Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images

We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Scharfenberger, Christian [verfasserIn] Wong, Alexander Clausi, David A

Format:	Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation

Übergeordnetes Werk:	Enthalten in: IEEE transactions on image processing - New York, NY : Inst., 1992, 24(2015), 1, Seite 457-470
Übergeordnetes Werk:	volume:24 ; year:2015 ; number:1 ; pages:457-470

Links:	Volltext Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.1109/TIP.2014.2380351

Katalog-ID:	OLC1959239325

Internformat


LEADER	01000caa a2200265 4500
001	OLC1959239325
003	DE-627
005	20230714150844.0
007	tu
008	160206s2015 xx \|\|\|\|\| 00\| \|\|eng c
024	7		\|a 10.1109/TIP.2014.2380351 \|2 doi
028	5	2	\|a PQ20160617
035			\|a (DE-627)OLC1959239325
035			\|a (DE-599)GBVOLC1959239325
035			\|a (PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0
035			\|a (KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
082	0	4	\|a 004 \|a 620 \|q DNB
084			\|a 54.00 \|2 bkl
100	1		\|a Scharfenberger, Christian \|e verfasserin \|4 aut
245	1	0	\|a Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images
264		1	\|c 2015
336			\|a Text \|b txt \|2 rdacontent
337			\|a ohne Hilfsmittel zu benutzen \|b n \|2 rdamedia
338			\|a Band \|b nc \|2 rdacarrier
520			\|a We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches.
650		4	\|a multilayer approach
650		4	\|a Image resolution
650		4	\|a saliency maps
650		4	\|a Atomic layer deposition
650		4	\|a structured image elements
650		4	\|a natural images
650		4	\|a Computational modeling
650		4	\|a performance evaluation metrics
650		4	\|a image texture
650		4	\|a Statistical textural distinctiveness
650		4	\|a probability
650		4	\|a salient region detection approach
650		4	\|a statistical textural distinctiveness matrix
650		4	\|a structure
650		4	\|a public data sets
650		4	\|a feature extraction
650		4	\|a salient region detection
650		4	\|a edge detection
650		4	\|a sparse texture modeling
650		4	\|a statistical analysis
650		4	\|a Image color analysis
650		4	\|a Image segmentation
650		4	\|a rotational-invariant neighborhood-based textural representations
650		4	\|a structure-guided statistical textural distinctiveness approach
650		4	\|a representative texture atom pairs
650		4	\|a occurrence probability
650		4	\|a image representation
700	1		\|a Wong, Alexander \|4 oth
700	1		\|a Clausi, David A \|4 oth
773	0	8	\|i Enthalten in \|t IEEE transactions on image processing \|d New York, NY : Inst., 1992 \|g 24(2015), 1, Seite 457-470 \|w (DE-627)131074458 \|w (DE-600)1111265-7 \|w (DE-576)029165008 \|x 1057-7149 \|7 nnns
773	1	8	\|g volume:24 \|g year:2015 \|g number:1 \|g pages:457-470
856	4	1	\|u http://dx.doi.org/10.1109/TIP.2014.2380351 \|3 Volltext
856	4	2	\|u http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068
856	4	2	\|u http://www.ncbi.nlm.nih.gov/pubmed/25695960
856	4	2	\|u http://search.proquest.com/docview/1640798317
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_OLC
912			\|a SSG-OLC-TEC
912			\|a SSG-OLC-MAT
912			\|a GBV_ILN_70
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_4318
936	b	k	\|a 54.00 \|q AVZ
951			\|a AR
952			\|d 24 \|j 2015 \|e 1 \|h 457-470

