CV Chapter 6 Categorization 2
Questions about the lecture 'Computer Vision' of the RWTH Aachen Chapter 6 Categorization 2
Questions about the lecture 'Computer Vision' of the RWTH Aachen Chapter 6 Categorization 2
Set of flashcards Details
| Flashcards | 50 |
|---|---|
| Language | English |
| Category | Computer Science |
| Level | University |
| Created / Updated | 04.02.2017 / 21.02.2017 |
| Licencing | Not defined |
| Weblink |
https://card2brain.ch/box/20170204_cv_chapter_6_categorization_2
|
| Embed |
<iframe src="https://card2brain.ch/box/20170204_cv_chapter_6_categorization_2/embed" width="780" height="150" scrolling="no" frameborder="0"></iframe>
|
What is the difference of categorization to local feature matching?
Recognizable objects have no longer exact correspondence, only local
Name models for object categorization 2? [3]
1. Part-based models
2. Implicit shape models (ISM)
3. Deformable part-based model
What is the idea of part-based models for classification 2? [2]
1. Parts are 2D image fragments
2. Structure is configuration of parts
Name connectivity structures for part-based models for categorization 2? [7]
1. Bag of visual words with O(N)
2. Constellation with O(N^k)
3. Star shape with O(N²)
4. Tree with O(N²)
5. k-fan with O(N³)
6. Hierarchy
7. Sparse flexible model
What is the idea of implicit shape models (ISM) for categorization 2? [4]
1. Learn appearance codebook and star topology structural model
2. Features are considered independent given object center
3. Use visual vocabulary with displacement vectors to index votes
4. Robust to clutter, occlusion, noise and low contrast
What changed for the probabilistic generalized hough transform? [5]
1. Exact correspondence → probabilistic match
2. NN matching → soft matching
3. Feature location on object → part location distribution
4. Uniform votes → probabilistic vote weighting
5. Quantized hough array → continuous hough space
How does recognition works for implicit shape models (ISM) for categorization 2? [3]
1. Choose interest points from image feature f
2. Compare with codebook entries with probabilistic vote weights
3. Locate object position and return back project hypothesis
How does segmentation works for implicit shape models (ISM) for categorization 2? [2]
1. Find pixel contributions with meta information from hypothesis
2. Perform segmentation
