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首页> 外文会议>Intelligent robots and computer vision XXXI: algorithms and techniques >Classification and segmentation of orbital space based objects against terrestrial distractors for the purpose of finding holes in Shape from Motion 3D reconstruction
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Classification and segmentation of orbital space based objects against terrestrial distractors for the purpose of finding holes in Shape from Motion 3D reconstruction

机译：针对地面干扰物对基于轨道空间的对象进行分类和分割，目的是从Motion 3D重建中找到Shape中的孔

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摘要

3D reconstruction of objects via Shape from Motion (SFM) has made great strides recently. Utilizing images from a variety of poses, objects can be reconstructed in 3D without knowing a priori the camera pose. These feature points can then be bundled together to create large scale scene reconstructions automatically. A shortcoming of current methods of SFM reconstruction is in dealing with specular or flat low feature surfaces. The inability of SFM to handle these places creates holes in a 3D reconstruction. This can cause problems when the 3D reconstruction is used for proximity detection and collision avoidance by a space vehicle working around another space vehicle. As such, we would like the automatic ability to recognize when a hole in a 3D reconstruction is in fact not a hole, but is a place where reconstruction has failed. Once we know about such a location, methods can be used to try to either more vigorously fill in that region or to instruct a space vehicle to proceed with more caution around that area. Detecting such areas in earth orbiting objects is non-trivial since we need to parse out complex vehicle features from complex earth features, particularly when the observing vehicle is overhead the target vehicle. To do this, we have created a Space Object Classifier and Segmenter (SOCS) hole finder. The general principle we use is to classify image features into three categories (earth, man-made, space). Classified regions are then clustered into probabilistic regions which can then be segmented out. Our categorization method uses an augmentation of a state of the art bag of visual words method for object categorization. This method works by first extracting PHOW (dense SIFT like) features which are computed over an image and then quantized via KD Tree. The quantization results are then binned into histograms and results classified by the PEGASOS support vector machine solver. This gives a probability that a patch in the image corresponds to one of three categories: Earth, Man-Made or Space. Here man-made refers to artificial objects in space. To categorize a whole image, a common sliding window protocol is used. Here we utilized 90 high resolution images from space shuttle servicing missions of the international space station. We extracted 9000 128×128 patches from the images, then we hand sorted them into one of three categories. We then trained our categorizer on a subset of 6000 patches. Testing on 3000 testing patches yielded 96.8% accuracy. This is basically good enough because detection returns a probabilistic score (e.g. p of man-made). Detections can then be spatially pooled to smooth out statistical blips. Spatial pooling can be done by creating a three channel (dimension) image where each channel is the probability of each of the three classes at that location in the image. The probability image can then be segmented or co-segmented with the visible image using a classical segmentation method such as Mean Shift. This yields contiguous regions of classified image. Holes can be detected when SFM does not fill in a region segmented as man-made. Results are shown of the SOCS implementation finding and segmenting man-made objects in pictures containing space vehicles very different from the training set such as Skylab, the Hubble space telescope or the Death Star.
机译：通过运动形状（SFM）进行对象的3D重建近来取得了长足进步。利用来自各种姿势的图像，无需先验相机姿势即可在3D模式下重建对象。然后可以将这些特征点捆绑在一起以自动创建大规模场景重建。当前的SFM重建方法的缺点在于处理镜面或平坦的低特征表面。 SFM无法处理这些位置会在3D重建中造成漏洞。当3D重建被用于在另一空间飞行器周围工作的空间飞行器用于接近检测和避免碰撞时，这可能引起问题。因此，我们希望能够自动识别3D重建中的孔何时实际上不是孔，而是重建失败的地方。一旦我们知道了这样的位置，就可以使用方法来尝试更加有力地填充该区域，或者指示航天器在该区域周围更加谨慎。检测地球轨道物体中的此类区域并非易事，因为我们需要从复杂的地球特征中解析出复杂的车辆特征，尤其是当观察车辆在目标车辆上方时。为此，我们创建了一个空间对象分类器和分段器（SOCS）孔查找器。我们使用的一般原则是将图像特征分为三类（地球，人造，空间）。然后将分类区域聚类为概率区域，然后可以将其划分出来。我们的分类方法使用最先进的视觉单词袋方法进行对象分类。此方法的工作原理是首先提取在图像上计算出的PHOW（类似于SIFT的密集特征）特征，然后通过KD树对其进行量化。然后将量化结果合并到直方图中，并通过PEGASOS支持向量机求解器对结果进行分类。这提供了图像中的色块对应于以下三种类别之一的可能性：地球，人造或太空。在这里，人造是指太空中的人造物体。为了对整个图像进行分类，使用了通用的滑动窗口协议。在这里，我们利用了国际空间站航天飞机维修任务的90张高分辨率图像。我们从图像中提取了9000 128×128色块，然后将它们手工分类为三类之一。然后，我们在6000个补丁的子集中训练了分类器。在3000个测试补丁上进行测试的准确性为96.8％。这基本上足够好，因为检测会返回概率得分（例如，p为人造值）。然后可以在空间上合并检测，以消除统计错误。可以通过创建一个三通道（三维）图像来完成空间池化，其中每个通道都是图像中该位置上三个类别中每个类别的概率。然后，可以使用经典的分割方法（例如均值平移）将概率图像与可见图像进行分割或共同分割。这产生了分类图像的连续区域。当SFM未填充人造区域时，可以检测到孔。结果显示了SOCS实现的发现结果，并在包含与训练集（例如Skylab，哈勃太空望远镜或死亡之星）截然不同的太空飞行器的图片中分割了人造对象。

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