CV Chapter 5 Local Features and Matching

Questions about the lecture 'Computer Vision' of the RWTH Aachen Chapter 5 Local Features and Matching

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Flashcards	55
Language	English
Category	Computer Science
Level	University
Created / Updated	04.02.2017 / 20.02.2017
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What are the motivations for local feature representations? [2]

1. Global representations have limitations

2. Local representations have increased robustness to occlusion, articulation and intra-category variation

Name two applications of local feature representations? [2]

1. Analyze if two images display same content from different point of view

2. Image stitching

What are the steps of image stitching? [3]

1. Detect feature points

2. Find corresponding pairs matching local descriptors

3. Align images

What are the steps of feature point detection (step one) of image stitching? [3]

1. Define region around keypoint

2. Extract and normalize region content

3. Compute local descriptor

What are the steps of pair matching (step two) of image stitching?

Find same points // Correctly find corresponding one

What are local feature detectors in recent applications in computer vision?

Basic building block

What are the requirements for local feature detectors? [5]

1. Regions are repeatable and accurate

2. Invariant to translation, rotation and scale changes

3. Features have locality making them robust to occlusion and clutter

4. Provide sufficient number of distinct regions

5. Operations must be close to real-time performance

Name available local feature detectors? [7]

1. Hessian and Harris

2. Laplacian, DoG

3. Hessian-/Harris-Laplace

4. Hessian-/Harris-Affine

5. EBR and IBR

6. MSER

7. Salient Regions