Indexfelder

author_variant	c s cs
matchkey_str	article:10577149:2015----::tutrgiesaitcletrlitntvnsfrainrgod
hierarchy_sort_str	2015
bklnumber	54.00
publishDate	2015
allfields	10.1109/TIP.2014.2380351 doi PQ20160617 (DE-627)OLC1959239325 (DE-599)GBVOLC1959239325 (PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0 (KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Scharfenberger, Christian verfasserin aut Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation Wong, Alexander oth Clausi, David A oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 457-470 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:457-470 http://dx.doi.org/10.1109/TIP.2014.2380351 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068 http://www.ncbi.nlm.nih.gov/pubmed/25695960 http://search.proquest.com/docview/1640798317 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 457-470
spelling	10.1109/TIP.2014.2380351 doi PQ20160617 (DE-627)OLC1959239325 (DE-599)GBVOLC1959239325 (PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0 (KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Scharfenberger, Christian verfasserin aut Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation Wong, Alexander oth Clausi, David A oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 457-470 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:457-470 http://dx.doi.org/10.1109/TIP.2014.2380351 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068 http://www.ncbi.nlm.nih.gov/pubmed/25695960 http://search.proquest.com/docview/1640798317 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 457-470
allfields_unstemmed	10.1109/TIP.2014.2380351 doi PQ20160617 (DE-627)OLC1959239325 (DE-599)GBVOLC1959239325 (PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0 (KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Scharfenberger, Christian verfasserin aut Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation Wong, Alexander oth Clausi, David A oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 457-470 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:457-470 http://dx.doi.org/10.1109/TIP.2014.2380351 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068 http://www.ncbi.nlm.nih.gov/pubmed/25695960 http://search.proquest.com/docview/1640798317 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 457-470
allfieldsGer	10.1109/TIP.2014.2380351 doi PQ20160617 (DE-627)OLC1959239325 (DE-599)GBVOLC1959239325 (PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0 (KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Scharfenberger, Christian verfasserin aut Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation Wong, Alexander oth Clausi, David A oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 457-470 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:457-470 http://dx.doi.org/10.1109/TIP.2014.2380351 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068 http://www.ncbi.nlm.nih.gov/pubmed/25695960 http://search.proquest.com/docview/1640798317 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 457-470
allfieldsSound	10.1109/TIP.2014.2380351 doi PQ20160617 (DE-627)OLC1959239325 (DE-599)GBVOLC1959239325 (PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0 (KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Scharfenberger, Christian verfasserin aut Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation Wong, Alexander oth Clausi, David A oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 457-470 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:457-470 http://dx.doi.org/10.1109/TIP.2014.2380351 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068 http://www.ncbi.nlm.nih.gov/pubmed/25695960 http://search.proquest.com/docview/1640798317 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 457-470
language	English
source	Enthalten in IEEE transactions on image processing 24(2015), 1, Seite 457-470 volume:24 year:2015 number:1 pages:457-470
sourceStr	Enthalten in IEEE transactions on image processing 24(2015), 1, Seite 457-470 volume:24 year:2015 number:1 pages:457-470
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation
dewey-raw	004
isfreeaccess_bool	false
container_title	IEEE transactions on image processing
authorswithroles_txt_mv	Scharfenberger, Christian @@aut@@ Wong, Alexander @@oth@@ Clausi, David A @@oth@@
publishDateDaySort_date	2015-01-01T00:00:00Z
hierarchy_top_id	131074458
dewey-sort	14
id	OLC1959239325
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1959239325</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714150844.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx \|\|\|\|\| 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/TIP.2014.2380351</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1959239325</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1959239325</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Scharfenberger, Christian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multilayer approach</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image resolution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">saliency maps</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Atomic layer deposition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">structured image elements</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">natural images</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computational modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">performance evaluation metrics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image texture</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistical textural distinctiveness</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">probability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">salient region detection approach</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">statistical textural distinctiveness matrix</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">structure</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">public data sets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">feature extraction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">salient region detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">edge detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sparse texture modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">statistical analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image color analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image segmentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">rotational-invariant neighborhood-based textural representations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">structure-guided statistical textural distinctiveness approach</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">representative texture atom pairs</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">occurrence probability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image representation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wong, Alexander</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Clausi, David A</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">IEEE transactions on image processing</subfield><subfield code="d">New York, NY : Inst., 1992</subfield><subfield code="g">24(2015), 1, Seite 457-470</subfield><subfield code="w">(DE-627)131074458</subfield><subfield code="w">(DE-600)1111265-7</subfield><subfield code="w">(DE-576)029165008</subfield><subfield code="x">1057-7149</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:24</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:457-470</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1109/TIP.2014.2380351</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.ncbi.nlm.nih.gov/pubmed/25695960</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1640798317</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4318</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">24</subfield><subfield code="j">2015</subfield><subfield code="e">1</subfield><subfield code="h">457-470</subfield></datafield></record></collection>
author	Scharfenberger, Christian
spellingShingle	Scharfenberger, Christian ddc 004 bkl 54.00 misc multilayer approach misc Image resolution misc saliency maps misc Atomic layer deposition misc structured image elements misc natural images misc Computational modeling misc performance evaluation metrics misc image texture misc Statistical textural distinctiveness misc probability misc salient region detection approach misc statistical textural distinctiveness matrix misc structure misc public data sets misc feature extraction misc salient region detection misc edge detection misc sparse texture modeling misc statistical analysis misc Image color analysis misc Image segmentation misc rotational-invariant neighborhood-based textural representations misc structure-guided statistical textural distinctiveness approach misc representative texture atom pairs misc occurrence probability misc image representation Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images
