Emerging trends in image processing, computer vision and pattern recognition
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100 | 1 | |a Deligiannidis, Leonidas |e Verfasser |0 (DE-588)1068554029 |4 aut | |
245 | 1 | 0 | |a Emerging trends in image processing, computer vision and pattern recognition |c ed. by Leonidas Deligiannidis ; Hamid R. Arabnia |
264 | 1 | |a Amsterdam [u.a.] |b Morgan Kaufmann |c 2015 | |
300 | |a XXXV, 609 S. |b Ill. | ||
336 | |b txt |2 rdacontent | ||
337 | |b n |2 rdamedia | ||
338 | |b nc |2 rdacarrier | ||
650 | 4 | |a Image processing / Digital techniques | |
650 | 4 | |a Computer vision | |
650 | 4 | |a Optical pattern recognition | |
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650 | 0 | 7 | |a Mustererkennung |0 (DE-588)4040936-3 |2 gnd |9 rswk-swf |
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689 | 0 | 1 | |a Computervisualistik |0 (DE-588)7535806-2 |D s |
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adam_text | Contents
Contributors
............................................................................................................xxi
Acknowledgments
................................................................................................xxix
Preface
..................................................................................................................xxxi
Introduction
..........................................................................................................xxxv
PART
1
IMAGE AND SIGNAL PROCESSING
________________
CHAPTER
1
Denoising camera data: Shape-adaptive noise
reduction for color filter array image data
..................
з
1
Introduction
.....................................................................................3
2
Camera Noise
..................................................................................4
3
Adaptive Raw Data Denoising
.......................................................6
3.1
Luminance Transformation of Bayer Data
.............................6
3.2
LPA-ICI for Neighborhood Estimation
..................................7
3.3
Shape-Adaptive DCT and Denoising via Hard Thresholding....?
4
Experiments: Image Quality vs System Performance
...................8
4.1
Visual Quality of Denoising Results
.......................................9
4.2
Processing Real Camera Data
...............................................10
5
Video Sequences
...........................................................................14
5.1
Implementation Aspects
........................................................
5
6
Conclusion
....................................................................................
5
References
...........................................................................................16
CHAPTER
2
An approach to classifying four-part music
in multidimensional space
.........................................19
1
Introduction
...................................................................................19
1.1 Related Work
.........................................................................19
1.2
Explanation of Musical Terms
..............................................19
2
Collecting the Pieces
—
Training and Test Pieces
........................20
2.1
Downloading and Converting Files
......................................21
2.2
Formatting the MusicXML
...................................................21
3
Parsing MusicXML
—
Training and Test Pieces
..........................23
3.1
Reading in Key and Divisions
..............................................24
3.2
Reading in Notes
...................................................................24
3.3
Handling Note Values
...........................................................25
3.4
Results
....................................................................................26
4
Collecting Piece Statistics
............................................................26
4.1
Metrics
...................................................................................26
ví
Contents
5
Collecting Classifier Statistics
—
Training Pieces Only
...............28
5.1
Approach
................................................................................29
6
Classifying Test Pieces
.................................................................29
6.1
Classification Techniques
......................................................30
6.2
User Interface
........................................................................31
6.3
Classification Steps
................................................................31
6.4
Testing the Classification Techniques
..................................32
6.5
Classifying from Among Two Composers
...........................32
6.6
Classifying from Among Three Composers
.........................33
6.7
Selecting the Best Metrics
.....................................................33
7
Additional Composer and Metrics
...............................................34
7.1
Lowell Mason
........................................................................34
7.2
Additional Metrics
.................................................................36
8
Conclusions
...................................................................................37
References
...........................................................................................37
Further Reading
...................................................................................37
CHAPTER
3
Measuring rainbow trout by using simple statistics
39
1
Introduction
...................................................................................39
2
Experimentai
Prototype
................................................................40
2.1
Canalization System
..............................................................41
2.2
Illumination System
...............................................................41
2.3
Vision System
........................................................................42
3
Statistical Measuring Approach
...................................................42
4
Experimental Framework
................................................ ...43
4.1
Testing Procedure
..................................................................44
5
Performance Evaluation
...............................................................48
β
Conclusions
..................................... 52
Acknowledgments
......................................
