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Generalized Hough Transform Algorithm for Shape Detection With Multi-Scale and Multi-Shift Detection (Algorithm 3)

Output using algo1

Output using algo1

--Output Using Algorithm 3

This advanced algorithm detects shapes using the Generalized Hough Transform with added multi-scale and multi-shift detection capabilities.

Key Components and Steps

1. Edge Detection and Gradient Orientation

  • Canny Edge Detection:
    • Uses the canny function to detect edges effectively.
  • Gradient Orientation Calculation:
    • Computes gradient orientation using the Sobel filter.

2. R-Table Construction

  • Building the R-Table:
    • Constructs an R-Table based on detected edges and orientations.

3. Accumulation of Gradients

  • Accumulate Gradients Using R-Table:
    • Accumulates votes in an accumulator array.

4. Multi-Scale and Multi-Shift Detection

  • Generate Shifts:
    • Creates a list of possible shifts for the reference image.
  • Multi-Scale Detection:
    • Tests different scales of the reference image to detect size variations.
  • Multi-Shift Detection:
    • Tests various shifts to find the best alignment.

5. Overlay and Visualization

  • Overlay the Reference Image:
    • Overlays the reference image on the query image at the detected position.
  • Visualization:
    • Visualizes the results including the reference image, query image with detected points, and the final overlay.

6. Testing and Usage

  • Shape-to-Image Conversion:
    • Converts shapes to binary images.
  • Testing:
    • Reads shape data from CSV files, converts them to images, and applies the Generalized Hough Transform.

Example Workflow

  1. Read and Convert Shape Data:

    • Read and convert shape data from CSV files to binary images.

  2. Apply Generalized Hough Transform:

    • Apply the Generalized Hough Transform with multi-scale and multi-shift detection.

  3. Visualize Results:

    • Visualize the best match and overlayed reference image.

Algo 3 Summary

  • Edge Detection: Uses Canny edge detection and Sobel filter.
  • R-Table Construction: Creates a lookup table for edge points.
  • Gradient Accumulation: Accumulates votes in an accumulator array.
  • Multi-Scale and Multi-Shift Detection: Tests different scales and shifts.
  • Overlay and Visualization: Displays the detected shapes and overlays the reference image. .