https://github.com/heisenberg141/probability-based-boundary-detection

This Repository consists of implementation of probability based boundary detection algorithm which gives more accurate results than canny edge detection.
https://github.com/heisenberg141/probability-based-boundary-detection

boundary-detection canny-edge-detection computer-vision edge-detection gabor-filters gaussian-filters image-gradients image-processing kmeans-clustering lm-filters numpy opencv python3 sobel-filter texton-maps

Last synced: 3 months ago
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This Repository consists of implementation of probability based boundary detection algorithm which gives more accurate results than canny edge detection.

• Host: GitHub
• URL: https://github.com/heisenberg141/probability-based-boundary-detection
• Owner: heisenberg141
• Created: 2023-03-10T19:20:21.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-03-10T19:57:31.000Z (over 3 years ago)
• Last Synced: 2025-03-26T15:48:15.740Z (over 1 year ago)
• Topics: boundary-detection, canny-edge-detection, computer-vision, edge-detection, gabor-filters, gaussian-filters, image-gradients, image-processing, kmeans-clustering, lm-filters, numpy, opencv, python3, sobel-filter, texton-maps
• Language: Python
• Homepage:
• Size: 7.2 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          

# Probability-based-Boundary-Detection

This repository consists of comparison of baseline edge detection algorithms like Canny and Sobel with [Probability of boundary detection algorithm](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/papers/amfm_pami2010.pdf). A simpler version of PB algorithm has been implemented which considers texture, color and intensity discontinuities. This algorithm predicts per pixel probability of the boundary detected. The original image and the output of implementation is shown below:

 

The algorithm of PBLite detection is shown below:



The main steps for implementing the same are:

## Step 1: Feature extraction using Filtering

The filter banks implemented for low-level feature extraction are Oriented Derivative if Gaussian Filters, Leung-Malik Filters (multi-scale) and Gabor Filter.

  

## Step 2: Extracting texture, color and brightness using clustering

Filter banks can be used for extraction of texture properties but here all the three filter banks are combined which results into vector of filter responses. As filter response vectors are generated, they are clustered together using k-means clustering. For Texton Maps k = 64 is used; Color and Brightness Maps k= 16 is used.

  

The gradient measurement is performed to know how much all features distribution is changing at a given pixel. For this purpose, half-disc masks are used.

  

## Step 3: Pb-Score

The gradient maps which are generated are combined with classical edge detectors like Canny and Sobel baselines for weighted average.



## Run Instructions

```

python Wrapper.py

```

# File structure

    ├── Code

    |  ├── Wrapper.py

    |  ├── utils.py

    ├── BSDS500

    ├── Results

    |  ├── color_gradient_maps

    |  ├── color_maps

    |  ├── intensity_gradient_maps

    |  ├── intensiy_maps

    |  ├── final_output

    |  ├── texture_maps

    |  ├── texture_gradient_maps