Hierarchical neural networks for image interpretation:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin u.a.
Springer
2003
|
| Schriftenreihe: | Lecture notes in computer science
2766 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XII, 224 S. Ill., graph. Darst. |
| ISBN: | 3540407227 |
Internformat
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| 100 | 1 | |a Behnke, Sven |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Hierarchical neural networks for image interpretation |c Sven Behnke |
| 264 | 1 | |a Berlin u.a. |b Springer |c 2003 | |
| 300 | |a XII, 224 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Lecture notes in computer science |v 2766 | |
| 502 | |a Zugl.: Berlin, Freie Univ., Diss., 2002 | ||
| 650 | 7 | |a Beeldverwerking |2 gtt | |
| 650 | 7 | |a Neurale netwerken |2 gtt | |
| 650 | 7 | |a Réseau neuronal (Informatique) |2 rasuqam | |
| 650 | 4 | |a Réseaux neuronaux (Informatique) | |
| 650 | 4 | |a Traitement d'images - Techniques numériques | |
| 650 | 7 | |a Traitement numérique de l'image |2 rasuqam | |
| 650 | 7 | |a Vision artificielle |2 rasuqam | |
| 650 | 4 | |a Vision par ordinateur | |
| 650 | 4 | |a Computer vision | |
| 650 | 4 | |a Image processing |x Digital techniques | |
| 650 | 4 | |a Neural networks (Computer science) | |
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Datensatz im Suchindex
| _version_ | 1820874925559250944 |
|---|---|
| adam_text |
TABLE
OF
CONTENTS
FOREWORD
.
V
PREFACE
.
VII
1.
INTRODUCTION
.
1
1.1
MOTIVATION
.
1
1.1.1
IMPORTANCE
OFVISUAL
PERCEPTION.
1
1.1.2
PERFORMANCE
OF
THE
HUMANVISUAL
SYSTEM
.
2
1.1.3
LIMITATIONS
OF
CURRENT
COMPUTERVISION
SYSTEMS
.
6
1.1.4
ITERATIVE
INTERPRETATION
-
LOCAL
INTERACTIONS
IN
A
HIERARCHY
.
9
1.2
ORGANIZATION
OF
THE
THESIS
.
1
1
1.3
CONTRIBUTIONS
.
1
2
PART
I.
THEORY
2.
NEUROBIOLOGICAL
BACKGROUND
.
17
2.1
VISUAL
PATHWAYS.
1
7
2.2
FEATURE
MAPS
.
2
2
2.3
LAYERS
.
2
3
2.4
NEURONS
.
2
6
2.5
SYNAPSES.
2
8
2.6
DISCUSSION
.
3
0
2.7
CONCLUSIONS
.
3
3
3.
RELATED
WORK
.
35
3.1
HIERARCHICAL
IMAGE
MODELS
.
3
5
3.1.1
GENERIC
SIGNAL
DECOMPOSITIONS
.
3
5
3.1.2
NEURAL
NETWORKS.
4
1
3.1.3
GENERATIVE
STATISTICAL
MODELS
.
4
6
3.2
RECURRENT
MODELS
.
5
0
3.2.1
MODELS
WITH
LATERAL
INTERACTIONS
.
5
1
3.2.2
MODELS
WITHVERTICAL
FEEDBACK
.
5
6
3.2.3
MODELS
WITH
LATERAL
ANDVERTICAL
FEEDBACK.
6
0
3.3
CONCLUSIONS
.
6
3
X
TABLE
OF
CONTENTS
4.
NEURAL
ABSTRACTION
PYRAMID
ARCHITECTURE
.
65
4.1
OVERVIEW
.
6
5
4.1.1
HIERARCHICAL
NETWORK
STRUCTURE
.
6
5
4.1.2
DISTRIBUTED
REPRESENTATIONS
.
6
7
4.1.3
LOCAL
RECURRENT
CONNECTIVITY
.
6
9
4.1.4
ITERATIVE
REFINEMENT
.
7
0
4.2
FORMAL
DESCRIPTION
.
7
1
4.2.1
SIMPLE
PROCESSING
ELEMENTS
.
