## Data Segmentation and Model Selection for Computer Vision: A Statistical ApproachAlireza Bab-Hadiashar, David Suter The primary focus of this book is on techniques for segmentation of visual data. By "visual data," we mean data derived from a single image or from a sequence of images. By "segmentation" we mean breaking the visual data into meaningful parts or segments. However, in general, we do not mean "any old data": but data fundamental to the operation of robotic devices such as the range to and motion of objects in a scene. Having said that, much of what is covered in this book is far more general: The above merely describes our driving interests. The central emphasis of this book is that segmentation involves model fitting. We believe this to be true either implicitly (as a conscious or sub conscious guiding principle of those who develop various approaches) or explicitly. What makes model-fitting in computer vision especially hard? There are a number of factors involved in answering this question. The amount of data involved is very large. The number of segments and types (models) are not known in advance (and can sometimes rapidly change over time). The sensors we have involve the introduction of noise. Usually, we require fast ("real-time" or near real-time) computation of solutions independent of any human intervention/supervision. Chapter 1 summarizes many of the attempts of computer vision researchers to solve the problem of segmenta tion in these difficult circumstances. |

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### Contents

VI | 3 |

VIII | 5 |

X | 6 |

XI | 10 |

XII | 11 |

XIII | 12 |

XV | 14 |

XVI | 15 |

LXI | 96 |

LXII | 97 |

LXIII | 98 |

LXV | 100 |

LXVI | 103 |

LXVII | 105 |

LXVIII | 107 |

LXIX | 109 |

XVII | 17 |

XVIII | 18 |

XIX | 21 |

XX | 22 |

XXII | 24 |

XXIII | 26 |

XXIV | 29 |

XXV | 31 |

XXVII | 32 |

XXVIII | 34 |

XXIX | 36 |

XXX | 37 |

XXXI | 38 |

XXXII | 39 |

XXXIII | 40 |

XXXIV | 41 |

XXXVI | 42 |

XXXVII | 45 |

XXXVIII | 46 |

XXXIX | 47 |

XL | 48 |

XLI | 49 |

XLII | 51 |

XLIII | 57 |

XLIV | 58 |

XLV | 61 |

XLVI | 63 |

XLVII | 69 |

XLIX | 70 |

LI | 74 |

LII | 77 |

LIII | 81 |

LIV | 82 |

LVI | 88 |

LVII | 91 |

LIX | 93 |

LX | 95 |

LXX | 111 |

LXXII | 113 |

LXXIII | 114 |

LXXIV | 118 |

LXXV | 119 |

LXXVII | 121 |

LXXVIII | 125 |

LXXIX | 126 |

LXXX | 128 |

LXXXI | 130 |

LXXXII | 136 |

LXXXIII | 138 |

LXXXIV | 139 |

LXXXV | 141 |

LXXXVI | 143 |

LXXXVII | 145 |

LXXXVIII | 147 |

LXXXIX | 148 |

XC | 152 |

XCI | 154 |

XCII | 159 |

XCIII | 161 |

XCIV | 163 |

XCV | 165 |

XCVII | 168 |

XCVIII | 169 |

XCIX | 170 |

C | 173 |

CI | 176 |

CIII | 177 |

CIV | 178 |

CV | 179 |

CVI | 180 |

CVII | 182 |

CVIII | 185 |

205 | |

### Other editions - View all

Data Segmentation and Model Selection for Computer Vision Alireza Bab-Hadiashar,David Suter No preview available - 2000 |

Data Segmentation and Model Selection for Computer Vision: A Statistical ... Alireza Bab-Hadiashar,David Suter No preview available - 2012 |

### Common terms and phrases

Akaike algorithm analysis approach approximation assumed asymptotic Bayes factor Bayesian camera computer vision considered corresponding covariance matrix data set defined degrees of freedom dimension distribution edge equation error example Figure full model fundamental matrix Gaussian GBIC geometric homography Information Criterion inliers K-th order statistic least squares estimation likelihood ratio estimator linear regression M-estimation Mallows matches maximum likelihood estimator measures of evidence methods minimizes model averaging model selection motion model noise noncentrality parameter number of parameters optic flow order statistic outliers overfitting pixel planar plot posterior posterior probability prediction predictors prior problem procedure quadratic range data range image range segmentation RANSAC regions relation residual robust estimation robust statistics Ronchetti Rotation Affinity sample scale estimate scene segmentation Section selection criteria sequence Sommer Staudte structure submodel surface TABLE tion values variable selection vector Wald measures Wald statistic Wald test weights of evidence xa,ya zero