52
References
.............................................
53
CHAPTER
4
Fringe noise removal of retinal
fundus
images using trimming regions
........................... 55
1
Introduction
..................................... ......
I.I Image Processing
......................................
56
I
-2
Retinal Image Processing
.... .................
2
Methodology
....................................... * * ...............................
2.1
Implementáljon
..................................... ........................
3
Results and Discussion..
.............................
4
Conclusion
............. .........................................
^
References
........ ...........................................................
62
.......................................................-.........................63
Contents
vii
CHAPTER
5
pSQ: Image quantizer based on contrast
band-pass filtering
......................................................67
1
Introduction
...................................................................................67
2
Related Work: JPEG 2(XX) Global Visual Frequency
Weighting
......................................................................................68
3
Perceptual Quantization
................................................................68
3.1
Contrast Band-Pass Filtering
.................................................68
3.2
Forward Inverse Quantization
...............................................69
3.3
Perceptual Inverse Quantization
............................................73
4
Experimental Results
....................................................................74
4.1
Based on Histogram
..............................................................74
4.2
Correlation Analysis
..............................................................74
5
Conclusions
...................................................................................78
Acknowledgment
................................................................................85
References
...........................................................................................85
CHAPTER
6
Rebuilding IVUS images from raw data of the RF
signal exported by IVUS equipment
...........................87
1
Introduction
...................................................................................87
2
Method for IVUS Image Reconstruction
.....................................88
2.1
RF Dataset
.............................................................................89
2.2
Band-Pass Filter
.....................................................................90
2.3
Time Gain Compensation
......................................................
9()
2.4
Signal Envelope
.....................................................................92
2.5
Log-Compression
...................................................................93
2.6
Digital Development Process
................................................93
2.7
Postprocessing
........................................................................93
3
Experimental Results
....................................................................94
4
Discussion, Conclusion, and Future Work
...................................95
Acknowledgments
...............................................................................96
References
...........................................................................................96
CHAPTER
7
XSET: Image coder based on contrast band-pass
filtering
........................................................................99
1
Introduction
...................................................................................99
2
Related Work: JPEG2000 Global Visual Frequency
Weighting
....................................................................................1«)
3
Image Entropy Encoding: Xsel Algorithm
................................101
3.1
Perceptual Quantization
.......................................................
H>!
3.2
Startup Considerations
.........................................................102
3.3
Coding Algorithm
................................................................105
viii Contents
f
ryj
4 Experiments
and Results
............................................................
1У}
113
5 Conclusions
.................................................................................
Acknowledgment..............................................................................
References
.........................................................................................
CHAPTER
8
Security surveillance applications utilizing
parallel video-processing techniques in the
spatial domain
..........................................................
117
1
Introduction
.................................................................................
117
2
Graphical Processing Unit and Compute Unified Device
Architecture
................................................................................
3
Parallel Aigorithms for image Processing
.................................
Π
9
4
Applications for Surveillance Using Parallel Video Processing..
121
4.1
Motion Detector
...................................................................122
4.2
Overa
Line Motion Detector
..............................................123
4.3
Line Crossing Detector
..................-.....................................124
4.4
Area Motion Detector
..........................................................127
4.5
Fire Detection
........,.............................................................127
5
Conclusion
..................................................................................128
Acknowledgments
.............................................................................128
References
.........................................................................................128
CHAPTER
9
Highlight image filter significantly improves
optical character recognition on text images
.........131
1
Introduction
.................................................................................131
I.I Properties of Highlight Image Filter
...................................132
2
Description of Smart Contrast Image Filter
...............................132
2.1
Contrast Image Filter
...........................................................133
2.2
New Image Filter: Smart Contrast
......................................135
2.3
Visual Result of Applying Smart Contrast on Images
.......136
3
Description of Highlight Image Filter
........................................137
3.1
Description of the Image Filters Visual Effects That
Are Included in Highlight s Visual Effect
..........................137
3.2
New Image Filter: Highlight
...............................................138
3.3
Visual Results of Applying Highlight Filter on Images
.....142
3.4
Highlight Image Filter Program Code and Visual
Representation
....................................................
