How to calculate the lbp codes at the ends of the images?

ghz 15hours ago ⋅ 4 views

To calculate Local Binary Patterns (LBP) at the ends (or borders) of an image, the primary challenge is handling pixels at the boundaries, where you don't have neighboring pixels on all sides. In LBP, each pixel is compared to its surrounding pixels, so at the image borders, there aren't enough neighboring pixels to form a complete pattern.

There are a few common approaches to handle this situation when calculating LBP at the image boundaries:

1. Padding the Image

  • You can pad the image by replicating the pixel values at the borders. For example, you can duplicate the border pixels to extend the image.
  • Common padding techniques include:
    • Replication Padding: Duplicate the edge pixels.
    • Constant Padding: Use a constant value (such as 0 or the mean pixel value) to pad the image.
    • Reflect Padding: Reflect the pixels at the borders.

After padding the image, you can then calculate the LBP normally, as you'll have neighboring pixels for every pixel in the image.

2. Ignoring Boundaries

  • If you don’t want to pad the image, you can simply ignore the boundary pixels when calculating the LBP. This means you will only calculate the LBP for pixels that have full neighborhoods (i.e., pixels that are not on the edges).

3. Using Circular Boundary Conditions

  • Another approach is to treat the image as if it wraps around. This is known as circular boundary conditions. When you calculate the LBP for a boundary pixel, you treat the pixel’s neighbors on the other side of the image as its neighbors.

Example of Calculating LBP with Padding

Here’s a basic Python implementation of LBP with replication padding at the borders, using the scikit-image library:

import numpy as np
from skimage import feature
import cv2
import matplotlib.pyplot as plt

def calculate_lbp_with_padding(image):
    # Apply padding to the image
    padded_image = np.pad(image, pad_width=1, mode='edge')

    # Calculate LBP for each pixel in the padded image
    lbp_image = feature.local_binary_pattern(padded_image, P=8, R=1, method='uniform')

    # Remove padding to get the original size
    lbp_image = lbp_image[1:-1, 1:-1]

    return lbp_image

# Load an image (grayscale)
image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Calculate LBP with padding
lbp_result = calculate_lbp_with_padding(image)

# Show the result
plt.imshow(lbp_result, cmap='gray')
plt.title("LBP with Padding")
plt.show()

How it Works:

  • The image is padded using np.pad with the 'edge' mode, which replicates the border pixels.
  • Then, the LBP is calculated for the padded image, and after that, the padding is removed to return the result to the original image size.

Handling LBP Calculation with Padding

  • Padding with border values ensures that boundary pixels can still have neighbors to form their LBP code.
  • The padded image will allow you to calculate LBP for all pixels, including those at the edges, using the padded values as neighboring pixels.

Conclusion

When calculating LBP at the ends of an image, padding is a common approach to handle boundary pixels. The padding ensures that boundary pixels have enough neighbors to form a valid LBP code. You can use various padding methods (e.g., replication, constant, or reflective) depending on your requirements, but padding with the edge values is a simple and effective solution.

#py