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Data Segmentation and Model Selection for Computer Vision
Data Segmentation and Model Selection for Computer Vision
Detailed Information
- Material Type
- 단행본
- ISBN
- 0-387-98815-7
- UDC
- 621.397.3
- DDC
- 621.367 A412d-23
- Callnumber
- 621.367 A412d
- Title/Author
- Data Segmentation and Model Selection for Computer Vision / Alireza Bab-Hadiashar, David Suter 편.
- Publish Info
- 미국 : Springer-Verlag, 2000
- Material Info
- 208p. ; 24cm
- Formatted Contents Note
- 완전내용ⅠHistorical Review부분내용1완전내용1 2D and 3D Scene Segmentation for Robotic Vision부분내용3완전내용1.1 Introduction부분내용3완전내용1.2 Binary Image Segmentation부분내용5완전내용1.3 2D Multitonal Image Segmentation부분내용5완전내용1.4 2½D Scene Segmentation부분내용12완전내용1.5 3D Scene Segmentation부분내용22완전내용1.6 Discussion and Comclusions부분내용26완전내용ⅡStatistical and Geometrical Foundations부분내용29완전내용2 Robust Regression Methods and Model Selection부분내용3100완전내용2.1 Introduction부분내용3111완전내용2.2 The Influence Function and the Breakdown Point부분내용3222완전내용2.3 Robust Estimation and Inference in Linear Models부분내용3433완전내용2.4 Robust Model Selection부분내용3744완전내용2.5 Conclusions부분내용4055완전내용3 Robust Measures of Evidence for Variable Selection부분내용4166완전내용3.1 Introduction부분내용4177완전내용3.2 The Akaike Information Griterion부분내용4288완전내용3.3 Model Selection Based on the Wald Test부분내용4599완전내용3.4 Gypothesis Testing and Measures of Evidence for Variable Selection부분내용5700완전내용3.5 Examples부분내용6911완전내용3.6 Recommendations부분내용8822완전내용4 Model Selection Criteria for Geometric Inference부분내용9133완전내용4.1 Introduction부분내용9144완전내용4.2 Classical Regression부분내용9355완전내용4.3 Geometric Line Fitting부분내용10066완전내용4.4 General Geometric Model Selection부분내용10577완전내용4.5 Gemetric Cρ부분내용10788완전내용4.6 Bayesian Approaches부분내용10999완전내용4.7 Noise Estimation부분내용11100완전내용4.8 Concluding Remarks부분내용11411완전내용Ⅲ Segmentation and Model Selection: Range and Motion부분내용11722완전내용5 Range and Motion Segmentation부분내용11933완전내용5.1 Introduction부분내용11944완전내용5.2 Robust Statistical Segmentation Methods: A Review부분내용12155완전내용5.3 Segmentation Using Unbiased Scale Estimate from Randed Residuals부분내용12866완전내용5.4 Range Segmentation부분내용13077완전내용5.5 Optic Flow Segmentation부분내용13688완전내용5.6 Conclusion부분내용14199완전내용6 Model Selection for Structure and Motion Recovery from Multiple Images부분내용14300완전내용6.1 Introduction부분내용14311완전내용6.2 Putative Motion Models부분내용14522완전내용6.3 Maximum Likeligood Estimation(MLE)부분내용14833완전내용6.4 Model Selection-Hypothesis Testing부분내용15244완전내용6.5 AIC for Model Selection부분내용15455완전내용6.6 Bayes Factors and Bayesian Model Comparison부분내용15966완전내용6.7 GRIC-Modified Bayes Factors for Fitting Varieties부분내용16577완전내용6.8 The Quest for the Universal Prior: MDL부분내용16988완전내용6.9 Bayesian Model Selection and Model Averaging부분내용17099완전내용6.10 Results부분내용17300완전내용6.11 Discussion부분내용17711완전내용6.12 Conclusion부분내용17822완전내용References부분내용18533완전내용Index부분내용20544
- Price Info
- ￦70000
- Control Number
- gtec:8291
MARC
008021021s2000 us 000a eng■020 ▼a0-387-98815-7
■0801 ▼a621.397.3
■082 ▼a621.367▼bA412d▼223
■090 ▼a621.367▼bA412d
■1000 ▼aSuter, Alireza Bab-Hadiashar, David
■24510▼aData Segmentation and Model Selection for Computer Vision▼dAlireza Bab-Hadiashar, David Suter 편.
■260 ▼a미국▼bSpringer-Verlag▼c2000
■300 ▼a208p.▼c24cm
■505 ▼aⅠHistorical Review▼c1▼a1 2D and 3D Scene Segmentation for Robotic Vision▼c3▼a1.1 Introduction▼c3▼a1.2 Binary Image Segmentation▼c5▼a1.3 2D Multitonal Image Segmentation▼c5▼a1.4 2½D Scene Segmentation▼c12▼a1.5 3D Scene Segmentation▼c22▼a1.6 Discussion and Comclusions▼c26▼aⅡStatistical and Geometrical Foundations▼c29▼a2 Robust Regression Methods and Model Selection▼c3100▼a2.1 Introduction▼c3111▼a2.2 The Influence Function and the Breakdown Point▼c3222▼a2.3 Robust Estimation and Inference in Linear Models▼c3433▼a2.4 Robust Model Selection▼c3744▼a2.5 Conclusions▼c4055▼a3 Robust Measures of Evidence for Variable Selection▼c4166▼a3.1 Introduction▼c4177▼a3.2 The Akaike Information Griterion▼c4288▼a3.3 Model Selection Based on the Wald Test▼c4599▼a3.4 Gypothesis Testing and Measures of Evidence for Variable Selection▼c5700▼a3.5 Examples▼c6911▼a3.6 Recommendations▼c8822▼a4 Model Selection Criteria for Geometric Inference▼c9133▼a4.1 Introduction▼c9144▼a4.2 Classical Regression▼c9355▼a4.3 Geometric Line Fitting▼c10066▼a4.4 General Geometric Model Selection▼c10577▼a4.5 Gemetric Cρ▼c10788▼a4.6 Bayesian Approaches▼c10999▼a4.7 Noise Estimation▼c11100▼a4.8 Concluding Remarks▼c11411▼aⅢ Segmentation and Model Selection: Range and Motion▼c11722▼a5 Range and Motion Segmentation▼c11933▼a5.1 Introduction▼c11944▼a5.2 Robust Statistical Segmentation Methods: A Review▼c12155▼a5.3 Segmentation Using Unbiased Scale Estimate from Randed Residuals▼c12866▼a5.4 Range Segmentation▼c13077▼a5.5 Optic Flow Segmentation▼c13688▼a5.6 Conclusion▼c14199▼a6 Model Selection for Structure and Motion Recovery from Multiple Images▼c14300▼a6.1 Introduction▼c14311▼a6.2 Putative Motion Models▼c14522▼a6.3 Maximum Likeligood Estimation(MLE)▼c14833▼a6.4 Model Selection-Hypothesis Testing▼c15244▼a6.5 AIC for Model Selection▼c15455▼a6.6 Bayes Factors and Bayesian Model Comparison▼c15966▼a6.7 GRIC-Modified Bayes Factors for Fitting Varieties▼c16577▼a6.8 The Quest for the Universal Prior: MDL▼c16988▼a6.9 Bayesian Model Selection and Model Averaging▼c17099▼a6.10 Results▼c17300▼a6.11 Discussion▼c17711▼a6.12 Conclusion▼c17822▼aReferences▼c18533▼aIndex▼c20544
■950 ▼b￦70000
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