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Image processing is used in pattern recognition to identify the items in an image, and machine learning is then used to train the system to recognise changes in patterns.

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image-classification image-processing image-recognition image-segmentation k-means-clustering object-detection template-matching

image_processing's Introduction

Image Processing in Python

This repository focuses on image processing in Python. Here's a simplified overview:

  1. Introduction: Learn to manipulate and analyze images using Python.

  2. Key Libraries:

    • OpenCV: A powerful library for image processing.
    • Scikit-Image: Ideal for image segmentation and filters.
  3. Basic Image Operations:

    • Loading and displaying images.
    • Image resizing, rotation, and cropping.
    • Changing image color spaces (e.g., grayscale, RGB).
  4. Image Filtering and Enhancement:

    • Applying filters like blurring and sharpening.
    • Histogram equalization for enhancing contrast.
  5. Object Detection:

    • Detecting objects in images using techniques like Haar cascades.
    • Implementing custom object detection with machine learning.
  6. Image Segmentation:

    • Separating an image into distinct regions.
    • Useful for tasks like object recognition.

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