Deep Hough-Transform Line Priors (2007.09493v1)

Abstract: Classical work on line segment detection is knowledge-based; it uses carefully designed geometric priors using either image gradients, pixel groupings, or Hough transform variants. Instead, current deep learning methods do away with all prior knowledge and replace priors by training deep networks on large manually annotated datasets. Here, we reduce the dependency on labeled data by building on the classic knowledge-based priors while using deep networks to learn features. We add line priors through a trainable Hough transform block into a deep network. Hough transform provides the prior knowledge about global line parameterizations, while the convolutional layers can learn the local gradient-like line features. On the Wireframe (ShanghaiTech) and York Urban datasets we show that adding prior knowledge improves data efficiency as line priors no longer need to be learned from data. Keywords: Hough transform; global line prior, line segment detection.

• The paper proposes integrating classical Hough-Transform line priors into deep learning networks for enhanced line segment detection.
• It introduces a trainable HT-IHT block that leverages Hough and inverse Hough transformations to embed global line parameterization within deep architectures.
• Experiments show the method improves line detection performance and data efficiency on datasets like Wireframe and York Urban, reducing the need for extensive annotation.

Analysis of "Deep Hough-Transform Line Priors"

In the paper titled "Deep Hough-Transform Line Priors," the authors explore a method to enhance line segment detection in computer vision by integrating classical knowledge-based geometric priors with learning-based approaches. Historically, line detection relied on either image gradients and Hough transform variants or deep learning on manually annotated datasets. This paper proposes a hybrid method combining classical priors with deep networks to enable line detection with fewer labeled samples.

Methodology

The authors introduce a process of integrating line priors into deep networks using a trainable Hough transform block. This approach leverages the traditional Hough transform, which represents lines in terms of offset and angle. By embedding this concept into deep neural networks, the convolutional layers focus on learning local gradient-like features, while the Hough transform provides global line parameterization. Specifically, the method employs a block, termed HT-IHT, that applies both Hough and inverse Hough transformations. The HT-IHT block aggregates local image data into a global line context, ultimately improving line detection efficiency.

Contributions and Experiments

The paper outlines several contributions:

1. Incorporation of Global Line Priors: The paper emphasizes the introduction of global geometric line priors through Hough transformations integrated within deep networks.
2. Improved Data Efficiency: The framework reduces the dependency on extensive annotated datasets by embedding prior knowledge into the model's architecture.
3. End-to-End Trainable HT-IHT Block: The HT-IHT block allows convolutional operations in the Hough domain, facilitating global line parameterization learning.

Experiments were conducted using the Wireframe (ShanghaiTech) and York Urban datasets, alongside a controlled Line-Circle dataset. The findings confirm that incorporating the HT-IHT block results in better line detection with less training data. The paper illustrates notable improvements in average precision (AP) across several scenarios, particularly when training data is limited. Additionally, the proposed HT-LCNN and HT-HAWP models outperform existing baselines, indicating the effectiveness of integrating classical priors into modern deep architectures.

Practical and Theoretical Implications

The research highlights the advantages of blending classical Hough-based line detection with contemporary neural networks. The practical implication is twofold: improved line detection performance and reduced need for extensive training datasets, thus lowering manual annotation costs. Theoretically, the findings suggest a promising direction for enhancing AI models via the integration of traditional geometric principles, potentially applicable to other feature detection tasks beyond line segments.

Future Prospects

Looking ahead, this hybrid methodology could inspire further research in embedding classical computer vision techniques into learning frameworks, offering a robust alternative to purely data-driven approaches. Future studies might explore expanding this concept to other domains such as pattern recognition or object detection, where global structural information can complement local features.

In conclusion, the paper "Deep Hough-Transform Line Priors" provides valuable insights into enhancing data efficiency and performance in line segment detection by coupling Hough transform with deep network capabilities. The approach demonstrates a significant step towards more resource-efficient AI models, integrating foundational and contemporary vision techniques.