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Dey, Sandipan. Hands-on image processing with Python: expert techniques for advanced image analysis and effective interpretation of image data / Sandipan Dey. — 1 online resource (1 volume) : illustrations — <URL:http://elib.fa.ru/ebsco/1950555.pdf>.

Дата создания записи: 04.02.2019

Тематика: Image processing.; Python (Computer program language); Computer vision.; Machine learning.; Computer vision.; Image processing.; Machine learning.; Python (Computer program language); TECHNOLOGY & ENGINEERING / Mechanical.

Коллекции: EBSCO

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Оглавление

  • Cover
  • Title Page
  • Copyright and Credits
  • Dedication
  • About Packt
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Getting Started with Image Processing
    • What is image processing and some applications
      • What is an image and how it is stored on a computer
      • What is image processing?
      • Some applications of image processing
    • The image processing pipeline
    • Setting up different image processing libraries in Python
      • Installing pip
      • Installing some image processing libraries in Python
      • Installing the Anaconda distribution
      • Installing Jupyter Notebook
    • Image I/O and display with Python
      • Reading, saving, and displaying an image using PIL
        • Providing the correct path to the images on the disk
      • Reading, saving, and displaying an image using Matplotlib
        • Interpolating while displaying with Matplotlib imshow()
      • Reading, saving, and displaying an image using scikit-image
        • Using scikit-image's astronaut dataset
        • Reading and displaying multiple images at once
      • Reading, saving, and displaying an image using scipy misc
        • Using scipy.misc's face dataset
    • Dealing with different image types and file formats and performing basic image manipulations
      • Dealing with different image types and file formats
        • File formats
          • Converting from one file format to another
        • Image types (modes)
          • Converting from one image mode into another
        • Some color spaces (channels)
          • Converting from one color space into another
        • Data structures to store images
          • Converting image data structures
      • Basic image manipulations
        • Image manipulations with numpy array slicing 
          • Simple image morphing - α-blending of two images using cross-dissolving
        • 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
          • Inverse warping and geometric transformation using the warp() function
          • Applying the swirl transform
          • Adding random Gaussian noise to images
          • Computing the cumulative distribution function of an image 
        • Image manipulation with Matplotlib
          • Drawing contour lines for an image
        • Image manipulation with the scipy.misc and scipy.ndimage modules
    • Summary
    • Questions
    • Further reading
  • Chapter 2: Sampling, Fourier Transform, and Convolution
    • Image formation – sampling and quantization
      • Sampling
        • Up-sampling
          • Up-sampling and interpolation 
        • Down-sampling
          • Down-sampling and anti-aliasing
      • Quantization
        • Quantizing with PIL
    • Discrete Fourier Transform
      • Why do we need the DFT?
      • The Fast Fourier Transform algorithm to compute the DFT
        • The FFT with the scipy.fftpack module
          • Plotting the frequency spectrum
        • The FFT with the numpy.fft module
          • Computing the magnitude and phase of a DFT
    • Understanding convolution
      • Why convolve an image?
      • Convolution with SciPy signal's convolve2d
        • Applying convolution to a grayscale image
          • Convolution modes, pad values, and boundary conditions
        • Applying convolution to a color (RGB) image
      • Convolution with SciPy ndimage.convolve
      • Correlation versus convolution
        • Template matching with cross-correlation between the image and template
    • Summary
    • Questions
    • Further reading
  • Chapter 3: Convolution and Frequency Domain Filtering
    • Convolution theorem and frequency domain Gaussian blur
      • Application of the convolution theorem
        • Frequency domain Gaussian blur filter with numpy fft
          • Gaussian kernel in the frequency domain
        • Frequency domain Gaussian blur filter with scipy signal.fftconvolve()
        • Comparing the runtimes of SciPy convolve() and fftconvolve() with the Gaussian blur kernel
    • Filtering in the frequency domain (HPF, LPF, BPF, and notch filters)
      • What is a filter?
      • High-Pass Filter (HPF)
        • How SNR changes with frequency cut-off
      • Low-pass filter (LPF)
        • LPF with scipy ndimage and numpy fft
          • LPF with fourier_gaussian
        • LPF with scipy fftpack
        • How SNR changes with frequency cutoff
      • Band-pass filter (BPF) with DoG
      • Band-stop (notch) filter
        • Using a notch filter to remove periodic noise from images
