The projection of a real world scenery to a planar image sensor inherits the loss of information about the 3D structure as well as the absolute dimensions of the scene. For image analysis and object classification tasks, however, absolute size information can make results more accurate. Today, the creation of size annotated image datasets is effort intensive and typically requires measurement equipment not available to public image contributors. In this paper, we propose an effective annotation method that utilizes the camera within smart mobile devices to capture the missing size information along with the image. The approach builds on the fact that with a camera, calibrated to a specific object distance, lengths can be measured in the object’s plane. We use the camera’s minimum focus distance as calibration distance and propose an adaptive feature matching process for precise computation of the scale change between two images facilitating measurements on larger object distances. Eventually, the measured object is segmented and its size information is annotated for later analysis. A user study showed that humans are able to retrieve the calibration distance with a low variance. The proposed approach facilitates a measurement accuracy comparable to manual measurement with a ruler and outperforms state-of-the-art methods in terms of accuracy and repeatability. Consequently, the proposed method allows in-situ size annotation of objects in images without the need for additional equipment or an artificial reference object in the scene.

中文翻译：

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使用移动设备有效地注释具有绝对尺寸信息的对象图像

将真实世界的风景投影到平面图像传感器会继承有关 3D 结构以及场景绝对尺寸的信息丢失。然而，对于图像分析和对象分类任务，绝对尺寸信息可以使结果更加准确。今天，创建尺寸标注的图像数据集需要大量的工作，并且通常需要公共图像贡献者无法使用的测量设备。在本文中，我们提出了一种有效的注释方法，该方法利用智能移动设备中的摄像头来捕获丢失的尺寸信息以及图像。该方法基于这样一个事实，即使用校准到特定物体距离的相机，可以在物体平面上测量长度。我们使用相机的最小焦距作为校准距离，并提出了一种自适应特征匹配过程，用于精确计算两个图像之间的比例变化，从而促进对较大物体距离的测量。最终，对被测物体进行分割，并标注其尺寸信息以供后续分析。一项用户研究表明，人类能够以低方差检索校准距离。所提出的方法有助于实现与使用尺子进行手动测量相当的测量精度，并且在精度和可重复性方面优于最先进的方法。因此，所提出的方法允许对图像中的对象进行原位尺寸标注，而无需额外的设备或场景中的人工参考对象。