4
Description of the Optimized Implementation of Smart
Contrast and Highlight Using Byte Buffer Techniques
.........143
5
Conclusions
..............................................
Nomenclature
.......................................
References
............................................ 147
Contents ix
CHAPTER
10
A study on the relationship between depth map
quality and stereoscopic image quality using
upsampled depth maps
.............................................149
1
Introduction
.................................................................................149
2
Objective Quality Assessment Tools
.........................................151
2.1 FR IQA
Tools
......................................................................152
2.2
NR IQA Tools
.....................................................................153
3 3D
Subjective Quality Assessment
............................................154
4
Experimental Results
..................................................................154
5
Conclusion
..................................................................................159
References
.........................................................................................159
CHAPTER
11
pGBbBShift: Method for introducing
perceptual criteria to region of interest
coding
.......................................................................161
1
Introduction
.................................................................................161
2
Related Work
..............................................................................163
2.1
BbB Shift
.............................................................................163
2.2
GBbBShiľt
............................................................................165
3
Perceptual GBbBShift
................................................................166
3.1
Quantization
.........................................................................166
3.2
pGBbBShift Algorithm
........................................................167
4
Experimental Results
..................................................................168
4.1
Application in Weil-Known Test Images
...........................168
4.2
Application in Other Image Compression Fields
...............179
5
Conclusions
.................................................................................180
Acknowledgment
..............................................................................181
References
.........................................................................................181
CHAPTER
12
DT-Binarize: A decision tree based binarization
for protein images
....................................................
івз
1
Introduction
.................................................................................183
2
Background
.................................................................................185
2.1
Image Binari/ation Methods
...............................................185
3
DT-Binarize: Selection of Best Binarization Method Using
Decision Tree
..............................................................................188
3.1
Overview
..............................................................................188
3.2
Stages of the Algorithm
......................................................188
3.3
Application of DT-Binarize on Protein Crystal Images
.......190
4
Hxperiments and Results
............................................................191
4.1
Dataset
.................................................................................191
4.2
Correctness Measurement
....................................................193
4.3
Results
..................................................................................195
Contents
198
5
Conclusion
................................................................................
Acknowledgment
..............................................................................
_ _ ....................
19У
References
...................................................................
CHAPTER
13
Automatic mass segmentation method
in
mammograms
based on improved VFC
snake model
..............................................................
201
1
Introduction
.................................................................................
ZUi
2
Methodology
...............................................................................
