Progressive Feature Learning for Facade Parsing With Occlusions.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Deep learning models struggle with facade parsing in occluded image areas due to poor feature representation.
• Occlusions in facade images present a significant challenge for accurate pixel classification.

Purpose of the Study:

• To develop an occlusion-immune deep learning architecture for improved facade parsing.
• To enhance feature representation for pixels in occluded regions of facade images.

Main Methods:

• Utilized Bayesian uncertainty evaluation to identify occluded regions in facade images.
• Proposed a progressive feature re-expression strategy, processing pixels from easier to harder contexts.
• Introduced a context enhancement module (CEM) with directional strip kernels for improved feature aggregation.

Main Results:

• The proposed method significantly improves facade parsing performance on occluded regions.
• Achieved state-of-the-art results on established facade datasets.
• Demonstrated the effectiveness of progressive learning and directional strip kernels.

Conclusions:

• Progressive feature learning effectively addresses challenges in facade parsing with occlusions.
• The developed architecture offers a robust solution for real-world facade image analysis.
• The method enhances the ability of deep models to interpret complex and partially obscured architectural details.