Sparse Checkerboard Corner Detection from Global Perspective

Jiwoo Kang, H. Yoon, Seongmin Lee, Sanghoon Lee
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引用次数: 1

Abstract

Detecting corners from an image is an essential step for camera calibration in geometric computer vision and image processing applications. In this paper, a novel framework is proposed to detect sparse checkerboard corners with a global context from an image. The proposed framework addresses two major problems that the previous neural network-based corner detection networks have had: locality and non-sparsity. Our framework encodes the global context from an image and uses the context to determine the per-patch existence of the checkerboard. It enables the network to distinguish between the checkerboard pattern and pattern-like noise in the image background while preserving pixel-level detection details. Also, the patch-wise sparse regularization is introduced using counting distribution to obtain clear-cut predictions while maintaining the true positive rate. The experimental results demonstrate that parsing the global context helps the proposed network to decrease false positive detection significantly. Also, the proposed counting regularization improves to detect true positives while decreasing false negatives concurrently. It enables the proposed network to precisely detect sparse checkerboard corners, leading to significant improvements over the state-of-the-art methods.
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全局视角下的稀疏棋盘角点检测
在几何计算机视觉和图像处理应用中,角点检测是相机标定的重要步骤。本文提出了一种基于全局背景的图像稀疏棋盘角点检测框架。该框架解决了以往基于神经网络的角点检测网络存在的两个主要问题:局部性和非稀疏性。我们的框架从图像中编码全局上下文,并使用上下文来确定每个补丁中棋盘的存在。它使网络能够区分图像背景中的棋盘图案和图案样噪声,同时保留像素级检测细节。在保持真阳性率的同时,引入了基于计数分布的稀疏正则化算法。实验结果表明,分析全局上下文有助于该网络显著减少误报检测。此外,所提出的计数正则化改进了检测真阳性的同时减少假阴性。它使所提出的网络能够精确地检测稀疏的棋盘角,从而大大改进了最先进的方法。
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