Landmark-Based Image Analysis: Using Geometric and Intensity ModelsSpringer Science & Business Media, 2001 M02 28 - 303 páginas Landmarks are preferred image features for a variety of computer vision tasks such as image mensuration, registration, camera calibration, motion analysis, 3D scene reconstruction, and object recognition. Main advantages of using landmarks are robustness w. r. t. lightning conditions and other radiometric vari ations as well as the ability to cope with large displacements in registration or motion analysis tasks. Also, landmark-based approaches are in general com putationally efficient, particularly when using point landmarks. Note, that the term landmark comprises both artificial and natural landmarks. Examples are comers or other characteristic points in video images, ground control points in aerial images, anatomical landmarks in medical images, prominent facial points used for biometric verification, markers at human joints used for motion capture in virtual reality applications, or in- and outdoor landmarks used for autonomous navigation of robots. This book covers the extraction oflandmarks from images as well as the use of these features for elastic image registration. Our emphasis is onmodel-based approaches, i. e. on the use of explicitly represented knowledge in image analy sis. We principally distinguish between geometric models describing the shape of objects (typically their contours) and intensity models, which directly repre sent the image intensities, i. e. ,the appearance of objects. Based on these classes of models we develop algorithms and methods for analyzing multimodality im ages such as traditional 20 video images or 3D medical tomographic images. |
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Contenido
II | 1 |
III | 4 |
IV | 7 |
V | 13 |
VI | 16 |
VII | 19 |
VIII | 21 |
IX | 26 |
XLI | 109 |
XLII | 110 |
XLIII | 112 |
XLV | 117 |
XLVI | 143 |
XLVII | 151 |
XLIX | 152 |
L | 156 |
X | 28 |
XI | 29 |
XII | 31 |
XIII | 32 |
XIV | 33 |
XV | 35 |
XVI | 36 |
XVIII | 39 |
XIX | 40 |
XX | 41 |
XXI | 42 |
XXII | 43 |
XXIII | 45 |
XXIV | 47 |
XXVI | 50 |
XXVII | 58 |
XXVIII | 60 |
XXIX | 63 |
XXX | 68 |
XXXI | 70 |
XXXII | 78 |
XXXIII | 81 |
XXXIV | 84 |
XXXV | 87 |
XXXVI | 90 |
XXXVII | 93 |
XXXVIII | 100 |
XXXIX | 102 |
XL | 106 |
LI | 168 |
LII | 169 |
LIII | 171 |
LIV | 172 |
LV | 176 |
LVI | 179 |
LVII | 183 |
LIX | 186 |
LX | 188 |
LXI | 191 |
LXII | 198 |
LXIV | 201 |
LXV | 205 |
LXVI | 206 |
LXVII | 211 |
LXVIII | 216 |
LXIX | 217 |
LXX | 221 |
LXXI | 224 |
LXXII | 226 |
LXXIII | 232 |
LXXIV | 241 |
LXXV | 247 |
LXXVI | 250 |
LXXVII | 255 |
LXXVIII | 259 |
301 | |
Otras ediciones - Ver todas
Landmark-Based Image Analysis: Using Geometric and Intensity Models Karl Rohr Vista previa limitada - 2013 |
Landmark-Based Image Analysis: Using Geometric and Intensity Models Karl Rohr Sin vista previa disponible - 2010 |
Landmark-Based Image Analysis: Using Geometric and Intensity Models Karl Rohr Sin vista previa disponible - 2012 |
Términos y frases comunes
2D and 3D 3D differential operators 3D images 3D operators affine transformation algorithms analytic anatomical point landmarks applied approximating thin-plate splines basis functions Berlin Heidelberg blur characterization comparison computer graphics computer vision Conf contour corner operators corresponding Cramér-Rao bound CT images curve dataset denoted described detection performance differential operators Distance vox elastic registration error ellipsoid estimated example Figure Förstner Gaussian curvature geometric models gradient H.S. Stiehl Hessian matrix human brain image analysis image data image features image gradient image intensities image noise image registration intensity models intensity variations ISBN isocontour L-corner landmark extraction localization errors localization uncertainty mathematical matrix mean curvature measure Medical Imaging model-based normal landmarks objects obtain operator Op3 operator responses order partial derivatives orientation parameters partial derivatives position principal curvatures Proc quasi-landmarks registration result represent Rohr scheme Section slices solution step edge structures surface vector ventricular horn ventricular system voxels στ
Pasajes populares
Página 282 - Maciunas, RJ, and Fitzpatrick, JM, "Registration of head CT images to physical space using a weighted combination of points and surfaces," IEEE Transactions on Medical Imaging, 17, pp.
Página 268 - Medical Image Analysis: Progress over Two Decades and the Challenges Ahead," IEEE Transactions on Pattern Analysis and Machine Intelligence 22, pp.
Página 296 - New feature points based on geometric invariants for 3D image registration," International Journal of Computer Vision 18, pp.
Página 283 - JV Miller, DE Breen, WE Lorensen, RM O'Bara, and MJ Wozny. Geometrically Deformed Models: A method for extracting closed geometric models from volume data.
Página 283 - On the problem of geometric distortion in Magnetic Resonance Images for Stereotactic Neurosurgery,
Página 280 - Distortion invariant object recognition in the dynamic link architecture", IEEE Trans, on Computers, vol.
Referencias a este libro
2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial ... Arthur Ardeshir Goshtasby Vista previa limitada - 2005 |
Precision Landmark Location for Machine Vision and Photogrammetry: Finding ... José A. Gutierrez,Brian S.R. Armstrong Vista previa limitada - 2007 |