6533b832fe1ef96bd129ab42

RESEARCH PRODUCT

Entire reflective object surface structure understanding based on reflection motion estimation

Olivier LaligantQinglin LuAnastasia ZakharovaEric Fauvet

subject

Surface (mathematics)business.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCognitive neuroscience of visual object recognitionObject (computer science)Artificial IntelligenceMotion estimationSignal ProcessingComputer visionComputer Vision and Pattern RecognitionNoise (video)Specular reflectionArtificial intelligenceReflection (computer graphics)businessSoftwareComputingMethodologies_COMPUTERGRAPHICSCoherence (physics)Mathematics

description

An sub-segmentation method for the reflective surface structure understanding.The use of reflection motion features as spatiotemporal coherence for video segmentation.Straightforward implementation.A building block for object recognition. The presence of reflection on a surface has been a long-standing problem for object recognition since it brings negative effects on object's color, texture and structural information. Because of that, it is not a trivial task to recognize the surface structure affected by the reflection, especially when the object is entirely reflective. Most of the cases, reflection is considered as noise. In this paper, we propose a novel method for entire reflective object sub-segmentation by transforming the reflection motion into object surface label. To the best of our knowledge, the segmentation of entirely reflective surfaces has not been studied. The experimental results on specular and transparent objects show that the surface structures of the reflective objects can be revealed and the segmentation based on the surface structure outperforms the approaches in literature.

https://doi.org/10.1016/j.patrec.2015.09.006