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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 128x128 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.
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