      • Image restoration
        • Deconvolution and inverse filtering with FFT
        • Image deconvolution with the Wiener filter
        • Image denoising with FFT
          • Filter in FFT
          • Reconstructing the final image
    • Summary
    • Questions
    • Further reading
  • Chapter 4: Image Enhancement
    • Point-wise intensity transformations – pixel transformation
      • Log transform
      • Power-law transform
      • Contrast stretching
        • Using PIL as a point operation
        • Using the PIL ImageEnhance module
      • Thresholding
        • With a fixed threshold
        • Half-toning
        • Floyd-Steinberg dithering with error diffusion
    • Histogram processing – histogram equalization and matching
      • Contrast stretching and histogram equalization with scikit-image
      • Histogram matching
        • Histogram matching for an RGB image
    • Linear noise smoothing
      • Smoothing with PIL
        • Smoothing with ImageFilter.BLUR
        • Smoothing by averaging with the box blur kernel
        • Smoothing with the Gaussian blur filter
      • Comparing smoothing with box and Gaussian kernels using SciPy ndimage
    • Nonlinear noise smoothing
      • Smoothing with PIL
        • Using the median filter
        • Using max and min filter
      • Smoothing (denoising) with scikit-image
        • Using the bilateral filter
        • Using non-local means
      • Smoothing with scipy ndimage
    • Summary
    • Questions
    • Further reading
  • Chapter 5: Image Enhancement Using Derivatives
    • Image derivatives – Gradient and Laplacian
      • Derivatives and gradients
        • Displaying the magnitude and the gradient on the same image
      • Laplacian
        • Some notes about the Laplacian
      • Effects of noise on gradient computation
    • Sharpening and unsharp masking
      • Sharpening with Laplacian
      • Unsharp masking
        • With the SciPy ndimage module
    • Edge detection using derivatives and filters (Sobel, Canny, and so on)
      • With gradient magnitude computed using the partial derivatives
        • The non-maximum suppression algorithm
      • 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
        • The LoG filter with the SciPy ndimage module
        • Edge detection with the LoG filter
          • Edge detection with the Marr and Hildreth's algorithm using the zero-crossing computation
      • Finding and enhancing edges with PIL
    • Image pyramids (Gaussian and Laplacian) – blending images
      • A Gaussian pyramid with scikit-image transform pyramid module
      • A Laplacian pyramid with scikit-image transform's pyramid module
      • Constructing the Gaussian Pyramid
      • Reconstructing an image only from its Laplacian pyramid
      • Blending images with pyramids
    • Summary
    • Questions
    • Further reading
  • Chapter 6: Morphological Image Processing
    • The scikit-image morphology module
      • Binary operations
        • Erosion
        • Dilation
        • Opening and closing
        • Skeletonizing
        • Computing the convex hull
        • Removing small objects
        • White and black top-hats
        • Extracting the boundary 
      • Fingerprint cleaning with opening and closing
      • Grayscale operations
    • The scikit-image filter.rank module
      • Morphological contrast enhancement
      • Noise removal with the median filter
      • Computing the local entropy
    • The SciPy ndimage.morphology module
      • Filling holes in binary objects
      • Using opening and closing to remove noise
      • Computing the morphological Beucher gradient
      • Computing the morphological Laplace
    • Summary
    • Questions
    • Further reading
  • Chapter 7: Extracting Image Features and Descriptors
    • Feature detectors versus descriptors
    • Harris Corner Detector
      • With scikit-image
        • With sub-pixel accuracy
      • An application – image matching
        • Robust image matching using the RANSAC algorithm and Harris Corner features
    • Blob detectors with LoG, DoG, and DoH
      • Laplacian of Gaussian (LoG)
      • Difference of Gaussian (DoG)
      • Determinant of Hessian (DoH)
    • Histogram of Oriented Gradients
      • Algorithm to compute HOG descriptors
      • Compute HOG descriptors with scikit-image
    • Scale-invariant feature transform
      • Algorithm to compute SIFT descriptors
      • With opencv and opencv-contrib
      • Application – matching images with BRIEF, SIFT, and ORB
        • Matching images with BRIEF binary descriptors with scikit-image
        • Matching with ORB feature detector and binary descriptor using scikit-image
        • Matching with ORB features using brute-force matching with python-opencv
        • Brute-force matching with SIFT descriptors and ratio test with OpenCV
    • Haar-like features
      • Haar-like feature descriptor with scikit-image
      • Application – face detection with Haar-like features
        • Face/eye detection with OpenCV using pre-trained classifiers with Haar-cascade features
    • Summary
    • Questions
    • Further reading
  • Chapter 8: Image Segmentation
    • What is image segmentation?
