Contrast Invariant and Affine sub-pixel Optical Flow

Neus Sabater; Sébastien Leprince; Jean Philippe Avouac

doi:10.1109/ICIP.2012.6466793

Contrast Invariant and Affine sub-pixel Optical Flow

Neus Sabater, Sébastien Leprince, Jean Philippe Avouac

School of Geog & Environmental Scs

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

This paper presents the Contrast Invariant and Affine Optical Flow (CIAO), a dense area-based sub-pixel image matching algorithm. CIAO does not force the estimated disparity field between two images to be smooth and allows for contrast and brightness changes. It is robust to drastic changes in the images' content thanks to an adaptive weighting of the neighboring pixels. In addition, the proposed model considers local affine displacements instead of simpler translations. CIAO proves particularly useful to extract high quality, high accuracy, and high density disparity maps from pairs of stereoscopic images. Comparative results with real and synthetic data are provided.

Original language	English
Title of host publication	2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Pages	53-56
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2012.6466793
Publication status	Published (in print/issue) - 1 Dec 2012
Event	2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL, United States Duration: 30 Sept 2012 → 3 Oct 2012

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Conference

Conference	2012 19th IEEE International Conference on Image Processing, ICIP 2012
Country/Territory	United States
City	Lake Buena Vista, FL
Period	30/09/12 → 3/10/12

Keywords

affine and contrast invariant
Digital Elevation Model (DEM)
Optical flow
remote sensing
sub-pixel disparity

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10.1109/ICIP.2012.6466793

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title = "Contrast Invariant and Affine sub-pixel Optical Flow",

abstract = "This paper presents the Contrast Invariant and Affine Optical Flow (CIAO), a dense area-based sub-pixel image matching algorithm. CIAO does not force the estimated disparity field between two images to be smooth and allows for contrast and brightness changes. It is robust to drastic changes in the images' content thanks to an adaptive weighting of the neighboring pixels. In addition, the proposed model considers local affine displacements instead of simpler translations. CIAO proves particularly useful to extract high quality, high accuracy, and high density disparity maps from pairs of stereoscopic images. Comparative results with real and synthetic data are provided.",

keywords = "affine and contrast invariant, Digital Elevation Model (DEM), Optical flow, remote sensing, sub-pixel disparity",

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Sabater, N, Leprince, S & Avouac, JP 2012, Contrast Invariant and Affine sub-pixel Optical Flow. in 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings., 6466793, Proceedings - International Conference on Image Processing, ICIP, pp. 53-56, 2012 19th IEEE International Conference on Image Processing, ICIP 2012, Lake Buena Vista, FL, United States, 30/09/12. https://doi.org/10.1109/ICIP.2012.6466793

Contrast Invariant and Affine sub-pixel Optical Flow. / Sabater, Neus; Leprince, Sébastien; Avouac, Jean Philippe.
2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings. 2012. p. 53-56 6466793 (Proceedings - International Conference on Image Processing, ICIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Avouac, Jean Philippe

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AB - This paper presents the Contrast Invariant and Affine Optical Flow (CIAO), a dense area-based sub-pixel image matching algorithm. CIAO does not force the estimated disparity field between two images to be smooth and allows for contrast and brightness changes. It is robust to drastic changes in the images' content thanks to an adaptive weighting of the neighboring pixels. In addition, the proposed model considers local affine displacements instead of simpler translations. CIAO proves particularly useful to extract high quality, high accuracy, and high density disparity maps from pairs of stereoscopic images. Comparative results with real and synthetic data are provided.

KW - affine and contrast invariant

KW - Digital Elevation Model (DEM)

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KW - remote sensing

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BT - 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings

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Y2 - 30 September 2012 through 3 October 2012

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Contrast Invariant and Affine sub-pixel Optical Flow

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