Skip to Main content Skip to Navigation
Journal articles

TRISK: A local features extraction framework for texture-plus-depth content matching

Abstract : In this paper we present a new complete detector–descriptor framework for local features extraction from grayscale texture-plus-depth images. It is designed by putting together a locally normalized binary descriptor and the popular AGAST corner detector modified to incorporate the depth map into the keypoint detection process. With these new local features, we target image matching applications when significant out-of-plane rotations and viewpoint position changes are present in the input data. Our approach is designed to perform on RGBD images acquired with low-cost sensors such as Kinect without any complex depth map preprocessing such as denoising or inpainting. We show improved results with respect to several other highly competitive local image features through both a classic local feature evaluation procedure and an illustrative application scenario. Moreover, the proposed method requires low computational effort.
Complete list of metadatas

Cited literature [62 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01654139
Contributor : Frédéric Dufaux <>
Submitted on : Friday, January 10, 2020 - 11:38:09 AM
Last modification on : Wednesday, June 24, 2020 - 4:19:18 PM
Document(s) archivé(s) le : Saturday, April 11, 2020 - 3:30:27 PM

File

2018_IMAVIS_Karpushin_et_al.pd...
Files produced by the author(s)

Identifiers

Citation

Maxim Karpushin, Giuseppe Valenzise, Frederic Dufaux. TRISK: A local features extraction framework for texture-plus-depth content matching. Image and Vision Computing, Elsevier, 2018, 71, pp.1-16. ⟨10.1016/j.imavis.2017.11.007⟩. ⟨hal-01654139⟩

Share

Metrics

Record views

618

Files downloads

110