202
2.1
Mammogram
Database
........................................................202
2.2
Mammogram
Preprocessing
................................................203
2.3
ROI
Extraction and Location
..............................................204
2.4
Mass Segmentation
..............................................................207
3
Experiment Results and Discussion
...........................................209
3.1
Experiments Results
............................................................212
3.2
Algorithm Performance Analysis
........................................212
4
Conclusions
.................................................................................215
Acknowledgments
.............................................................................216
References
.........................................................................................216
CHAPTER
14
Correction of intensity nonuniformity in breast
MR images
................................................................219
1
Introduction
.................................................................................219
2
Preprocessing Steps
....................................................................220
2.1
Noise Reduction
..................................................................220
2.2
Bias Field Reduction
...........................................................221
2.3
Locally Normalization Step
................................................222
2.4
Hybrid Method for Bias Field Correction
...........................222
3
Experimentai
Results
..................................................................226
4
Conclusion
..................................................................................227
Acknowledgments
.............................................................................228
References
.........................................................................................228
CHAPTER
15
Traffic control by digital imaging cameras
.............231
1
Introduction
............................................................. 231
2
Paper Overview
.......................................................... 232
3
Implementation
................................................... 232
4
Traffic Detectors
............................................... 233
4.1
Induction Loops
.................................................. 233
4.2
Microwave Radar
...................................... 234
4.3
Infrared Sensors
.............................................. 234
4.4
Video Detection
............................................... 235
Contents xi
5 Image Processing........................................................................236
5.1 Basic
Types
oí
Images........................................................237
6
Project
Design.............................................................................238
6.1 Red-Lighl
Violation
.............................................................240
6.2 Speed
Violation
...................................................................241
6.3
Plate Numbers Recognition
.................................................242
7
Performance Analysis
.................................................................243
7.1
Speed Violation
...................................................................243
7.2
Red Violation
.......................................................................244
7.3
Plate Position Determination
...............................................244
8
General Conclusion
....................................................................246
8.1
Problems
..............................................................................246
8.2
Future Work
.........................................................................246
References
.........................................................................................246
CHAPTER
16
Night color image enhancement via statistical
law and retinex
.........................................................249
1
Introduction
.................................................................................249
2
Overview of Retinex Theory
......................................................250
2.1
The Basic Idea of Retinex Theory
......................................250
2.2
The Halo Effect
................................................................250
3
Analyzing the Transformation Law and Enhancing the
Nighttime Image
.........................................................................250
4
Comparison and Results
.............................................................254
5
Application
..................................................................................259
6
The Conclusion
...........................................................................259
References
.........................................................................................260
PART
2
COMPUTER VISION AND RECOGNITION
SYSTEMS
_____________________________________
CHAPTER
17
Trajectory evaluation and behavioral scoring
using JAABA in a noisy system
................................265
1
Introduction
.................................................................................265
2
Methods
.......................................................................................266
2.1
ML in JAABA and Trajectory Scoring
...............................268
3
Results
.........................................................................................269
4
Discussion
...................................................................................273
Acknowledgments
.............................................................................275
References
.........................................................................................275
xii Contents
CHAPTER
18 An
algorithm for mobile vision-based localization
of skewed nutrition labels that maximizes
specificity
.................................................................
277
1
Introduction
.................................................................................277
2
Previous Work
............................................................................
278
3
Skewed NL Localization
............................................................279
3.1
Detection of Edges, Lines, and Corners
.............................279
3.2
Comer Detection and Analysis
...........................................282
3.3
Selection of Boundary Lines
...............................................283
3.4
Finding Intersections in Cartesian Space
............................284
4
Experiments
................................................................................286
4.1
Complete and Partial True Positives
...................................286
4.2
Results
..................................................................................288
4.3
Limitations
...........................................................................289
5
Conclusions
.................................................................................290
References
.........................................................................................292
CHAPTER
19
A rough fuzzy neural network approach for
robust face detection and tracking
..........................295
1
Introduction
.................................................................................295
2
Theoretical Background
.............................................................297
3
Face-Detection Method
..............................................................298
3.1
The Proposed Multiscale Method
.......................................300
3.2
Clustering Subnetwork
........................................................301
4
Skin Map Segmentation
.............................................................304
4.1
Skin Map Segmentation Results
.........................................304
5
Face Detection
.......................................................
305
β
Face Tracking
................................................
3q^
7
Experiments
...................
7.
