A fast X-corner detection method based on block-search strategy

Abstract

In order to detect X-corner (or X-point) features more accurately and apace, this article presents a novel and fast detection method based on block-by-block search strategy. Unlike general pixel-by-pixel searching method, the sampling window is first moved along the image block-by-block to find the X-corner candidates rapidly keeping in view the fourstep and min-step-distance constraints. During the motion, some overlap is kept between the adjacent sampling windows in order to ensure that all X-corners could have a chance to reside inside, avoiding the possibility of that some Xcorners may locate on the edge. Moreover, labeling technology is adopted to prevent duplicate candidates. After the collection of X-corner candidates, the neighborhood variance and centrosymmetry constraints are used to exclude outliers, and the intersection lines is calculated as the sub-pixel position of true X-corner. The experimental results using synthetic and real images show that the presented method approximately takes just about 13 ms to detect 52 X-corners in an image size of 1024 3 768 on a computer having Intel Core i3 CPU at 3.6 GHz and 4GB RAM. The proposed method has faster detection speed compared with the latest methods such as ChESS, SC, and Micron Tracker system while possessing the same or higher detection precision.

Robust 3D Object Tracking with X-Triplet Markers: A Machine Learning-Based Approach

“…However, since these approaches are all based on pixel-bypixel search strategies, their speed is limited. To accelerate the search efficiency, a block-by-block based method called Block-X was proposed in [39], but it is unable to filter out certain false X-corners. Most of the above methods first extract X-corner candidates and then rely on hand-crafted thresholds to filter out outliers, which is not robust to noise and has limited generalization ability under different scales, camera poses and illumination conditions.…”

“…Hessian-X [18] leverages the eigenvector directions of the Hessian matrix for orientation but it is susceptible to noise. On the other hand, SC-X [36] and Block-X [39] utilize the edge direction as the orientation and are more stable. Although the orientation information of Xcorners is considered in Hessian-X [18], SC-X [36] and Block-X [39], its utilization has not been discussed.…”

“…On the other hand, SC-X [36] and Block-X [39] utilize the edge direction as the orientation and are more stable. Although the orientation information of Xcorners is considered in Hessian-X [18], SC-X [36] and Block-X [39], its utilization has not been discussed. While in our approach, the orientation information represented by the edge direction is obtained and explicitly utilized to extract Tri-X candidates in 2D image space.…”

“…We have addressed some issues in our previous study [41], but the speed and accuracy were low due to the adoption of the Hessian matrix in Hessian-X [18]. Hence, based on our previous research, the SC-X [36] and Block-X [39], a faster and more robust X-corner detection method based on the block search strategy and support vector machine(SVM) is proposed. Compared to existing methods that adopt hand-crafted thresholds to filter out false X-corners, our machine learning based approach is more robust to noise, different scales, camera poses and illumination conditions.…”

Camera Calibration Using Multiple Unordered Coplanar Chessboards

“…The high demand for such flexible automatic 2D calibration tools -also used for thermal and underwater cameras (e.g. Javadnejad et al, 2019;Shortis, 2019) -is directly reflected in a steadily ongoing research which addresses issues such as computational efficiency, poor lighting/contrast, non-homogeneous illumination, overexposure, image blur, low image resolution, image noise, si-gnificant image distortion, missing corner points or partial occlusion of patterns, extreme imaging poses, board printing inaccuracy or deviations from planarity (recent works include Duda & Frese, 2018;Yamaguchi et al, 2018;Yan et al, 2018;Hannemose et al, 2019;Meng et al, 2019;Wholfeil et al, 2019;Zhu et al, 2019). Deep learning tools have also been recently used for robust detection of checkboard corners (Donné et al, 2016;Raza et al, 2019).…”