Image Processing

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Image Processing by Mind Map: Image Processing

1. Feature based

1.1. Feature Detectors

1.1.1. Edge -detection

1.1.1.1. canny

1.1.1.2. sobel

1.1.1.3. prewitt

1.1.1.4. Roberts cross

1.1.1.5. Deriche

1.1.1.6. Differential

1.1.2. Corner detection

1.1.2.1. Harris operator

1.1.2.2. Shi and Tomasi

1.1.2.3. Level curve curvature

1.1.2.4. Hessian feature strength measures

1.1.2.5. SUSAN

1.1.2.6. FAST

1.1.3. Blob detection

1.1.3.1. Laplacian of Gaussian (LoG)

1.1.3.2. Difference of Gaussians (DoG)

1.1.3.3. Determinant of Hessian (DoH

1.1.3.4. PCBR

1.1.3.5. Maximally stable extremal regions

1.2. Feature Descriptors

1.2.1. BRIEF

1.2.2. SURF

1.2.3. ORB

1.2.4. SIFT

1.3. Feature Matching

1.3.1. Brute Force

1.3.2. FLANN

1.3.3. Brute-Force Matching with ORB Descriptors

1.3.4. Brute-Force Matching with SIFT Descriptors and Ratio Test

2. Image based

2.1. Filters

2.1.1. Edge -detection

2.1.1.1. canny

2.1.1.2. sobel

2.1.1.3. prewitt

2.1.1.4. Roberts cross

2.1.1.5. Deriche

2.1.1.6. Differential

2.1.2. Corner detection

2.1.2.1. Harris operator

2.1.2.2. Shi and Tomasi

2.1.2.3. Level curve curvature

2.1.2.4. Hessian feature strength measures

2.1.2.5. SUSAN

2.1.2.6. FAST

2.1.3. Blob detection

2.1.3.1. Laplacian of Gaussian (LoG)

2.1.3.2. Difference of Gaussians (DoG)

2.1.3.3. Determinant of Hessian (DoH

2.1.3.4. PCBR

2.1.3.5. Maximally stable extremal regions

2.2. Image Comparison tools

2.2.1. Pixel-by-pixel comparison

2.2.2. OpenCV method : Comapre Histograms

2.2.3. Distance-based functions for image comparison:

2.2.4. Image Change Detection Algorithms:

2.2.5. Comparing Images Using Color Coherence Vectors and joint histogram

2.2.6. Statistical comparison of images using Gibbs random fields:

2.2.7. Visual Analysis for Image Comparison

2.2.8. Least Squares Image Matching

2.2.9. Comparing images using the Hausdorff distance

2.2.10. pixel-based change detection methods