CSDD features: Center-surround distribution distance for feature extraction and matching

Robert T. Collins; Weina Ge

doi:10.1007/978-3-540-88690-7_11

CSDD features: Center-surround distribution distance for feature extraction and matching

Robert T. Collins, Weina Ge

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

8 Scopus citations

Abstract

We present an interest region operator and feature descriptor called Center-Surround Distribution Distance (CSDD) that is based on comparing feature distributions between a central foreground region and a surrounding ring of background pixels. In addition to finding the usual light(dark) blobs surrounded by a dark(light) background, CSDD also detects blobs with arbitrary color distribution that "stand out" perceptually because they look different from the background. A proof-of-concept implementation using an isotropic scale-space extracts feature descriptors that are invariant to image rotation and covariant with change of scale. Detection repeatability is evaluated and compared with other state-of-the-art approaches using a standard dataset, while use of CSDD features for image registration is demonstrated within a RANSAC procedure for affine image matching.

Original language	English (US)
Title of host publication	Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings
Publisher	Springer Verlag
Pages	140-153
Number of pages	14
Edition	PART 3
ISBN (Print)	3540886893, 9783540886891
DOIs	https://doi.org/10.1007/978-3-540-88690-7_11
State	Published - 2008
Event	10th European Conference on Computer Vision, ECCV 2008 - Marseille, France Duration: Oct 12 2008 → Oct 18 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	5304 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	10th European Conference on Computer Vision, ECCV 2008
Country/Territory	France
City	Marseille
Period	10/12/08 → 10/18/08

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-88690-7_11

Cite this

Collins, R. T., & Ge, W. (2008). CSDD features: Center-surround distribution distance for feature extraction and matching. In Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings (PART 3 ed., pp. 140-153). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5304 LNCS, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-540-88690-7_11

Collins, Robert T. ; Ge, Weina. / CSDD features : Center-surround distribution distance for feature extraction and matching. Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings. PART 3. ed. Springer Verlag, 2008. pp. 140-153 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

@inproceedings{a37386978ce44c70926c4ed92c5033dc,

title = "CSDD features: Center-surround distribution distance for feature extraction and matching",

abstract = "We present an interest region operator and feature descriptor called Center-Surround Distribution Distance (CSDD) that is based on comparing feature distributions between a central foreground region and a surrounding ring of background pixels. In addition to finding the usual light(dark) blobs surrounded by a dark(light) background, CSDD also detects blobs with arbitrary color distribution that {"}stand out{"} perceptually because they look different from the background. A proof-of-concept implementation using an isotropic scale-space extracts feature descriptors that are invariant to image rotation and covariant with change of scale. Detection repeatability is evaluated and compared with other state-of-the-art approaches using a standard dataset, while use of CSDD features for image registration is demonstrated within a RANSAC procedure for affine image matching.",

author = "Collins, {Robert T.} and Weina Ge",

year = "2008",

doi = "10.1007/978-3-540-88690-7_11",

language = "English (US)",

isbn = "3540886893",

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

publisher = "Springer Verlag",

number = "PART 3",

pages = "140--153",

booktitle = "Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings",

address = "Germany",

edition = "PART 3",

}

Collins, RT & Ge, W 2008, CSDD features: Center-surround distribution distance for feature extraction and matching. in Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 5304 LNCS, Springer Verlag, pp. 140-153, 10th European Conference on Computer Vision, ECCV 2008, Marseille, France, 10/12/08. https://doi.org/10.1007/978-3-540-88690-7_11

CSDD features: Center-surround distribution distance for feature extraction and matching. / Collins, Robert T.; Ge, Weina.
Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings. PART 3. ed. Springer Verlag, 2008. p. 140-153 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5304 LNCS, No. PART 3).

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

TY - GEN

T1 - CSDD features

T2 - 10th European Conference on Computer Vision, ECCV 2008

AU - Collins, Robert T.

AU - Ge, Weina

PY - 2008

Y1 - 2008

N2 - We present an interest region operator and feature descriptor called Center-Surround Distribution Distance (CSDD) that is based on comparing feature distributions between a central foreground region and a surrounding ring of background pixels. In addition to finding the usual light(dark) blobs surrounded by a dark(light) background, CSDD also detects blobs with arbitrary color distribution that "stand out" perceptually because they look different from the background. A proof-of-concept implementation using an isotropic scale-space extracts feature descriptors that are invariant to image rotation and covariant with change of scale. Detection repeatability is evaluated and compared with other state-of-the-art approaches using a standard dataset, while use of CSDD features for image registration is demonstrated within a RANSAC procedure for affine image matching.

AB - We present an interest region operator and feature descriptor called Center-Surround Distribution Distance (CSDD) that is based on comparing feature distributions between a central foreground region and a surrounding ring of background pixels. In addition to finding the usual light(dark) blobs surrounded by a dark(light) background, CSDD also detects blobs with arbitrary color distribution that "stand out" perceptually because they look different from the background. A proof-of-concept implementation using an isotropic scale-space extracts feature descriptors that are invariant to image rotation and covariant with change of scale. Detection repeatability is evaluated and compared with other state-of-the-art approaches using a standard dataset, while use of CSDD features for image registration is demonstrated within a RANSAC procedure for affine image matching.

UR - http://www.scopus.com/inward/record.url?scp=56749178351&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=56749178351&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-88690-7_11

DO - 10.1007/978-3-540-88690-7_11

M3 - Conference contribution

AN - SCOPUS:56749178351

SN - 3540886893

SN - 9783540886891

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

SP - 140

EP - 153

BT - Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings

PB - Springer Verlag

Y2 - 12 October 2008 through 18 October 2008

ER -

Collins RT, Ge W. CSDD features: Center-surround distribution distance for feature extraction and matching. In Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings. PART 3 ed. Springer Verlag. 2008. p. 140-153. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-540-88690-7_11

CSDD features: Center-surround distribution distance for feature extraction and matching

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this