Abstract
The past 20 years has seen a progressive evolution of computer vision algorithms for unsupervised 2D image segmentation. While earlier efforts relied on Markov random fields and efficient optimization (graph cuts, etc.), the next wave of methods beginning in the early part of this century were, in the main, stovepiped. Of these 2D segmentation efforts, one of the most popular and, indeed, one that comes close to being a state of the art method is the ultrametric contour map (UCM). The pipelined methodology consists of (i) computing local, oriented responses, (ii) graph creation, (iii) eigenvector computation (globalization), (iv) integration of local and global information, (v) contour extraction, and (vi) superpixel hierarchy construction. UCM performs well on a range of 2D tasks. Consequently, it is somewhat surprising that no 3D version of UCM exists at the present time. To address that lack, we present a novel 3D supervoxel segmentation method, dubbed 3D UCM, which closely follows its 2D counterpart while adding 3D relevant features. The methodology, driven by supervoxel extraction, combines local and global gradient-based features together to first produce a low-level supervoxel graph. Subsequently, an agglomerative approach is used to group supervoxel structures into a segmentation hierarchy with explicitly imposed containment of lower-level supervoxels in higher-level supervoxels. Comparisons are conducted against state of the art 3D segmentation algorithms. The considered applications are 3D spatial and 2D spatiotemporal segmentation scenarios. For the latter comparisons, we present results of 3D UCM with and without optical flow video pre-processing. As expected, when motion correction beyond a certain range is required, we demonstrate that 3D UCM in conjunction with optical flow is a very useful addition to the pantheon of video segmentation methods.
Highlights
The ready availability of 3D datasets and video has opened up the need for more 3D computational tools
We introduced transfer learning to 3D ultrametric contour map (UCM) without the intermediate supervised convolutional neural networks (CNNs) layers as in Convolutional oriented boundaries (COB)
In 2D, the normalized cuts approach begins with sparse graph construction obtained by connecting pixels that are spatially close to each other. Globalized probability map UCM (gPb-UCM) [1] specifies a sparse symmetric affinity matrix W using the intervening contour cue [5] which is the maximal value of mPb along a line connecting the two pixels i, j at the ends of relation Wij
Summary
The ready availability of 3D datasets and video has opened up the need for more 3D computational tools. The top eigenvectors of the graph are extracted, placed in image coordinates followed by gradient computation This results in the sPb(x, y, θ) detector which carries global information as it is derived from eigenvector “images.” the globalized probability detector gPb(x, y, θ) is computed via a weighted linear combination of mPb and sPb. This results in the sPb(x, y, θ) detector which carries global information as it is derived from eigenvector “images.” the globalized probability detector gPb(x, y, θ) is computed via a weighted linear combination of mPb and sPb While this completes the pipeline in terms of information accrued for segmentation, UCM proceeds to obtain a set of closed regions using gPb as the input via the application of the oriented watershed transform (OWT). We perform graph-based agglomeration using all voxels following recent work With these changes to the pipeline, the 3D UCM framework is broadly subdivided into (i) local, volume gradient detection, (ii) globalization using reduced order eigensolvers, and (iii) graph-based agglomeration to reflect the emphasis on the changed subsystems. The upside is that 3D UCM becomes scalable to handle sizable datasets
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