Arc-support Line Segments Revisited: An Efficient and High-quality Ellipse Detection (1810.03243v5)

Abstract: Over the years many ellipse detection algorithms spring up and are studied broadly, while the critical issue of detecting ellipses accurately and efficiently in real-world images remains a challenge. In this paper, we propose a valuable industry-oriented ellipse detector by arc-support line segments, which simultaneously reaches high detection accuracy and efficiency. To simplify the complicated curves in an image while retaining the general properties including convexity and polarity, the arc-support line segments are extracted, which grounds the successful detection of ellipses. The arc-support groups are formed by iteratively and robustly linking the arc-support line segments that latently belong to a common ellipse. Afterward, two complementary approaches, namely, locally selecting the arc-support group with higher saliency and globally searching all the valid paired groups, are adopted to fit the initial ellipses in a fast way. Then, the ellipse candidate set can be formulated by hierarchical clustering of 5D parameter space of initial ellipses. Finally, the salient ellipse candidates are selected and refined as detections subject to the stringent and effective verification. Extensive experiments on three public datasets are implemented and our method achieves the best F-measure scores compared to the state-of-the-art methods. The source code is available at https://github.com/AlanLuSun/High-quality-ellipse-detection.

• The paper introduces an innovative ellipse detection technique that extracts arc-support line segments and links them using saliency and polarity constraints.
• The method employs both local selection and global search strategies, refining candidates through hierarchical clustering in a five-dimensional parameter space.
• Experimental results show superior F-measure, precision, and recall on multiple datasets, demonstrating its practical efficiency for real-world applications.

Overview of "Arc-support Line Segments Revisited: An Efficient High-quality Ellipse Detection"

In the paper, "Arc-support Line Segments Revisited: An Efficient High-quality Ellipse Detection," the authors introduce an advanced method for ellipse detection in images that combines accuracy with computational efficiency. The proposed method leverages the concept of arc-support line segments to enhance the detection process, effectively balancing high detection quality with practical application efficiency.

Methodology

The approach begins with the extraction of arc-support line segments from an image. These segments serve as simplified representations of curves while retaining crucial properties such as convexity and polarity, essential for ensuring accurate ellipse detection. The process involves identifying groups of arc-support line segments that likely belong to the same ellipse through a robust linking algorithm. This step forms the basis for further analysis.

The initial ellipses are obtained using two complementary strategies:

1. Local Selection: High-saliency arc-support groups are individually fitted to ellipses based on a predefined saliency threshold.
2. Global Searching: Valid pairs of arc-support groups undergo a global search to uncover additional latent ellipses. Various constraints, including polarity, geometric positioning, and adaptive inliers, guide the fitting process to ensure efficiency.

This generates an initial set of ellipse candidates. These candidates are subsequently refined through hierarchical clustering of their five-dimensional parameter space, culminating in a more precise set of potential ellipses. Each candidate then undergoes verification, where stringent criteria are applied to finalize the detections, ensuring both accuracy and quality.

Experimental Results

Extensive experiments have been conducted using the method on three publicly available datasets: the Traffic Sign Dataset, the Prasad Dataset, and the PCB Dataset. The method consistently outperformed existing state-of-the-art techniques, achieving superior F-measure scores across all datasets. Specifically, the technique exhibits notable improvement in precision and recall metrics, underpinning its effectiveness in minimizing false positives and negatives.

The computational complexity of the approach aligns with quadratic time complexity concerning image size and arc-support group count. This efficiency is a marked improvement over traditional methods, which often suffer from higher computational demands.

Implications and Future Directions

The proposed ellipse detection method holds significant implications for practical applications in fields like computer vision, where accurate shape detection is critical. Its ability to handle complex real-world conditions, such as cluttered backgrounds and occlusion, suggests potential utility in diverse industrial applications. The integration of novel constraints and efficient clustering sets a new benchmark for upcoming research in ellipse detection.

Additionally, the exploration of polarity-specific detection provides an innovative avenue, allowing for targeted identification of brighter or darker ellipses within an image. This capability expands the functionality of the detection system, introducing specificity that could be advantageous in particular use cases.

Looking forward, further enhancements could focus on extending this methodology to other geometric shapes or integrating it with machine learning models for even more robust performance. Exploration of real-time applications, leveraging advancements in hardware, might also be a fruitful direction, offering enhancements in speed that are demanded by on-the-fly computer vision systems.

In conclusion, this paper presents a well-rounded and technically sound method that significantly contributes to the field of image processing, specifically in high-quality ellipse detection, showcasing both innovation and practicality.