Abstract:
This paper proposes a method to improve the segmentation of traffic images after edge preservation using anisotropic
diffusion filtering. Anisotropic diffusion filtering is applied to the traffic images to preserve important edges and structures while
reducing noise. However, the resulting images may still contain artifacts that can affect the accuracy of segmentation tasks such as
object detection and lane delineation. To address this issue, we introduce a novel post-processing technique to refine the
segmentation results. The proposed method leverages the edge-preserving properties of anisotropic diffusion filtering to enhance the
boundaries of segmented objects and remove spurious artifacts. Experimental evaluations conducted on a variety of traffic scenes
demonstrate the effectiveness of the proposed approach in improving the segmentation accuracy compared to traditional methods.
The results highlight the potential of integrating edge preservation techniques with segmentation algorithms for enhanced
performance in traffic image analysis tasks. The proposed methodology uses various phases such Noise Reduction, Edge
Preservation, Improved Segmentation, Enhanced Visibility, Adaptive Filtering. Step by step each algorithm different operations on
transportation’s Images. Using Anisotropic diffusion filtering and Two-Directional Two-Dimension Principal Component Analysis
(2D2PCA) reduces more than 30% original image size and also preserve edges more than 95% of original images. The reduced size
images are very useful for future work.