1 Face-Detection Experiment
.307
s
.....................................................307
7.2
Face-Tracking Experiments
.................................................310
β
Conclusions and Future Works
..................................................312
Acknowledgments
.............................................................................312
References
.........................................................................................313
CHAPTER
20
A content-based image retrieval approach
based on document queries
.....................................315
1
Introduction
.................................................................................315
2
Related Work
..............................................................................316
3
Our Approach
.............................................................................317
Contents xiii
4
Experimental
Setup.....................................................................322
5
Future
Research..........................................................................327
Acknowledgments
.............................................................................328
References
.........................................................................................328
CHAPTER
21
Optical flow-based representation for video
action detection
........................................................331
1
Introduction
.................................................................................331
2
Related Work
..............................................................................332
3
Temporal Segment Representation
............................................334
4
Optical Flow
...............................................................................336
4.1
Derivation of Optical Flow
.................................................337
4.2
Algorithms
...........................................................................338
5
Optical Flow-Based Segment Representation
............................339
5.1
Optical Flow Estimation
......................................................339
5.2
Proposed Representation
.....................................................341
6
Cut Detection Inspiration
...........................................................344
7
Experiments and Results
............................................................345
8
Conclusion
..................................................................................348
References
.........................................................................................349
CHAPTER
22
Anecdotes extraction from webpage context
as image annotation
.................................................353
1
Introduction
.................................................................................353
2
Literature Background
................................................................354
2.1
Automatic Image Annotation
..............................................354
2.2
Keyword Extraction
.............................................................354
2.3
Lexical Chain
.......................................................................355
3
Research Design
.........................................................................356
3.1
Research Model Overview
..................................................356
3.2
Chinese Lexical Chain Processing
......................................357
4
Evaluation
...................................................................................362
4.1
Evaluation of Primary Annotation
......................................362
4.2
Expert Evaluation of Secondary Annotation
......................362
4.3
User Evaluation of Secondary Annotation
.........................363
4.4
Results of Image Annotation
...............................................363
4.5
Performance Testing
............................................................364
5
Conclusion
..................................................................................365
Acknowledgments
.............................................................................365
References
.........................................................................................365
xiv Contents
CHAPTER
23 Automatic
estimation of a resected liver
region using a tumor domination ratio
369
1
Introduction
.................................................................................
2
Estimating an Ideal Resected Region Using the TDR
..............371
3
Estimating an Optimal Resected Region Under the Practical
Conditions in Surgery
.................................................................
J /4>
4
Modifying a Resected Region Considering Hepatic Veins
.......376
477
5
Conclusion
..................................................................................
J
References
.........................................................................................
378
CHAPTER
24
Gesture recognition in cooking video based
on image features and motion features using
Bayesian network classifier
.....................................379
1
Introduction
.................................................................................379
2
Related Work
..............................................................................381
3
Our Method
.................................................................................382
3.1
Our Recognition System Overview
....................................382
3.2
Preprocessing Input Data
.....................................................383
3.3
Image Feature Extraction
....................................................384
3.4
Motion Feature Extraction
...................................................385
3.5
BNs Training
.......................................................................385
4
Experiments
................................................................................387
4.1
Dataset
.................................................................................387
4.2
Parametersetting.................................................................
387
4.3
Results
..................................................................................388
5
Conclusions
.................................................................................390
Acknowledgments
.............................................................................391
References
.........................................................................................391
CHARTER
25
Biometrie
analysis for finger vein data:
Two-dimensional kernel principal component
analysis
.....................................................................
393
1
Introduction
............................................................ 393
2
Image Acquisition
...................................................... 394
3
Two-Dimensional Principal Component Analysis
.....................395
4
Kernel Mapping Along Row and Column Direction
.................396
4.1
Two-Dimensional KPCA
............................................... .396
4.2
Kernel Mapping in Row and Column Directions
and 2DPCA
........................................................
397
5
Finger Vein Recognition Algorithm
................................... 398
5.1
ROI
Extraction
.................................................
Contents xv
5.2 Image
Normalization...........................................................
399
5.3
Feature Extraction and Classification Method
....................399
6
Experimental Results on Finger Vein Database
........................399
6.1
Experimental Setup-1
..........................................................
4(X)
6.2
Experimental Setup-2
..........................................................
