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 important because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations. 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 percent of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115 percent, SIFT 195 percent). 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 of very high quality.

On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening

“…A standalone feature detector FAST (Features from Accelerated Segment Test) [23] provides significant number of candidate points for extraction while maintaining low computational cost. The detector-extractor frameworks BRIEF (Binary Robust Independent Elementary Features) [24], and BRISK (Binary Robust Invariant Scalable Key-points) [18] offer integer-space representations, avoiding the floating point operation of earlier SURF/SIFT variants, for faster extraction and subsequent computation on embedded platforms.…”

Energy flow: image correspondence approximation for motion analysis

“…6,11,23,26 In the meanwhile, with the same settings except using SIFT 31 and optical flow features 17 replacing our energy flow features, we get the ARR which is shown in Table 2. Table 3 shows the recognition results of our approach (ARR is 27.92%) and others 26,32,33 on HMDB database. Also we substitute energy flow using optical flow and SIFT features for comparison, and the corresponding recognition rates are also given.…”

An optimisation approach to the design of a fast, compact and distinctive binary descriptor

“…The sampling pairs in ORB are determined in a learning process involving a greedy algorithm working on 300 K keypoints, maximising the amount of information carried by the descriptor. Here, FAST [20] detects keypoints. The dominant orientation in ORB is obtained using the intensity centroid approach [21].…”

“…Therefore, a simplified version of the descriptor was used. It contains only four patches (432 bits) with S p = [5,10,15,20], and obtained F C = 1683.9 with SURF keypoints.…”