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  2. scikit-image - Wikipedia

    en.wikipedia.org/wiki/Scikit-image

    scikit-image (formerly scikits.image) is an open-source image processing library for the Python programming language. [2] It includes algorithms for segmentation , geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection , and more. [ 3 ]

  3. Digital image processing - Wikipedia

    en.wikipedia.org/wiki/Digital_image_processing

    The purpose of early image processing was to improve the quality of the image. It was aimed for human beings to improve the visual effect of people. In image processing, the input is a low-quality image, and the output is an image with improved quality. Common image processing include image enhancement, restoration, encoding, and compression.

  4. Convolutional neural network - Wikipedia

    en.wikipedia.org/wiki/Convolutional_neural_network

    A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]

  5. Kernel (image processing) - Wikipedia

    en.wikipedia.org/wiki/Kernel_(image_processing)

    In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more.This is accomplished by doing a convolution between the kernel and an image.

  6. Motion analysis - Wikipedia

    en.wikipedia.org/wiki/Motion_analysis

    Motion analysis is used in computer vision, image processing, high-speed photography and machine vision that studies methods and applications in which two or more consecutive images from an image sequences, e.g., produced by a video camera or high-speed camera, are processed to produce information based on the apparent motion in the images.

  7. Gradient-domain image processing - Wikipedia

    en.wikipedia.org/wiki/Gradient-domain_image...

    Alternatively, the pasting can be performed in the gradient domain: if the differences between pixels are pasted rather than the actual pixel values, there is sometimes much less user input needed to achieve a clean result. The following example demonstrates the use of gradient-domain image processing to paste from one image to another seamlessly.

  8. Normalization (image processing) - Wikipedia

    en.wikipedia.org/wiki/Normalization_(image...

    In more general fields of data processing, such as digital signal processing, it is referred to as dynamic range expansion. [1] The purpose of dynamic range expansion in the various applications is usually to bring the image, or other type of signal, into a range that is more familiar or normal to the senses, hence the term normalization.

  9. Gaussian blur - Wikipedia

    en.wikipedia.org/wiki/Gaussian_blur

    In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function (named after mathematician and scientist Carl Friedrich Gauss). It is a widely used effect in graphics software, typically to reduce image noise and reduce detail.