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Selective Use of Optimal Image Resolution for Depth from Multiple Motions based on Gradient Scheme

机译:基于梯度方案的多个动作的深度选择性地使用最佳图像分辨率

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The gradient-based depth from motion method is effective for obtaining a dense depth map. However, the accuracy of the depth map recovered only from two successive images is not so high, and hence, to increase the depth information by tracking corresponding image points through an image sequence is often performed by using, for example, the Kalman filter-like technique. Alternatively, multiple image pairs generated by random small camera rotations around a reference direction can be used for gaining much information of depth without such the tracking procedure. In the framework of this strategy, in this study, to further improve the accuracy, we propose a selective use of the optimal image resolution. The appropriate resolution image is required to have a linear intensity pattern which is the most important supposition for the gradient method often used for dense depth recovery based on the theory of "shape from motion." The performance of our proposal is examined through numerical evaluations using artificial images.
机译:来自运动方法的基于梯度的深度对于获得密集的深度图是有效的。然而,仅从两个连续图像恢复的深度映射的精度不是那么高,因此,通过使用例如卡尔曼滤波器类似地执行通过图像序列来跟踪相应的图像点来增加深度信息。技术。或者,由参考方向周围的随机小型摄像机旋转产生的多个图像对可用于在没有这种跟踪过程的情况下获得深度的大量信息。在这项策略的框架中,在本研究中,为了进一步提高准确性,我们提出了选择性地使用最佳图像分辨率。需要适当的分辨率图像来具有线性强度模式,这是基于“来自运动形状”理论的梯度方法的最重要假设。通过使用人造图像的数值评估来检查我们的提案的表现。

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