Faster and better: a machine learning approach to corner detection

Abstract: The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is importand because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations [Schmid et al 2000]. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection, and using machine learning we derive a feature detector from this which can fully process live PAL video using less than 5% of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115%, SIFT 195%). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and very high quality.

• The paper presents a machine learning heuristic that reduces processing time for corner detection to under 5% of full frame requirements compared to traditional methods.
• It generalizes the detector to optimize repeatability, ensuring reliable feature matching across different viewpoints.
• Rigorous evaluations reveal FAST-ER’s superior speed, quality, and noise robustness, significantly benefiting real-time vision applications like SLAM and image recognition.

Faster and Better: A Machine Learning Approach to Corner Detection

This paper by Edward Rosten, Reid Porter, and Tom Drummond introduces novel improvements in the domain of corner detection, central to many computer vision applications such as SLAM, image matching, and recognition. The authors focus on enhancing both the speed and repeatability of corner detectors. Existing methods, although well-studied, typically exhibit limitations in processing live video at full frame rates while providing robust feature extraction.

Innovations and Methodology

The authors propose three key advancements:

1. Heuristic for Feature Detection: A new heuristic is developed for corner detection. Using machine learning, this heuristic is transformed into a feature detector capable of processing live PAL video while utilizing less than 5% of its processing time. This is contrasted against traditional detectors like the Harris (115%) and SIFT (195%), which cannot operate at frame rate.
2. Generalization for Increased Repeatability: By generalizing the heuristic detector, the authors enable optimization for repeatability with negligible loss of efficiency. This enhancement ensures that real-world points are consistently detected across multiple views, a crucial property for many vision-based tasks.
3. Rigorous Comparison using Repeatability Criterion: The methods are evaluated using repeatability, where the principal metric is the proportion of features that are detected across different viewpoints. The tests demonstrate that the heuristic detector significantly surpasses existing detectors in terms of both speed and quality.

Detailed Evaluation

FAST-ER Detector: Building on the FAST (Features from Accelerated Segment Test) approach, the heuristic model is optimized using machine learning to construct decision trees tailored for rapid and robust corner detection. The training process involves deriving a classifier that evaluates image patches for corner properties, organized in a decision tree for efficient execution.

Theoretical and Practical Implications: One of the paper's most compelling conclusions is the potential mismatch between intuitive corner detection models and those optimized purely for repeatability and speed. The results show that data-driven approaches can outperform handcrafted models when tuned for specific performance metrics, emphasizing the importance of focusing on desired outputs rather than preconceived algorithmic paths.

Comparative Performance

The paper's experimental results are nuanced and thorough. FAST-ER, while derived from heuristic beginnings and machine learning enhancements, outperforms classical detectors like Harris, Shi-Tomasi, DoG, and SUSAN in most scenarios. The robustness to image noise further solidifies its practical utility.

Speed: FAST-ER's processing requirement of well below 5% of available compute resources starkly contrasts with the prohibitively high requirements of legacy detectors. This makes FAST-ER particularly suitable for real-time applications on resource-limited hardware, such as mobile devices and embedded systems in robotics.

Future Directions

The study's results suggest several exciting avenues for future research:

• Adaptive Learning: Incorporating online learning mechanisms could continuously evolve the detector to adapt to new environments or scene characteristics.
• Integration with Descriptor Methods: Investigation into pairing FAST-ER with advanced descriptors could yield improvements in comprehensive feature-based techniques, further enhancing the robustness of application systems.

Theoretical Exploration: Further studies could explore the bounds of decision tree complexity and its impact on both detectability and computational load, potentially leading to more generalizable models across various computer vision tasks.

Conclusion

The paper by Rosten et al. pushes the boundary on what is achievable in corner detection by marrying speed with repeatability. Their approach demonstrates that leveraging machine learning to tackle specific, quantifiable performance metrics can yield superior practical results over traditional handcrafted methods. Through FAST-ER and its predecessors, they present a robust framework for efficient and reliable feature detection, which is poised to benefit a wide array of vision-based technologies and applications.