MOB LEC4 Image Feature Matching

杨丝儿

发布于 2022-11-10 16:25:11

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发布于 2022-11-10 16:25:11

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Image stitching is a process combine two image taking from different angle to a image with wider range.

Image Features: A General Process

Step 1 - Feature Detection: identify distinctive points in our images. We call these points features.
Step 2 - Feature Description: associate a descriptor for each feature from its neighborhood.
Step 3 - Feature Matching: we use these descriptors to match features across two or more images.

Feature Detection

Feature Define

Features: Points of interest in an image defined by its image pixel coordinates [u, v].
Points of interest should have the following characteristics: For the feature:

Saliency: distinctive, identifiable, and different from its immediate neighborhood. (similar to neighbor, would cause confusion, is not a feature)
Locality: occupies a relatively small subset of image space. (occupies a large area is not a good feature)

For the set of features:

Repeatability: can be found even under some distortion. (miss with small distortion, including rotation and scale change, is not a feature)
Quantity: enough points represented in the image. (small number is not a good feature)
Efficiency: reasonable computation time. (resource consuming is not a good feature)

Feature Detection Algorithms

Feature Detection: Algorithms

Harris {corners}: Easy to compute, but not scale invariant. [Harris and Stephens, 1988]
Harris-Laplace {corners}: Same procedure as Harris detector, addition of scale selection based on Laplacian. Scale invariance. [Mikolajczyk, 2001]
Features from accelerated segment test (FAST) {corners}: Machine learning approach for fast corner detection. [Rosten and Drummond, 2006]
Laplacian of Gaussian (LOG) detector {blobs}: Uses the concept of scale space in a large neighborhood (blob). Somewhat scale invariant. [Lindeberg, 1998]
Difference of Gaussian (DOG) detector {blobs}: Approximates LOG but is faster to compute. [Lowe, 2004]

Feature Description

Descriptor: An N-dimensional vector that provides a summary of the local neighborhood information around the detected feature.
An effective feature descriptor should have the following characteristics:

Repeatability: manifested as robustness and invariance to translation, rotation, scale, and illumination changes

Distinctiveness: should allow us to distinguish between two close-by features, very important for subsequent matching step

Compactness & Efficiency: reasonable computation time and efficiency

Scale Invariant Feature Transform (SIFT) descriptors: [Lowe 1999]

Step 1: Find a 16 x 16 window around detected feature
Step 2: Separate the neighborhood into 16 cells, each comprised of 4 x 4 patch of pixels

Step 3: For each cell, first find the most prominent gradient change direction in each patch, and then create a 8-bin orientation histogram to describe the cell.

Step 4: Apply this 8-bin orientation description to all the 16 cells, and it yields a 128-dimension vector - the descriptor of the keypoint

The above process is usually compute on rotated and scaled versions of the 16 x 16 window, allowing for better scale robustness.

Feature Matching

Brute Force Feature Matching

Brute force feature matching might not be fast enough for extremely large amounts of features. In practice, a k-d tree is often used to speed the search by constraining it spatially.