    • Hough transform – detecting lines and circles
    • Thresholding and Otsu's segmentation
    • Edges-based/region-based segmentation
      • Edge-based segmentation
      • Region-based segmentation
        • Morphological watershed algorithm
    • Felzenszwalb, SLIC, QuickShift, and Compact Watershed algorithms 
      • Felzenszwalb's efficient graph-based image segmentation
      • SLIC
        • RAG merging
      • QuickShift
      • Compact Watershed
      • Region growing with SimpleITK 
    • Active contours, morphological snakes, and GrabCut algorithms
      • Active contours
      • Morphological snakes
      • GrabCut with OpenCV
    • Summary
    • Questions
    • Further reading
  • Chapter 9: Classical Machine Learning Methods in Image Processing
    • Supervised versus unsupervised learning
    • Unsupervised machine learning – clustering, PCA, and eigenfaces
      • K-means clustering for image segmentation with color quantization
      • Spectral clustering for image segmentation
      • PCA and eigenfaces 
        • Dimension reduction and visualization with PCA
          • 2D projection and visualization
        • Eigenfaces with PCA
          • Eigenfaces
          • Reconstruction
          • Eigen decomposition
    • Supervised machine learning – image classification
      • Downloading the MNIST (handwritten digits) dataset
      • Visualizing the dataset
      • Training kNN, Gaussian Bayes, and SVM models to classify MNIST 
        • k-nearest neighbors (KNN) classifier
          • Squared Euclidean distance
          • Computing the nearest neighbors
          • Evaluating the performance of the classifier
        • Bayes classifier (Gaussian generative model)
          • Training the generative model – computing the MLE of the Gaussian parameters
          • Computing the posterior probabilities to make predictions on test data and model evaluation
        • SVM classifier
    • Supervised machine learning – object detection
      • Face detection with Haar-like features and cascade classifiers with AdaBoost – Viola-Jones
        • Face classification using the Haar-like feature descriptor
          • Finding the most important Haar-like features for face classification with the random forest ensemble classifier
      • Detecting objects with SVM using HOG features
        • HOG training
        • Classification with the SVM model
        • Computing BoundingBoxes with HOG-SVM
        • Non-max suppression
    • Summary
    • Questions
    • Further reading
  • Chapter 10: Deep Learning in Image Processing - Image Classification
    • Deep learning in image processing
      • What is deep learning?
      • Classical versus deep learning
      • Why deep learning?
    • CNNs
      • Conv or pooling or FC layers – CNN architecture and how it works
        • Convolutional layer
        • Pooling layer
        • Non-linearity – ReLU layer
        • FC layer
        • Dropout
    • Image classification with TensorFlow or Keras
      • Classification with TF
      • Classification with dense FC layers with Keras
        • Visualizing the network
        • Visualizing the weights in the intermediate layers 
      • CNN for classification with Keras
        • Classifying MNIST
        • Visualizing the intermediate layers 
    • Some popular deep CNNs
      • VGG-16/19
        • Classifying cat/dog images with VGG-16 in Keras
          • Training phase
          • Testing (prediction) phase
      • InceptionNet
      • ResNet
    • Summary
    • Questions
    • Further reading
  • Chapter 11: Deep Learning in Image Processing - Object Detection, and more
    • Introducing YOLO v2 
      • Classifying and localizing images and detecting objects
      • Proposing and detecting objects using CNNs
      • Using YOLO v2 
        • Using a pre-trained YOLO model for object detection
    • Deep semantic segmentation with DeepLab V3+
      • Semantic segmentation
      • DeepLab V3+
        • DeepLab v3 architecture
        • Steps you must follow to use DeepLab V3+ model for semantic segmentation
    • Transfer learning – what it is, and when to use it
      • Transfer learning with Keras
    • Neural style transfers with cv2 using a pre-trained torch model
      • Understanding the NST algorithm
      • Implementation of NST with transfer learning
        • Ensuring NST with content loss
        • Computing the style cost
        • Computing the overall loss
      • Neural style transfer with Python and OpenCV
    • Summary
    • Questions
    • Further reading
  • Chapter 12: Additional Problems in Image Processing
    • Seam carving
      • Content-aware image resizing with seam carving
      • Object removal with seam carving
    • Seamless cloning and Poisson image editing
    • Image inpainting
    • Variational image processing
      • Total Variation Denoising
      • Creating flat-texture cartoonish images with total variation denoising
    • Image quilting
      • Texture synthesis
      • Texture transfer
    • Face morphing
    • Summary
    • Questions
    • Further reading
  • Other Books You May Enjoy
  • Index

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