Estimating shadows with the bright channel cue

Alexandros Panagopoulos; Chaohui Wang; Dimitris Samaras; Nikos Paragios

doi:10.1007/978-3-642-35740-4_1

Estimating shadows with the bright channel cue

Alexandros Panagopoulos
, Chaohui Wang
, Dimitris Samaras
, Nikos Paragios

Computer Science

Stony Brook University

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

11 Scopus citations

Abstract

In this paper, we introduce a simple but efficient cue for the extraction of shadows from a single color image, the bright channel cue. We discuss its limitations and offer two methods to refine the bright channel: by computing confidence values for the cast shadows, based on a shadow-dependent feature, such as hue; and by combining the bright channel with illumination invariant representations of the original image in a flexible way using an MRF model. We present qualitative and quantitative results for shadow detection, as well as results in illumination estimation from shadows. Our results show that our method achieves satisfying results despite the simplicity of the approach.

Original language	English
Title of host publication	Trends and Topics in Computer Vision - ECCV 2010 Workshops, Revised Selected Papers
Pages	1-12
Number of pages	12
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-35740-4_1
State	Published - 2012
Event	11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece Duration: Sep 10 2010 → Sep 11 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6554 LNCS

Conference

Conference	11th European Conference on Computer Vision, ECCV 2010
Country/Territory	Greece
City	Heraklion, Crete
Period	09/10/10 → 09/11/10

Access to Document

10.1007/978-3-642-35740-4_1

Cite this

Panagopoulos, A., Wang, C., Samaras, D., & Paragios, N. (2012). Estimating shadows with the bright channel cue. In Trends and Topics in Computer Vision - ECCV 2010 Workshops, Revised Selected Papers (PART 2 ed., pp. 1-12). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6554 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-35740-4_1

Panagopoulos, Alexandros ; Wang, Chaohui ; Samaras, Dimitris et al. / Estimating shadows with the bright channel cue. Trends and Topics in Computer Vision - ECCV 2010 Workshops, Revised Selected Papers. PART 2. ed. 2012. pp. 1-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{d44633e95b9a47f3860baab8bb0b52af,

title = "Estimating shadows with the bright channel cue",

abstract = "In this paper, we introduce a simple but efficient cue for the extraction of shadows from a single color image, the bright channel cue. We discuss its limitations and offer two methods to refine the bright channel: by computing confidence values for the cast shadows, based on a shadow-dependent feature, such as hue; and by combining the bright channel with illumination invariant representations of the original image in a flexible way using an MRF model. We present qualitative and quantitative results for shadow detection, as well as results in illumination estimation from shadows. Our results show that our method achieves satisfying results despite the simplicity of the approach.",

author = "Alexandros Panagopoulos and Chaohui Wang and Dimitris Samaras and Nikos Paragios",

year = "2012",

doi = "10.1007/978-3-642-35740-4\_1",

language = "English",

isbn = "9783642357398",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "1--12",

booktitle = "Trends and Topics in Computer Vision - ECCV 2010 Workshops, Revised Selected Papers",

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note = "11th European Conference on Computer Vision, ECCV 2010 ; Conference date: 10-09-2010 Through 11-09-2010",

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Panagopoulos, A, Wang, C, Samaras, D & Paragios, N 2012, Estimating shadows with the bright channel cue. in Trends and Topics in Computer Vision - ECCV 2010 Workshops, Revised Selected Papers. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6554 LNCS, pp. 1-12, 11th European Conference on Computer Vision, ECCV 2010, Heraklion, Crete, Greece, 09/10/10. https://doi.org/10.1007/978-3-642-35740-4_1

Estimating shadows with the bright channel cue. / Panagopoulos, Alexandros; Wang, Chaohui; Samaras, Dimitris et al.
Trends and Topics in Computer Vision - ECCV 2010 Workshops, Revised Selected Papers. PART 2. ed. 2012. p. 1-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6554 LNCS, No. PART 2).

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

TY - GEN

T1 - Estimating shadows with the bright channel cue

AU - Panagopoulos, Alexandros

AU - Wang, Chaohui

AU - Samaras, Dimitris

AU - Paragios, Nikos

PY - 2012

Y1 - 2012

N2 - In this paper, we introduce a simple but efficient cue for the extraction of shadows from a single color image, the bright channel cue. We discuss its limitations and offer two methods to refine the bright channel: by computing confidence values for the cast shadows, based on a shadow-dependent feature, such as hue; and by combining the bright channel with illumination invariant representations of the original image in a flexible way using an MRF model. We present qualitative and quantitative results for shadow detection, as well as results in illumination estimation from shadows. Our results show that our method achieves satisfying results despite the simplicity of the approach.

AB - In this paper, we introduce a simple but efficient cue for the extraction of shadows from a single color image, the bright channel cue. We discuss its limitations and offer two methods to refine the bright channel: by computing confidence values for the cast shadows, based on a shadow-dependent feature, such as hue; and by combining the bright channel with illumination invariant representations of the original image in a flexible way using an MRF model. We present qualitative and quantitative results for shadow detection, as well as results in illumination estimation from shadows. Our results show that our method achieves satisfying results despite the simplicity of the approach.

UR - https://www.scopus.com/pages/publications/84871152413

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DO - 10.1007/978-3-642-35740-4_1

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SN - 9783642357398

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1

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BT - Trends and Topics in Computer Vision - ECCV 2010 Workshops, Revised Selected Papers

T2 - 11th European Conference on Computer Vision, ECCV 2010

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ER -

Panagopoulos A, Wang C, Samaras D, Paragios N. Estimating shadows with the bright channel cue. In Trends and Topics in Computer Vision - ECCV 2010 Workshops, Revised Selected Papers. PART 2 ed. 2012. p. 1-12. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-35740-4_1

Estimating shadows with the bright channel cue

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