4(K)
7
Conclusion
..................................................................................404
References
.........................................................................................404
CHAPTER
26
A local feature-based facial expression
recognition system from depth video
.......................407
1
Introduction
.................................................................................407
2
Depth Image Preprocessing
........................................................408
3
Feature Extraction
.......................................................................408
3.1
LDP Features
.......................................................................410
3.2
PCA on LDP Features
.........................................................412
3.3
LDA on PCA Features
........................................................412
3.4
HMM
for Expression Modeling and Recognition
..............413
4
Experiments and Results
............................................................414
5
Concluding Remarks
..................................................................417
Acknowledgment
..............................................................................417
References
.........................................................................................417
CHAPTER
27
Automatic classification of protein crystal images
.421
1
Introduction
.................................................................................421
2
Image Categories
........................................................................422
3
System Overview
........................................................................423
4
Image Preprocessing and Feature Extraction
.............................424
4.1
Green Percentile Image Binari/.ation
..................................425
4.2
Region Features
...................................................................426
4.3
Edge Features
......................................................................426
4.4
Corner Features
....................................................................
42X
4.5
Hough Line Features
...........................................................428
5
Experimental Results
..................................................................428
6
Conclusion and Future Work
.....................................................430
Acknowledgment
..............................................................................431
References
.........................................................................................431
CHAPTER
28
Semi-automatic teeth segmentation in
3D
models
of dental casts using a hybrid methodology
433
1
Introduction
.................................................................................433
2
Dental Study Model
....................................................................434
2.1 3D
Model Acquisition
.........................................................434
xvi Contents
435
3 Point
Cloud Segmentation
..........................................................
3.1
RANSAC
.............................................................................
436
3 2
Region Growing Segmentation
...........................................
436
- 4^6
3.3
Min-Cut
................................................................................
4JO
3.4
Feature Sampling Using NARF
..........................................
437
3.5
The Hybrid Technique
.........................................................
438
4
Results of Segmentation Techniques Applied to
3D
Dental Models
.............................................................................
UQ
4.1
First, a Test Using RANSAC
..............................................440
4.2
Gum Extraction Using Region Growing
.............................441
4.3
Per-Tooth Separation Using Min-Cut
.................................441
4.4
Semi-Automatic Segmentation (Hybrid Technique)
..........442
5
Comments and Discussions
........................................................443
6
Conclusion
..................................................................................444
Acknowledgments
.............................................................................444
References
.........................................................................................444
CHAPTER
29
Effective finger vein-based authentication: Kernel
principal component analysis
..................................447
1
Introduction
.................................................................................447
2
Image Acquisition
.......................................................................448
3
Principal Component Analysis
...................................................449
4
Kernel Principal Component Analysis
.......................................449
4.1
KPCA Algorithm
.................................................................449
4.2
Kernel Feature Space Versus PC A Feature Space
.............450
5
Experimental Results
..................................................................451
6
Conclusion
..................................................................................454
References
.........................................................................................454
CHAPTER
30
Detecting distorted and benign blood cells using
the Hough transform based on neural networks
and decision trees
....................................................457
1
Introduction
.......................................................... 457
2
Related Work
.................................................................
І460
3
Hough Transforms
...............................................
460
4
Overview of
NN....................................................
46j
5
Overview of
lhe
Classification and Regression Tree
................462
β
The Proposed Algorithm
....................................................... ...463
7
The Experimental Results
..................................................
