tolerance to scale, rotation and even affine transformation, the proposed descriptor, MIFT, is also invariant to mirror reflection. We analyze the structure of MIFT and show how MIFT outperforms SIFT in the context of mirror reflec-tion while performs as well as SIFT when there is no mirror reflection. The performance evaluation is demonstrated on natural images such as reflection on the water, non-rigid symmetric objects viewed from differ-ent sides, and reflection in the mirror. Based on MIFT, applications to image search and symmetry axis detection for planar symmetric objects are also shown.
International audienceReflective symmetry can be used as a strong prior for many computer vision tas...
International audienceReflective symmetry can be used as a strong prior for many computer vision tas...
We propose a local feature descriptor based on moment. Although conventional scale invariant feature...
The traditional scale invariant feature transform (SIFT) method can extract distinctive features for...
Water reflection detection is a tough task in computer vision, since the reflection is distorted by ...
Water reflection, a kind of typical imperfect reflection symmetry problem, plays an important role i...
International audienceA novel method for reflection symmetry detection is addressed using a projecti...
International audienceA novel method for reflection symmetry detection is addressed using a projecti...
Abstract. Computing reflective symmetries of 2D and 3D shapes is a classical problem in computer vis...
Mirror detection aims to identify the mirror regions in the given input image. Existing works mainly...
We propose a novel approach for detecting partial reflectional symmetry in images. Our method consis...
Water reflection, a typical imperfect reflection symmetry problem, plays an important role in image ...
Abstract — We report a method for the detection and recog-nition of a large planar mirror based on t...
International audienceReflective symmetry can be used as a strong prior for many computer vision tas...
Symmetric-SIFT is a recently proposed local technique used for registering multimodal images. It is ...
International audienceReflective symmetry can be used as a strong prior for many computer vision tas...
International audienceReflective symmetry can be used as a strong prior for many computer vision tas...
We propose a local feature descriptor based on moment. Although conventional scale invariant feature...
The traditional scale invariant feature transform (SIFT) method can extract distinctive features for...
Water reflection detection is a tough task in computer vision, since the reflection is distorted by ...
Water reflection, a kind of typical imperfect reflection symmetry problem, plays an important role i...
International audienceA novel method for reflection symmetry detection is addressed using a projecti...
International audienceA novel method for reflection symmetry detection is addressed using a projecti...
Abstract. Computing reflective symmetries of 2D and 3D shapes is a classical problem in computer vis...
Mirror detection aims to identify the mirror regions in the given input image. Existing works mainly...
We propose a novel approach for detecting partial reflectional symmetry in images. Our method consis...
Water reflection, a typical imperfect reflection symmetry problem, plays an important role in image ...
Abstract — We report a method for the detection and recog-nition of a large planar mirror based on t...
International audienceReflective symmetry can be used as a strong prior for many computer vision tas...
Symmetric-SIFT is a recently proposed local technique used for registering multimodal images. It is ...
International audienceReflective symmetry can be used as a strong prior for many computer vision tas...
International audienceReflective symmetry can be used as a strong prior for many computer vision tas...
We propose a local feature descriptor based on moment. Although conventional scale invariant feature...