Verfügbarkeit: Emerging trends in image processing, computer vision and pattern recognition

Emerging trends in image processing, computer vision and pattern recognition:

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Bibliographische Detailangaben
Beteilige Person:	Deligiannidis, Leonidas (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Amsterdam [u.a.] Morgan Kaufmann 2015
Schlagwörter:	Image processing / Digital techniques Computer vision Optical pattern recognition Bildverarbeitung Mustererkennung Computervisualistik
Links:	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
Umfang:	XXXV, 609 S. Ill.
ISBN:	9780128020456

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MARC


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245	1	0	\|a Emerging trends in image processing, computer vision and pattern recognition \|c ed. by Leonidas Deligiannidis ; Hamid R. Arabnia
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Datensatz im Suchindex

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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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ctrlnum	(OCoLC)906708309 (DE-599)BVBBV042363200
discipline	Informatik
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id	DE-604.BV042363200
illustrated	Illustrated
indexdate	2024-12-20T17:09:14Z
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

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