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Dey, Sandipan. Python image processing cookbook: over 60 recipes to help you perform complex image processing and computer vision tasks with ease / Sandipan Dey. — 1 online resource (1 volume) : illustrations — <URL:http://elib.fa.ru/ebsco/2448942.pdf>.Дата создания записи: 18.09.2020 Тематика: Image processing.; Python (Computer program language); Computer vision.; Machine learning.; Computer vision; Image processing; Machine learning; Python (Computer program language) Коллекции: EBSCO Разрешенные действия: –
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Оглавление
- Cover
- Title Page
- Copyright and Credits
- About Packt
- Dedication
- Contributors
- Table of Contents
- Preface
- Chapter 1: Image Manipulation and Transformation
- Technical requirements
- Transforming color space (RGB → Lab)
- Getting ready
- How to do it...
- Converting RGB image into grayscale by setting the Lab space color channels to zero
- Changing the brightness of the image by varying the luminosity channel
- How it works...
- There's more...
- Applying affine transformation
- Getting ready
- How to do it...
- How it works...
- There's more...
- Applying perspective transformation and homography
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Creating pencil sketches from images
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Creating cartoonish images
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Simulating light art/long exposure
- Getting ready
- How to do it...
- How it works...
- There's more...
- Extended depth of field with mahotas
- See also
- Object detection using color in HSV
- Getting ready
- How to do it...
- How it works...
- See also
- Chapter 2: Image Enhancement
- Applying filters to denoise different types of noise in an image
- Getting ready
- How to do it...
- How it works...
- There's more...
- Image denoising with a denoising autoencoder
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Image denoising with PCA/DFT/DWT
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Image denoising with anisotropic diffusion
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Improving image contrast with histogram equalization
- Getting ready
- How to do it...
- How it works...
- There's more...
- Implementing histogram matching
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Performing gradient blending
- Getting ready
- How to do it...
- How it works...
- Edge detection with Canny, LoG/zero-crossing, and wavelets
- Getting ready
- How to do it...
- Canny/hysteresis thresholding
- LoG/zero-crossing
- Wavelets
- How it works...
- There's more...
- See also
- Applying filters to denoise different types of noise in an image
- Chapter 3: Image Restoration
- Restoring an image with the Wiener filter
- Getting ready
- How to do it...
- How it works...
- See also
- Restoring an image with the constrained least squares filter
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Image restoration with a Markov random field
- Getting ready
- How to do it...
- How it works...
- See also
- Image inpainting
- Getting ready
- How to do it...
- How it works...
- There's more...
- Image inpainting with convex optimization
- See also
- Image completion with inpainting using deep learning
- Getting ready
- How to do it...
- There's more...
- See also
- Image restoration with dictionary learning
- Getting ready
- How to do it ...
- There's more...
- Online dictionary learning
- See also
- Compressing an image using wavelets
- Getting ready
- How to do it...
- How it works...
- See also
- Using steganography and steganalysis
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Restoring an image with the Wiener filter
- Chapter 4: Binary Image Processing
- Applying morphological operators to a binary image
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Applying Morphological filters
- Getting ready
- How to do it...
- Computing the Euler number, eccentricity, and center of mass with mahotas/scikit-image
- Morphological image filters with mahotas
- Binary image filters with SimpleITK
- Dilation by reconstruction with skimage
- How it works...
- There's more...
- See also
- Morphological pattern matching
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Segmenting images with morphology
- Getting ready
- How to do it...
- Morphological watershed
- Blob detection with morphological watershed
- How it works...
- There's more...
- Blob detection with LOG scale-space
- See also
- Counting objects
- Getting ready
- How to do it...
- Blob separation and detection with erosion
- Object counting with closing and opening
- How it works...
- There's more...
- See also
- Applying morphological operators to a binary image
- Chapter 5: Image Registration
- Medical image registration with SimpleITK
- Getting ready
- How to do it...
