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- applications
- color_exposure
- adaptive histogram equalization (AHE)
- Adapting gray-scale filters to RGB images
- Histogram Equalization
- Histogram matching
- Color separation - immunohistochemical staining
- Histogram Equalization (local)
- Contrast adjustments
- Filtering regional maxima
- RGB to grayscale
- RGB to HSV
- Tinting gray-scale images
- developers
- edges
- features_detection
- Blob detection - 3 alternate algorithms
- BRIEF binary descriptor
- CENSURE
- Corner detection
- DAISY local image descriptor
- Fisher vector feature encoding
- Gabor filter banks for texture classification
- Gabors / Primary Visual Cortex "Simple Cells" from an Image
- GLCM Texture Features
- Haar-like feature descriptor
- HOG (Histogram of Oriented Gradients)
- Filling holes and finding peaks
- Local Binary Pattern for texture classification
- Local Binary Pattern (multiblock) for texture classification
- ORB feature detector and binary descriptor
- Shape Index
- SIFT feature detector and descriptor extractor
- Template Matching
- Sliding window histogram
- filters
- family of contour preserving filtering operations in mathematical morphology
- estimate blur strength
- butterworth filtering
- Shift-invariant wavelet denoising
- ensure PyWavelets is installed
- Image deconvolution using the Richardson-Lucy algo
- Image denoising with total variation, bilateral, and wavelet filters
- Wavelet denoising with VisuShrink & BayesShrink algos
- Band-pass filtering - Difference of Gaussians
- Image entropy
- hysteresis thresholding vs normal thresholding
- reconstructing lost or deteriorated parts of images and videos
- calibrating Denoisers Using J-Invariance
- plot_j_invariant_tutorial
- Non-local means denoising for preserving textures
- [plot_phase_unwrap](.ipynb
- compares 3 mean filters of the rank filter package
- Deconvolve a noisy image using Wiener and unsupervised Wiener algorithms
- Removing small objects in grayscale images with a top hat filter
- linear image sharpening technique
- Image windowing with FFTs
- numpy_operations
- segmentation
- Hierarchical Merging of Region Boundary RAGs
- Chan-Vese segmentation. Used for poorly defined boundries
- Find regular segments using compact watershed
- Euler numbers
- Expanding segmentation labels without overlap
- Detecting local extrema in an image
- 'paint bucket'
- hausdorff_distance
- Find the intersection of two segments
- segment an image with image labelling
- markers for watershed transforms
- compare segmentations obtained with SLIC and its masked version maskSLIC
- segmentation metrics: watershed vs Canny vs Morphological geodesic
- MorphGAC vs MorphACWE for image segmentation
- Multi-Otsu thresholding
- Normalized cuts
- Niblack and Sauvola thresholding
- Finding local maxima
- Perimeter estimation - classic vs Crofton estimators
- Region boundary based RAGs
- Drawing RAGs
- Demos merge_nodes function of a RAG
- Build a RAG, then merge regions which are similar in color
- RAG merge
- Random walker segmentation
- Explore and visualize region properties with pandas
- Measure properties of labelled image regions
- rolling-ball algorithm for estimating background intensity
- NOTE: a portion of this notebook uses the pywt package which isn't currently available?
- image segmentation - 4 methods
- Create a binary image from a grayscale image
- Trainable segmentation using local features and random forests
- Watershed segmentation
- transform
- interpolation: edge modes
- fundamental matrix
- geometric transforms
- image matching with RANSAC
- piecewise affine transformations
- image pyramids
- radon transforms - sinograms, FBP, SART
- line model estimation using RANSAC
- rescale, resize, and downscale
- structural similarity index
- swirl effect
- homographies