ece734 project-scale invariant feature transform algorithm
DESCRIPTION
ECE734 Project-Scale Invariant Feature Transform Algorithm. Jing Li. 1. Background. 2. Algorithm introdution. 3. Program implement. Outline. Background: Scale-Invariant Feature Transform (SIFT). Scale-Invariant Feature Transform: Apply in image recognition. - PowerPoint PPT PresentationTRANSCRIPT
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ECE734 Project-Scale Invariant Feature Transform AlgorithmJing Li
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Outline
Background1
Algorithm introdution2
Program implement3
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Background: Scale-Invariant Feature Transform (SIFT)
• Scale-Invariant Feature Transform: Apply in image recognition.
• Two steps for image recognition: 1. Feature extraction 2. Feature match• SIFT algorithm can extract distinctive features.• Features: invariant to image scale and rotation perform reliable matching between different views of object or
scene. • Random Sample Consensus (RANSAC): Match
features from object image to training images
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•Scale-space extrema detection
•Orientation assignment
•Keypoint localization
•Keypoint descriptor
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Scale-space extrema detection
• Define image scale space function L(x, y,σ) = G(x, y, σ) ∗ I(x, y)• Detect stable keypoint locations
used difference-of-Gaussian function
D(x,y,σ)= L(x, y,kσ) - L(x, y,σ) • D(x,y,σ) is the approximation of
Laplacian of Gaussian (LoG) operation.
• We use five steps to build the use Build difference-of-Gaussian Octaves D(x,y,σ)
use different to conversion to build each octave of scale space, based on the function:
•
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Keypoint localization
• Maxima and minima of LoG produce the most stable image features compared to a range of other possible image functions
• Detected by comparing a pixel (marked with X) to its 26 neighbors in 3x3 regions at the current and adjacent scales
• Talyor expansion for D(x,y,σ) and set its derivative to 0
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Orientation Assignment
• Assign orientation to each keypoints by local gradient direction
• Gradient magnitude, m(x, y),• Orientation θ(x,y)
• Divide image into equal regions (4,16…….)
• Divide each region to some small regions.
• Find the orientation of small region and shift to the location of keypoint of large region. Overlay these orientation and use it to express the feature of keypoints.
Keypoint Descriptor
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Program Test
Pgm file: Keypoint descriptor by SIFT algorithm1021 keypoints found
An image of size 500 pixels square will typically giveover 1000 keypoints depending on image content
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• SIFT extract features from training image and store in database. Set a sample image which include the same features with the training image. Compare the Euclidean distance of keypoint to test corrective of algorithm
• Same features typically have the minimum Euclidean distance.• Compare Euclidean distance of each keypoints.
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