MARC details
| 000 -LEADER |
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| fixed length control field |
05159 a2200433 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781608457281 (pbk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781608457298 (ebk.) |
| 024 7# - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
10.2200/S00370ED1V01Y201107AIM015 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
|
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.37 |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| -- |
24787 |
| -- |
24787 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hoiem, Derek |
| 245 10 - TITLE STATEMENT |
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| Title |
Representations and techniques for 3D object recognition and scene interpretation |
| Statement of responsibility, etc. |
Derek Hoiem, Silvio Savarese |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
San Rafael, California |
| Name of publisher, distributor, etc. |
Morgan & Claypool |
| Date of publication, distribution, etc. |
c2011 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxi, 147 p. |
| Other physical details |
ill |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Synthesis lectures on artificial intelligence and machine learning |
| Volume/sequential designation |
15 |
| 546 ## - LANGUAGE NOTE |
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| Language note |
eng |
| 500 ## - GENERAL NOTE |
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| General note |
Part of: Synthesis digital library of engineering and computer science |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
1. Background on 3D scene models -- 1.1 Theories of vision -- 1.1.1 Depth and surface perception -- 1.1.2 Awell-organized scene -- 1.2 Early computer vision and AI -- 1.3 Modern computer vision |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
2. Single-view geometry -- 2.1 Consequences of projection -- 2.2 Perspective projection with pinhole camera: 3D to 2D -- 2.3 3D measurement from a 2D image -- 2.4 Automatic estimation of vanishing points -- 2.5 Summary of key concepts -- |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
3. Modeling the physical scene -- 3.1 Elements of physical scene understanding -- 3.1.1 Elements -- 3.1.2 Physical interactions -- 3.2 Representations of scene space -- 3.2.1 Scene-level geometric description -- 3.2.2 Retinotopic maps -- 3.2.3 Highly structured 3D models -- 3.2.4 Loosely structured models: 3D point clouds and meshes -- 3.3 Summary -- |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
4. Categorizing images and regions -- 4.1 Overview of image labeling -- 4.2 Guiding principles -- 4.2.1 Creating regions -- 4.2.2 Choosing features -- 4.2.3 Classifiers -- 4.2.4 Datasets -- 4.3 Image features -- 4.3.1 Color -- 4.3.2 Texture -- 4.3.3 Gradient-based -- 4.3.4 Interest points and bag of words -- 4.3.5 Image position -- 4.3.6 Region shape -- 4.3.7 Perspective -- 4.4 Summary -- |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
5. Examples of 3D scene interpretation -- 5.1 Surface layout and automatic photo pop-up -- 5.1.1 Intuition -- 5.1.2 Geometric classes -- 5.1.3 Approach to estimate surface layout -- 5.1.4 Examples of predicted surface layout -- 5.1.5 3D reconstruction using the surface layout -- 5.2 Make3D: depth from an image -- 5.2.1 Predicting depth and orientation -- 5.2.2 Local constraints and priors -- 5.2.3 Results -- 5.3 The room as a box -- 5.3.1 Algorithm -- 5.3.2 Results -- 5.4 Summary -- |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Part II. Recognition of 3D objects from an image -- |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
6. Background on 3D recognition -- 6.1 Human vision theories -- 6.1.1 The Geon theory -- 6.1.2 2D-view specific templates -- 6.1.3 Aspect graphs -- 6.1.4 Computational theories by 3D alignment -- 6.1.5 Conclusions -- 6.2 Early computational models -- |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
7. Modeling 3D objects -- 7.1 Overview -- 7.2 Single instance 3D category models -- 7.2.1 Single instance 2D view-template models -- 7.2.2 Single instance 3D models -- 7.3 Mixture of single-view models -- 7.4 2-1/2D layout models -- 7.4.1 2-1/2D layout by ISM models -- 7.4.2 2-1/2D layout by view-invariant parts -- 7.4.3 2-1/2D hierarchical layout models -- 7.4.4 2-1/2D layout by discriminative aspects -- 7.5 3D layout models -- 7.5.1 3D layout models constructed upon 3D prototypes -- 7.5.2 3D layout models without 3D prototypes -- |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
8. Recognizing and understanding 3D objects -- 8.1 Datasets -- 8.2 Supervision and initialization -- 8.3 Modeling, learning and inference strategies -- |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
9. Examples of 2D 1/2 layout models -- 9.1 Linkage structure of canonical parts -- 9.1.1 The view-morphing formulation -- 9.1.2 Supervision -- 9.2 View-morphing models -- 9.2.1 Learning the model -- 9.2.2 Detection and viewpoint classification -- 9.2.3 Results -- 9.3 Conclusions -- |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Part III. Integrated 3D scene interpretation -- |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
10. Reasoning about objects and scenes -- 10.1 Objects in perspective -- 10.1.1 Object size -- 10.1.2 Appearance features -- 10.1.3 Interaction between objects and scene via object scale and pose -- 10.2 Scene layout -- 10.3 Occlusion -- 10.4 Summary -- |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
11. Cascades of classifiers -- 11.1 Intrinsic images revisited -- 11.1.1 Intrinsic image representation -- 11.1.2 Contextual interactions -- 11.1.3 Training and inference -- 11.1.4 Experiments -- 11.2 Feedback-enabled cascaded classification models -- 11.2.1 Algorithm -- 11.2.2 Experiments -- 11.3 Summary -- |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
12. Conclusion and future directions -- Bibliography -- Authors' biographies |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Computer vision |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Three-dimensional imaging |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Savarese, Silvio |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis lectures on artificial intelligence and machine learning |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Institution code [OBSOLETE] |
IMAR |
| Koha item type |
Carti |
| Serial record flag |
RM |
| -- |
EP |
| Call number prefix |
006.37 |