Visual object recognition
Saved in:
Author / Creator: | Grauman, Kristen Lorraine, 1979- |
---|---|
Imprint: | San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2011. |
Description: | 1 electronic text (xvii, 163 p.) : ill., digital file. |
Language: | English |
Series: | Synthesis lectures on artificial intelligence and machine learning, 1939-4616 ; # 11 Synthesis digital library of engineering and computer science. Synthesis lectures on artificial intelligence and machine learning, # 11. |
Subject: | |
Format: | E-Resource Book |
URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/10511006 |
Table of Contents:
- Preface
- Acknowledgments
- Figure credits
- 1. Introduction
- Overview
- Challenges
- The state of the art
- 2. Overview: recognition of specific objects
- Global image representations
- Local feature representations
- 3. Local features: detection and description
- Introduction
- Detection of interest points and regions
- Keypoint localization
- Scale invariant region detection
- Affine covariant region detection
- Orientation normalization
- Summary of local detectors
- Local descriptors
- The SIFT descriptor
- The SURF detector/descriptor
- Concluding remarks
- 4. Matching local features
- Efficient similarity search
- Tree-based algorithms
- Hashing-based algorithms and binary codes
- A rule of thumb for reducing ambiguous matches
- Indexing features with visual vocabularies
- Creating a visual vocabulary
- Vocabulary trees
- Choices in vocabulary formation
- Inverted file indexing
- Concluding remarks
- 5. Geometric verification of matched features
- Estimating geometric models
- Estimating similarity transformations
- Estimating affine transformations
- Homography estimation
- More general transformations
- Dealing with outliers
- RANSAC
- Generalized Hough transform
- Discussion
- 6. Example systems: specific-object recognition
- Image matching
- Object recognition
- Large-scale image retrieval
- Mobile visual search
- Image auto-annotation
- Concluding remarks
- 7. Overview: recognition of generic object categories
- 8. Representations for object categories
- Window-based object representations
- Pixel intensities and colors
- Window descriptors: global gradients and texture
- Patch descriptors: local gradients and texture
- A hybrid representation: bags of visual words
- Contour and shape features
- Feature selection
- Part-based object representations
- Overview of part-based models
- Fully-connected models: the constellation model
- Star graph models
- Mixed representations
- Concluding remarks
- 9. Generic object detection: finding and scoring candidates
- Detection via classification
- Speeding up window-based detection
- Limitations of window-based detection
- Detection with part-based models
- Combination classifiers
- Voting and the generalized Hough transform
- RANSAC
- Generalized distance transform
- 10. Learning generic object category models
- Data annotation
- Learning window-based models
- Specialized similarity measures and kernels
- Learning part-based models
- Learning in the constellation model
- Learning in the implicit shape model
- Learning in the pictorial structure model
- 11. Example systems: generic object recognition
- The Viola-Jones face detector
- Training process
- Recognition process
- Discussion
- The HOG person detector
- Bag-of-words image classification
- Training process
- Recognition process
- Discussion
- The implicit shape model
- Training process
- Recognition process
- Vote backprojection and top-down segmentation
- Hypothesis verification
- Discussion
- Deformable part-based models
- Training process
- Recognition process
- Discussion
- 12. Other considerations and current challenges
- Benchmarks and datasets
- Context-based recognition
- Multi-viewpoint and multi-aspect recognition
- Role of video
- Integrated segmentation and recognition
- Supervision considerations in object category learning
- Using weakly labeled image data
- Maximizing the use of manual annotations
- Unsupervised object discovery
- Language, text, and images
- 13. Conclusions
- Bibliography
- Authors' biographies.