View-Consistent 4D Light Field Superpixel Segmentation
Many 4D light field processing applications rely on superpixel segmentations, for which occlusion-aware view consistency is important. Yet, existing methods often enforce consistency by propagating clusters from a central view only, which can lead to inconsistent superpixels for non-central views. Our proposed approach combines an occlusion-aware angular segmentation in horizontal and vertical EPI spaces with an occlusion-aware clustering and propagation step across all views. Qualitative video demonstrations show that this helps to remove flickering and inconsistent boundary shapes versus the state-of-the-art approach, and quantitative metrics reflect these findings with improved boundary accuracy and view consistency scores.
Code (1)
Tasks
ClusteringSuperpixelsSimilar Papers 제목 키워드 기반
4D Light Field Superpixel and Segmentation
Superpixel segmentation of 2D image has been widely used in many computer vision tasks. However, limited to the Gaussian imaging principle, there is not a thorough segmentation solution to the ambiguity in defocus and oc…
SegmentationDeep Spherical Superpixels
Over the years, the use of superpixel segmentation has become very popular in various applications, serving as a preprocessing step to reduce data size by adapting to the content of the image, regardless of its semantic …
Data AugmentationSegmentationSuperpixelsFast and Accurate Depth Estimation from Sparse Light Fields
We present a fast and accurate method for dense depth reconstruction from sparsely sampled light fields obtained using a synchronized camera array. In our method, the source images are over-segmented into non-overlapping…
Depth EstimationSuperpixelsAdaptive strategy for superpixel-based region-growing image segmentation
This work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iterative…
Image SegmentationSegmentationSemantic SegmentationSuperpixelsRethinking Unsupervised Neural Superpixel Segmentation
Recently, the concept of unsupervised learning for superpixel segmentation via CNNs has been studied. Essentially, such methods generate superpixels by convolutional neural network (CNN) employed on a single image, and s…
SegmentationSuperpixels