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Revati S Misra

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Aryan Kumar

Aryan Kumar

26-Nov-2023

Image processing is a crucial aspect of modern computer vision applications, involving the manipulation and enhancement of images to extract valuable information. Here are some common image processing techniques used in computer vision applications:

Image Filtering:

  • Blur and Sharpening Filters: Used to smooth or enhance edges in an image.
  • Gaussian Filter: Reduces noise and blurs images using a Gaussian kernel.
  • Median Filter: Effective for removing salt-and-pepper noise by replacing each pixel's value with the median of its neighborhood.

Image Thresholding:

  • Binary Thresholding: Converts an image into a binary form based on a specified threshold.
  • Adaptive Thresholding: Adjusts the threshold dynamically in different regions of an image.

Image Morphology:

  • Erosion and Dilation: Used to remove or add pixels around the boundaries of objects, impacting image structure.
  • Opening and Closing: Combine erosion and dilation to remove noise and fill gaps in binary images.

Edge Detection:

  • Sobel and Prewitt Operators: Detect edges by computing gradients.
  • Canny Edge Detector: Utilizes multi-stage algorithms for optimal edge detection.

Histogram Equalization:

  • Enhances contrast: Adjusts the intensity distribution of an image to utilize the full available range.

Image Registration:

  • Aligns images: Used to align different images or frames in a sequence for further analysis or comparison.

Color Space Conversion:

  • Converts between color spaces: Allows the transformation of images from RGB to grayscale, HSV, etc.

These image processing techniques serve as building blocks for various computer vision applications, enhancing the quality of images, extracting meaningful information, and preparing the data for further analysis and interpretation.

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