7
1
4.2.2
SHARED
WEIGHTS
.
7
3
4.2.3
DISCRETE-TIME
COMPUTATION
.
7
5
4.2.4
VARIOUS
TRANSFER
FUNCTIONS
.
7
6
4.3
EXAMPLE
NETWORKS
.
7
9
4.3.1
LOCAL
CONTRAST
NORMALIZATION
.
7
9
4.3.2
BINARIZATION
OF
HANDWRITING
.
8
2
4.3.3
ACTIVITY-DRIVEN
UPDATE
.
8
9
4.3.4
INVARIANT
FEATURE
EXTRACTION
.
9
1
4.4
CONCLUSIONS
.
9
4
5.
UNSUPERVISED
LEARNING
.
95
5.1
INTRODUCTION
.
9
5
5.2
LEARNING
A
HIERARCHY
OF
SPARSE
FEATURES
.
9
9
5.2.1
NETWORKARCHITECTURE.1
0
0
5.2.2
INITIALIZATION
.1
0
1
5.2.3
HEBBIAN
WEIGHT
UPDATE.1
0
2
5.2.4
COMPETITION
.1
0
3
5.3
LEARNING
HIERARCHICAL
DIGIT
FEATURES
.1
0
4
5.4
DIGIT
CLASSIFICATION
.1
0
8
5.5
DISCUSSION
.1
0
9
6.
SUPERVISED
LEARNING
.
111
6.1
INTRODUCTION
.1
1
1
6.1.1
NEAREST
NEIGHBOR
CLASSIFIER.1
1
1
6.1.2
DECISION
TREES
.1
1
2
6.1.3
BAYESIAN
CLASSIFIER
.1
1
2
6.1.4
SUPPORTVECTOR
MACHINES.1
1
3
6.1.5
BIAS/VARIANCE
DILEMMA
.1
1
3
6.2
FEED-FORWARD
NEURAL
NETWORKS.1
1
4
6.2.1
ERROR
BACKPROPAGATION.1
1
4
6.2.2
IMPROVEMENTS
TO
BACKPROPAGATION.1
1
6
6.2.3
REGULARIZATION.1
1
9
6.3
RECURRENT
NEURAL
NETWORKS
.1
2
0
6.3.1
BACKPROPAGATION
THROUGH
TIME.1
2
0
6.3.2
REAL-TIME
RECURRENT
LEARNING
.1
2
2
6.3.3
DIFFICULTY
OF
LEARNING
LONG-TERM
DEPENDENCIES
.1
2
2
6.3.4
RANDOM
RECURRENT
NETWORKS
WITH
FADING
MEMORIES
.1
2
4
6.3.5
ROBUST
GRADIENT
DESCENT
.1
2
5
6.4
CONCLUSIONS
.1
2
6
TABLE
OF
CONTENTS
XI
PART
II.
APPLICATIONS
7.
RECOGNITION
OF
METER
VALUES
.
129
7.1
INTRODUCTION
TO
METERVALUE
RECOGNITION
.1
2
9
7.2
SWEDISH
POST
DATABASE
.1
3
0
7.3
PREPROCESSING.1
3
1
7.3.1
FILTERING
.1
3
1
7.3.2
NORMALIZATION
.1
3
4
7.4
BLOCK
CLASSIFICATION
.1
3
6
7.4.1
NETWORKARCHITECTURE
AND
TRAINING
.1
3
7
7.4.2
EXPERIMENTAL
RESULTS
.1
3
8
7.5
DIGIT
RECOGNITION
.1
4
0
7.5.1
DIGIT
PREPROCESSING
.1
4
0
7.5.2
DIGIT
CLASSIFICATION.1
4
3
7.5.3
COMBINATION
WITH
BLOCK
RECOGNITION
.1
4
4
7.6
CONCLUSIONS
.1
4
6
8.
BINARIZATION
OF
MATRIX
CODES
.