466
8
Conclusions
............................................ 47
j
References
............................................. 479
Contents xvii
PART
3
REGISTRATION, MATCHING, AND PATTERN
RECOGNITION
__________________________________
CHAPTER
31
Improving performance with different length
templates using both of correlation and absolute
difference on similar play estimation
......................477
1
Introduction
.................................................................................477
2
Structure of the Proposed Method
.............................................478
3
ID Degeneration from Videos
...................................................479
3.1
Motion Extraction from MPEG Videos and Construction
of Space-Time Image
..........................................................480
3.2
Motion Compensation Vectors from MPEG Videos
..........480
3.3
Space-Time Image
...............................................................480
3.4
Mulching Between Template ST Image and Retrieved
ST Image
..............................................................................482
4
Similarity Measure with Correlation and Absolute
Difference in Motion Retrieving Method
..................................482
4.1
Similarity Measure in Motion Space-Time Image
Based on Correlations
.........................................................482
4.2
Similarity Measure in Motion Space-Time Image
Based on Absolute Differences
...........................................483
5
Experiments on Baseball Games and Evaluations
.....................483
5.1
Baseball Game
.....................................................................483
5.2
Experimental Objects
..........................................................484
5.3
Experiment Process
.............................................................484
5.4
Correlation-Based Similarity Measure in Pitching
Retrieval
...............................................................................484
5.5
Absolute Difference Based Similarity Measure in
Pitching Retrieval
................................................................485
5.6
Combination Both of Correlations and Absolute
Differences
...........................................................................485
6
Conclusions
.................................................................................487
References
.........................................................................................487
CHAPTER
32
Surface registration by markers guided nonrigid
iterative closest points algorithm
............................489
1
Introduction
.................................................................................489
2
Materials and Methods
...............................................................490
3
Results
.........................................................................................492
4
Discussion and Conclusions
.......................................................492
Acknowledgment
..............................................................................495
References
.........................................................................................
-*97
xviii Contents
CHAPTER
33 An affine
shape constraint for geometric active
contours
....................................................................
1
Introduction
.................................................................................
2
Shape Alignment Using Fourier Descriptors
.............................500
2.1
Euclidean Shapes Alignment
...............................................
500
2.2
Affine
Shape Alignment
......................................................
502
2.3
Discussion
............................................................................
2.4
Global Matching Using
Affine
Invariants
Descriptors
...........................................................................
3
Shape Prior for Geometric Active Contours
..............................506
4
Experimental Results
..................................................................507
4.1
Robustness of the Proposed Shape Priors
...........................507
4.2
Application to Object Detection
.........................................508
5
Conclusions
.................................................................................513
References
.........................................................................................514
CHAPTER
34
A topological approach for detection of
chessboard patterns for camera calibration
...........517
1
Introduction
.................................................................................517
2
X-Corner Detector
......................................................................519
3
Topological Filter
.......................................................................520
4
Point Correspondences
...............................................................522
5
Location Refinement
..................................................................523
β
Experimental Results
..................................................................524
7
Conclusions
.................................................................................529
References
.........................................................................................529
CHAPTER
35
Precision distortion correction technique based
on FOV model for wide-angle cameras in
automotive sector
.....................................................533
1
Introduction
................................................................. 533
2
Related Research
..................................................................
534
3
Distortion Center Estimation Method Using FOV Model
and 2D Patterns
..................................................
536
3.1
Distortion Correction Method Considering Distortion
Center Estimation
...................................... 53g
3.2
FOV Distortion Model
....................... ..... .... 537
3.3
Distortion Coefficient Estimation of the FOV Model
........538
3.4
Distortion Center Estimation Method Using 2D
Patterns
...........................................
*~
„c
4
Experiment and Evaluation
.............................. .............
540
Contents xix
5
Application of Algorithm to Products Improving Vehicle
Convenience
................................................................................545
5.1
Rear View Camera
..............................................................546
5.2
Surround View Monitoring (SVM) System
........................546
6
Conclusion
..................................................................................547
Acknowledgments
.............................................................................548
References
.........................................................................................548
CHAPTER
36
Distances and kernels based on cumulative
distribution functions
................................................551
1
Introduction
.................................................................................551
2
Distance and Similarity Measures Between Distributions
........551
3
Distances on Cumulative Distribution Functions
......................553
4
Experimental Results and Discussions
.......................................556
5
Generalization
.............................................................................558
6
Conclusions and Future Work
....................................................559
References
.........................................................................................559
CHAPTER
37
Practical issues for binary code pattern
unwrapping in fringe projection method
..................561
1
Introduction
.................................................................................561
2
Prior and Related Work
..............................................................562
3
Practical Issues for Fringe Pattern Generation
..........................562
4
Binary Code Generation for Phase Ambiguity Resolution
.......566
5
Practical Issues for Projected Fringe Pattern Photography
.......567
6
Three-Dimensional Reconstruction
............................................569
6.1
How to Compute the Initial (Wrapped) Phase
...................569
6.2
How to Compute the Unwrapped Phase via Two
Previous Outcomes
..............................................................569
6.3
Noise Removal from Unwrapped Phase
.............................572
6.4
Compute Differential Phase
................................................572
6.5
Noise Removal from Differential Phase
.............................573
6.6
How to Make RGB Texture Image from Projected
Fringe
Paltem
Images
..........................................................574
6.7
Object Cropping
..................................................................575
6.8
Conven
Differential Phase to Depth and
3D
Visualization
........................................................................576
6.9
Accuracy Evaluation of
3D
Point Cloud
............................576
7
Summary and Conclusions
.........................................................579
References
.........................................................................................