authorStr	Scharfenberger, Christian
ppnlink_with_tag_str_mv	@@773@@(DE-627)131074458
format	Article
dewey-ones	004 - Data processing & computer science 620 - Engineering & allied operations
delete_txt_mv	keep
author_role	aut
collection	OLC
remote_str	false
illustrated	Not Illustrated
issn	1057-7149
topic_title	004 620 DNB 54.00 bkl Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation
topic	ddc 004 bkl 54.00 misc multilayer approach misc Image resolution misc saliency maps misc Atomic layer deposition misc structured image elements misc natural images misc Computational modeling misc performance evaluation metrics misc image texture misc Statistical textural distinctiveness misc probability misc salient region detection approach misc statistical textural distinctiveness matrix misc structure misc public data sets misc feature extraction misc salient region detection misc edge detection misc sparse texture modeling misc statistical analysis misc Image color analysis misc Image segmentation misc rotational-invariant neighborhood-based textural representations misc structure-guided statistical textural distinctiveness approach misc representative texture atom pairs misc occurrence probability misc image representation
topic_unstemmed	ddc 004 bkl 54.00 misc multilayer approach misc Image resolution misc saliency maps misc Atomic layer deposition misc structured image elements misc natural images misc Computational modeling misc performance evaluation metrics misc image texture misc Statistical textural distinctiveness misc probability misc salient region detection approach misc statistical textural distinctiveness matrix misc structure misc public data sets misc feature extraction misc salient region detection misc edge detection misc sparse texture modeling misc statistical analysis misc Image color analysis misc Image segmentation misc rotational-invariant neighborhood-based textural representations misc structure-guided statistical textural distinctiveness approach misc representative texture atom pairs misc occurrence probability misc image representation
topic_browse	ddc 004 bkl 54.00 misc multilayer approach misc Image resolution misc saliency maps misc Atomic layer deposition misc structured image elements misc natural images misc Computational modeling misc performance evaluation metrics misc image texture misc Statistical textural distinctiveness misc probability misc salient region detection approach misc statistical textural distinctiveness matrix misc structure misc public data sets misc feature extraction misc salient region detection misc edge detection misc sparse texture modeling misc statistical analysis misc Image color analysis misc Image segmentation misc rotational-invariant neighborhood-based textural representations misc structure-guided statistical textural distinctiveness approach misc representative texture atom pairs misc occurrence probability misc image representation
format_facet	Aufsätze Gedruckte Aufsätze
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	nc
author2_variant	a w aw d a c da dac
hierarchy_parent_title	IEEE transactions on image processing
hierarchy_parent_id	131074458
dewey-tens	000 - Computer science, knowledge & systems 620 - Engineering
hierarchy_top_title	IEEE transactions on image processing
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008
title	Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images
ctrlnum	(DE-627)OLC1959239325 (DE-599)GBVOLC1959239325 (PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0 (KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf
title_full	Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images
author_sort	Scharfenberger, Christian
journal	IEEE transactions on image processing
journalStr	IEEE transactions on image processing
lang_code	eng
isOA_bool	false
dewey-hundreds	000 - Computer science, information & general works 600 - Technology
recordtype	marc
publishDateSort	2015
contenttype_str_mv	txt
container_start_page	457
author_browse	Scharfenberger, Christian
container_volume	24
class	004 620 DNB 54.00 bkl
format_se	Aufsätze
author-letter	Scharfenberger, Christian
doi_str_mv	10.1109/TIP.2014.2380351
dewey-full	004 620
title_sort	structure-guided statistical textural distinctiveness for salient region detection in natural images
title_auth	Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images
abstract	We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches.
abstractGer	We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches.
abstract_unstemmed	We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318
container_issue	1
title_short	Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images
url	http://dx.doi.org/10.1109/TIP.2014.2380351 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068 http://www.ncbi.nlm.nih.gov/pubmed/25695960 http://search.proquest.com/docview/1640798317
remote_bool	false
author2	Wong, Alexander Clausi, David A
author2Str	Wong, Alexander Clausi, David A
ppnlink	131074458
mediatype_str_mv	n
isOA_txt	false
hochschulschrift_bool	false
author2_role	oth oth
doi_str	10.1109/TIP.2014.2380351
up_date	2024-07-03T16:25:19.777Z
_version_	1803575801747079170
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1959239325</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714150844.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx \|\|\|\|\| 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/TIP.2014.2380351</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1959239325</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1959239325</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Scharfenberger, Christian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multilayer approach</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image resolution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">saliency maps</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Atomic layer deposition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">structured image elements</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">natural images</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computational modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">performance evaluation metrics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image texture</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistical textural distinctiveness</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">probability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">salient region detection approach</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">statistical textural distinctiveness matrix</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">structure</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">public data sets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">feature extraction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">salient region detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">edge detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sparse texture modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">statistical analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image color analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image segmentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">rotational-invariant neighborhood-based textural representations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">structure-guided statistical textural distinctiveness approach</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">representative texture atom pairs</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">occurrence probability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image representation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wong, Alexander</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Clausi, David A</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">IEEE transactions on image processing</subfield><subfield code="d">New York, NY : Inst., 1992</subfield><subfield code="g">24(2015), 1, Seite 457-470</subfield><subfield code="w">(DE-627)131074458</subfield><subfield code="w">(DE-600)1111265-7</subfield><subfield code="w">(DE-576)029165008</subfield><subfield code="x">1057-7149</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:24</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:457-470</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1109/TIP.2014.2380351</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.ncbi.nlm.nih.gov/pubmed/25695960</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1640798317</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4318</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">24</subfield><subfield code="j">2015</subfield><subfield code="e">1</subfield><subfield code="h">457-470</subfield></datafield></record></collection>
score	7.4006376

Nicht das Richtige dabei?

Schreiben Sie uns!

Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?