149
8.1
INTRODUCTION
TO
TWO-DIMENSIONAL
CODES
.1
4
9
8.2
CANADA
POST
DATABASE.1
5
0
8.3
ADAPTIVE
THRESHOLD
BINARIZATION
.1
5
1
8.4
IMAGE
DEGRADATION
.1
5
3
8.5
LEARNING
BINARIZATION
.1
5
4
8.6
EXPERIMENTAL
RESULTS
.1
5
6
8.7
CONCLUSIONS
.1
6
4
9.
LEARNING
ITERATIVE
IMAGE
RECONSTRUCTION
.
167
9.1
INTRODUCTION
TO
IMAGE
RECONSTRUCTION.1
6
7
9.2
SUPER-RESOLUTION
.1
6
8
9.2.1
NIST
DIGITS
DATASET
.1
6
9
9.2.2
ARCHITECTURE
FOR
SUPER-RESOLUTION
.1
7
0
9.2.3
EXPERIMENTAL
RESULTS
.1
7
1
9.3
FILLING-IN
OCCLUSIONS.1
7
4
9.3.1
MNIST
DATASET.1
7
5
9.3.2
ARCHITECTURE
FOR
FILLING-IN
OF
OCCLUSIONS
.1
7
6
9.3.3
EXPERIMENTAL
RESULTS
.1
7
7
9.4
NOISE
REMOVAL
AND
CONTRAST
ENHANCEMENT
.1
7
9
9.4.1
IMAGE
DEGRADATION
.1
8
0
9.4.2
EXPERIMENTAL
RESULTS
.1
8
1
9.5
RECONSTRUCTION
FROM
A
SEQUENCE
OF
DEGRADED
DIGITS
.1
8
3
9.5.1
IMAGE
DEGRADATION
.1
8
4
9.5.2
EXPERIMENTAL
RESULTS
.1
8
5
9.6
CONCLUSIONS
.1
8
9
XII
TABLE
OF
CONTENTS
10.
FACE
LOCALIZATION
.
191
10.1
INTRODUCTION
TO
FACE
LOCALIZATION
.1
9
1
10.2
FACE
DATABASE
AND
PREPROCESSING
.1
9
3
10.3
NETWORKARCHITECTURE
.1
9
5
10.4
EXPERIMENTAL
RESULTS
.1
9
6
10.5
CONCLUSIONS
.2
0
2
11.
SUMMARY
AND
CONCLUSIONS
.
203
11.1
SHORT
SUMMARY
OF
CONTRIBUTIONS
.2
0
3
11.2
CONCLUSIONS
.2
0
4
11.3
FUTURE
WORK
.2
0
5
11.3.1
IMPLEMENTATION
OPTIONS
.2
0
5
11.3.2
USING
MORE
COMPLEX
PROCESSING
ELEMENTS
.2
0
6
11.3.3
INTEGRATION
INTO
COMPLETE
SYSTEMS.2
0
7
REFERENCES
.
209
INDEX
.
221 |
| any_adam_object | 1 |
| author | Behnke, Sven |
| author_facet | Behnke, Sven |
| author_role | aut |
| author_sort | Behnke, Sven |
| author_variant | s b sb |
| building | Verbundindex |
| bvnumber | BV017410757 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76 TA1634 |
| callnumber-search | QA76 TA1634 |
| callnumber-sort | QA 276 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | ST 301 |
| classification_tum | DAT 770d DAT 717d DAT 760d DAT 708d |
| ctrlnum | (OCoLC)723146177 (DE-599)BVBBV017410757 |
| dewey-full | 006.3/7 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/7 |
| dewey-search | 006.3/7 |
| dewey-sort | 16.3 17 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV017410757 |
| illustrated | Illustrated |
| indexdate | 2025-01-10T15:07:29Z |
| institution | BVB |
| isbn | 3540407227 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-010490268 |
| oclc_num | 723146177 |
| open_access_boolean | |
| owner | DE-384 DE-29T DE-91G DE-BY-TUM DE-739 DE-706 DE-11 DE-188 |
| owner_facet | DE-384 DE-29T DE-91G DE-BY-TUM DE-739 DE-706 DE-11 DE-188 |
| physical | XII, 224 S. Ill., graph. Darst. |
| publishDate | 2003 |
| publishDateSearch | 2003 |
| publishDateSort | 2003 |
| publisher | Springer |
| record_format | marc |
| series | Lecture notes in computer science |
| series2 | Lecture notes in computer science |
| spelling | Behnke, Sven Verfasser aut Hierarchical neural networks for image interpretation Sven Behnke Berlin u.a. Springer 2003 XII, 224 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Lecture notes in computer science 2766 Zugl.: Berlin, Freie Univ., Diss., 2002 Beeldverwerking gtt Neurale netwerken gtt Réseau neuronal (Informatique) rasuqam Réseaux neuronaux (Informatique) Traitement d'images - Techniques numériques Traitement numérique de l'image rasuqam Vision artificielle rasuqam Vision par ordinateur Computer vision Image processing Digital