xx Contents
CHAPTER
38
Detection and matching of object using proposed
signature
...................................................................583
1
Introduction
.................................................................................583
2
Overview on SURF Method
.......................................................584
3
Overview on Image Segmentation
.............................................586
4
The Proposed Algorithm
............................................................586
5
Experimental Results
..................................................................588
6
Conclusions
.................................................................................594
References
.........................................................................................595
Index
......................................................................................................................597
|
any_adam_object | 1 |
author | Deligiannidis, Leonidas |
author_GND | (DE-588)1068554029 |
author_facet | Deligiannidis, Leonidas |
author_role | aut |
author_sort | Deligiannidis, Leonidas |
author_variant | l d ld |
building | Verbundindex |
bvnumber | BV042363200 |
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discipline | Informatik |
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id | DE-604.BV042363200 |
illustrated | Illustrated |
indexdate | 2024-09-27T16:26:41Z |
institution | BVB |
isbn | 9780128020456 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027799613 |
oclc_num | 906708309 |
open_access_boolean | |
owner | DE-473 DE-BY-UBG DE-573 |
owner_facet | DE-473 DE-BY-UBG DE-573 |
physical | XXXV, 609 S. Ill. |
publishDate | 2015 |
publishDateSearch | 2015 |
publishDateSort | 2015 |
publisher | Morgan Kaufmann |
record_format | marc |
spellingShingle | Deligiannidis, Leonidas Emerging trends in image processing, computer vision and pattern recognition Image processing / Digital techniques Computer vision Optical pattern recognition Bildverarbeitung (DE-588)4006684-8 gnd Mustererkennung (DE-588)4040936-3 gnd Computervisualistik (DE-588)7535806-2 gnd |
subject_GND | (DE-588)4006684-8 (DE-588)4040936-3 (DE-588)7535806-2 |
title | Emerging trends in image processing, computer vision and pattern recognition |
title_auth | Emerging trends in image processing, computer vision and pattern recognition |
title_exact_search | Emerging trends in image processing, computer vision and pattern recognition |
title_full | Emerging trends in image processing, computer vision and pattern recognition ed. by Leonidas Deligiannidis ; Hamid R. Arabnia |
title_fullStr | Emerging trends in image processing, computer vision and pattern recognition ed. by Leonidas Deligiannidis ; Hamid R. Arabnia |
title_full_unstemmed | Emerging trends in image processing, computer vision and pattern recognition ed. by Leonidas Deligiannidis ; Hamid R. Arabnia |
title_short | Emerging trends in image processing, computer vision and pattern recognition |
title_sort | emerging trends in image processing computer vision and pattern recognition |
topic | Image processing / Digital techniques Computer vision Optical pattern recognition Bildverarbeitung (DE-588)4006684-8 gnd Mustererkennung (DE-588)4040936-3 gnd Computervisualistik (DE-588)7535806-2 gnd |
topic_facet | Image processing / Digital techniques Computer vision Optical pattern recognition Bildverarbeitung Mustererkennung Computervisualistik |
url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027799613&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
work_keys_str_mv | AT deligiannidisleonidas emergingtrendsinimageprocessingcomputervisionandpatternrecognition |