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image analysis and visualization in Python with scikit-image, napari, and friends | SciPy 2023
Explore the world of image analysis and visualization in Python with scikit-image, napari, and friends, covering topics like Gaussian filters, convolution, and more.
Image Analysis and Visualization with Python
- Gaussian Filter: A Gaussian filter is used to smooth an image, which can help remove noise and reduce the intensity of the signal.
- Convolution: Convolution is a mathematical operation that applies a kernel to an image to produce a new image.
- Kernel: A kernel is a small matrix that is used to perform a mathematical operation on an image.
- Image Segmentation: Image segmentation is the process of partitioning an image into its constituent parts or objects.
- Watershed Transform: The watershed transform is an algorithm used to separate objects in an image based on their intensity values.
- Napari: Napari is a Python library that provides an interactive 3D viewer for images.
- Image Morphology: Image morphology is the study of the shape and structure of images.
- Region Propagation: Region propagation is a technique used to extract information from an image by labeling pixels based on their intensity values.
- Intensive Color: Intensive color is a color that is not dependent on the background, but rather on the color itself.
- Scikit-image: Scikit-image is a Python library for image processing and analysis.
- RGB (Red, Green, Blue): RGB is a color model that uses three primary colors to create a wide range of colors.
- RGBA (Red, Green, Blue, Alpha): RGBA is a color model that adds an additional channel to the RGB model for transparency.
- Thresholding: Thresholding is a method of image segmentation that separates an image into two or more regions based on a threshold value.
- Minimum and Maximum Values: The minimum and maximum values of an image can be used to determine the range of intensity values within the image.
- Scipy: Scipy is a Python library for scientific computing and image processing.
- numpy: NumPy is a Python library for numerical computation.
- Polar Representation: Polar representation is a way of representing a 2D image in a 1D format using polar coordinates.
- Intensity Values: Intensity values are the numerical values that represent the brightness of each pixel in an image.
- Image Denoising: Image denoising is the process of removing noise from an image.
- De-Noising: De-noising is the process of removing noise from an image.
- Gaussian Blurring: Gaussian blurring is a technique used to reduce the noise in an image by convolving it with a Gaussian kernel.
- Median Filter: A median filter is a digital filter that replaces each pixel with the median value of the neighboring pixels.
- Anisotropic Diffusion: Anisotropic diffusion is a technique used to denoise an image by performing a series of operations that refine the image in a direction perpendicular to the edges.
- Iterative Algorithm: An iterative algorithm is an algorithm that repeats a set of instructions until a desired result is achieved.
- Grid-Based Algorithm: A grid-based algorithm is an algorithm that uses a grid to organize and manipulate an image.
- Image Processing: Image processing is the process of modifying an image to achieve a specific goal.
- Computer Vision: Computer vision is the field of study that deals with enabling computers to see and understand the world visually.
- Data Visualization: Data visualization is the process of creating a visual representation of data.
- Interactive Visualization: Interactive visualization is a form of data visualization that allows the user to interact with the data.
- Nepari: Nepari is a Python library that provides an interactive 3D viewer for images.
- Scikit-image installation: Scikit-image can be installed using pip, the Python package manager.
- Numpy installation: NumPy can be installed using pip, the Python package manager.
- OpenCV installation: OpenCV can be installed using pip, the Python package manager.
- Pillow installation: Pillow can be installed using pip, the Python package manager.
- matplotlib installation: Matplotlib can be installed using pip, the Python package manager.