- How it works...
- There's more
- See also
- Image alignment with ECC algorithm and warping
- Getting ready
- How to do it...
- How it works...
- There is more
- See also
- Face alignment with dlib
- Getting ready
- How to do it...
- How it works...
- There is more
- See also
- Robust matching and homography with the RANSAC algorithm
- Getting ready
- How to do it...
- How it works...
- See also
- Image mosaicing (panorama)
- Getting ready
- How to do it...
- Panorama with OpenCV-Python
- How it works...
- There is more
- See also
- Face morphing
- Getting ready
- How to do it...
- How it works
- There is more
- See also
- Implementing an image search engine
- Getting ready
- How to do it...
- Finding similarity between an image and a set of images with SIFT
- Steps to implement a simple image search engine
- There is more
- See also
- Medical image registration with SimpleITK
- Chapter 6: Image Segmentation
- Thresholding with Otsu and Riddler–Calvard
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Image segmentation with self-organizing maps
- Getting ready
- How to do it...
- How it works...
- There's more...
- Clustering handwritten digit images with SOM
- See also
- RandomWalk segmentation with scikit-image
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Human skin segmentation with the GMM-EM algorithm
- Getting ready
- How to do it...
- How it works...
- See also
- Medical image segmentation
- Getting ready
- How to do it...
- Segmentation with GMM-EM
- Brain tumor segmentation using deep learning
- Segmentation with watershed
- How it works...
- There's more...
- See also
- Deep semantic segmentation
- Getting ready
- How to do it...
- Semantic segmentation with DeepLabV3
- Semantic segmentation with FCN
- See also
- Deep instance segmentation
- Getting ready
- How to do it...
- How it works...
- See also
- Thresholding with Otsu and Riddler–Calvard
- Chapter 7: Image Classification
- Classifying images with scikit-learn (HOG and logistic regression)
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Classifying textures with Gabor filter banks
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Classifying images with VGG19/Inception V3/MobileNet/ResNet101 (with PyTorch)
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Fine-tuning (with transfer learning) for image classification
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Classifying traffic signs using a deep learning model (with PyTorch)
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Estimating a human pose using a deep learning model
- Getting ready
- How to do it...
- How it works...
- See also
- Classifying images with scikit-learn (HOG and logistic regression)
- Chapter 8: Object Detection in Images
- Object detection with HOG/SVM
- Getting started
- How to do it...
- How it works...
- There's more...
- See also
- Object detection with Yolo V3
- Getting started
- How to do it...
- How it works...
- There's more...
- See also
- Object detection with Faster R-CNN
- Getting started
- How to do it...
- How it works...
- There's more...
- See also
- Object detection with Mask R-CNN
- Getting started
- How to do it...
- How it works...
- There's more...
- See also
- Multiple object tracking with Python-OpenCV
- Getting started
- How to do it...
- How it works...
- There's more...
- See also
- Text detection/recognition in images with EAST/Tesseract
- Getting started
- How to do it...
- How it works...
- See also
- Face detection with Viola-Jones/Haar-like features
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Object detection with HOG/SVM
- Chapter 9: Face Recognition, Image Captioning, and More
- Face recognition using FaceNet
- Getting ready
- How to do it...
- How it works...
- See also
- Age, gender, and emotion recognition using deep learning models
- Getting ready
- How to do it...
- There's more...
- See also
- Image colorization with deep learning
- Getting ready
- How to do it...
- See also
- Automatic image captioning with a CNN and an LSTM
- Getting ready
- How to do it...
- How it works...
- See also
- Image generation with a GAN
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Using a variational autoencoder to reconstruct and generate images
- Getting ready
- How to do it...
- There's more...
- See also
- Using a restricted Boltzmann machine to reconstruct Bangla MNIST images
- Getting ready
- How to do it...
- See also
- Face recognition using FaceNet
- Other Books You May Enjoy
- Index
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