techniques Neural networks (Computer science) Bildverstehen (DE-588)4202022-0 gnd rswk-swf Hierarchisches System (DE-588)4159833-7 gnd rswk-swf Visuelles System (DE-588)4134101-6 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Bilderkennung (DE-588)4264283-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Bildverstehen (DE-588)4202022-0 s Bilderkennung (DE-588)4264283-8 s Maschinelles Lernen (DE-588)4193754-5 s Neuronales Netz (DE-588)4226127-2 s Visuelles System (DE-588)4134101-6 s Hierarchisches System (DE-588)4159833-7 s DE-188 Lecture notes in computer science 2766 (DE-604)BV000000607 2766 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010490268&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Behnke, Sven Hierarchical neural networks for image interpretation Lecture notes in computer science Beeldverwerking gtt Neurale netwerken gtt Réseau neuronal (Informatique) rasuqam Réseaux neuronaux (Informatique) Traitement d'images - Techniques numériques Traitement numérique de l'image rasuqam Vision artificielle rasuqam Vision par ordinateur Computer vision Image processing Digital techniques Neural networks (Computer science) Bildverstehen (DE-588)4202022-0 gnd Hierarchisches System (DE-588)4159833-7 gnd Visuelles System (DE-588)4134101-6 gnd Neuronales Netz (DE-588)4226127-2 gnd Bilderkennung (DE-588)4264283-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4202022-0 (DE-588)4159833-7 (DE-588)4134101-6 (DE-588)4226127-2 (DE-588)4264283-8 (DE-588)4193754-5 (DE-588)4113937-9 |
| title | Hierarchical neural networks for image interpretation |
| title_auth | Hierarchical neural networks for image interpretation |
| title_exact_search | Hierarchical neural networks for image interpretation |
| title_full | Hierarchical neural networks for image interpretation Sven Behnke |
| title_fullStr | Hierarchical neural networks for image interpretation Sven Behnke |
| title_full_unstemmed | Hierarchical neural networks for image interpretation Sven Behnke |
| title_short | Hierarchical neural networks for image interpretation |
| title_sort | hierarchical neural networks for image interpretation |
| topic | Beeldverwerking gtt Neurale netwerken gtt Réseau neuronal (Informatique) rasuqam Réseaux neuronaux (Informatique) Traitement d'images - Techniques numériques Traitement numérique de l'image rasuqam Vision artificielle rasuqam Vision par ordinateur Computer vision Image processing Digital techniques Neural networks (Computer science) Bildverstehen (DE-588)4202022-0 gnd Hierarchisches System (DE-588)4159833-7 gnd Visuelles System (DE-588)4134101-6 gnd Neuronales Netz (DE-588)4226127-2 gnd Bilderkennung (DE-588)4264283-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Beeldverwerking Neurale netwerken Réseau neuronal (Informatique) Réseaux neuronaux (Informatique) Traitement d'images - Techniques numériques Traitement numérique de l'image Vision artificielle Vision par ordinateur Computer vision Image processing Digital techniques Neural networks (Computer science) Bildverstehen Hierarchisches System Visuelles System Neuronales Netz Bilderkennung Maschinelles Lernen Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010490268&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000000607 |
| work_keys_str_mv | AT behnkesven hierarchicalneuralnetworksforimageinterpretation |