Traffic Sign Image Segmentation GUI Interface

This program implements a GUI interface for traffic sign image segmentation, employing three main segmentation approaches: Otsu's method for automatic threshold determination, iterative threshold selection algorithm for optimal value convergence, and region growing algorithm based on pixel connectivity analysis.

To further enhance traffic sign image segmentation performance, the following complementary methods could be considered:

1. Color-based segmentation method: By extracting color information from traffic sign images using color space conversion (e.g., RGB to HSV), images can be segmented into distinct regions through color clustering algorithms like K-means, enabling effective sign isolation.

2. Texture-based segmentation method: Through analysis of texture features using techniques like Gabor filters or Local Binary Patterns (LBP), images can be partitioned into regions with similar texture characteristics, facilitating precise sign segmentation through texture classification.

3. Shape-based segmentation method: By analyzing shape information through contour detection algorithms (e.g., Canny edge detection combined with Hough transform), images can be segmented into regions with similar geometric properties, allowing accurate sign identification via shape matching techniques.

These methods represent viable approaches for optimization. By strategically combining multiple segmentation techniques through ensemble methods or decision-level fusion algorithms, more accurate and robust traffic sign image segmentation can be achieved. We hope these methodological insights prove beneficial for your implementation!