We can compute the cumulative distribution function (CDF) for a given image with the cumulative_distribution() function, as we shall see in the image enhancement chapter. For now, the reader is encouraged to find the usage of this function to compute the CDF.
Computing the cumulative distribution function of an image 
by Sandipan Dey
Hands-On Image Processing with Python
- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Preface
- Getting Started with Image Processing
- What is image processing and some applications
- The image processing pipeline
- Setting up different image processing libraries in Python
- Image I/O and display with Python
- Dealing with different image types and file formats and performing basic image manipulations
- Dealing with different image types and file formats
- Basic image manipulations
- Image manipulations with numpy array slicing 
- Image manipulations with PIL
- Cropping an image
- Resizing an image
- Negating an image
- Converting an image into grayscale
- Some gray-level transformations
- Some geometric transformations
- Changing pixel values of an image
- Drawing on an image
- Drawing text on an image
- Creating a thumbnail
- Computing the basic statistics of an image
- Plotting the histograms of pixel values for the RGB channels of an image
- Separating the RGB channels of an image 
- Combining multiple channels of an image
- α-blending two images
- Superimposing two images
- Adding two images
- Computing the difference between two images
- Subtracting two images and superimposing two image negatives
- Image manipulations with scikit-image
- Image manipulation with Matplotlib
- Image manipulation with the scipy.misc and scipy.ndimage modules
- Summary
- Questions
- Further reading
- Sampling, Fourier Transform, and Convolution
- Convolution and Frequency Domain Filtering
- Image Enhancement
- Image Enhancement Using Derivatives
- Image derivatives – Gradient and Laplacian
- Sharpening and unsharp masking
- Edge detection using derivatives and filters (Sobel, Canny, and so on)
- With gradient magnitude computed using the partial derivatives
- Sobel edge detector with scikit-image
- Different edge detectors with scikit-image – Prewitt, Roberts, Sobel, Scharr, and Laplace
- The Canny edge detector with scikit-image
- The LoG and DoG filters
- Finding and enhancing edges with PIL
- Image pyramids (Gaussian and Laplacian) – blending images
- Summary
- Questions
- Further reading
- Morphological Image Processing
- Extracting Image Features and Descriptors
- Feature detectors versus descriptors
- Harris Corner Detector
- Blob detectors with LoG, DoG, and DoH
- Histogram of Oriented Gradients
- Scale-invariant feature transform
- Haar-like features
- Summary
- Questions
- Further reading
- Image Segmentation
- What is image segmentation?
- Hough transform – detecting lines and circles
- Thresholding and Otsu's segmentation
- Edges-based/region-based segmentation
- Felzenszwalb, SLIC, QuickShift, and Compact Watershed algorithms 
- Active contours, morphological snakes, and GrabCut algorithms
- Summary
- Questions
- Further reading
- Classical Machine Learning Methods in Image Processing
- Supervised versus unsupervised learning
- Unsupervised machine learning – clustering, PCA, and eigenfaces
- Supervised machine learning – image classification
- Supervised machine learning – object detection
- Summary
- Questions
- Further reading
- Deep Learning in Image Processing - Image Classification
- Deep Learning in Image Processing - Object Detection, and more
- Additional Problems in Image Processing
- Other Books You May Enjoy
Computing the cumulative distribution function of an image
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