Book ChapterDOI
Robust Interactive Multi-label Segmentation with an Advanced Edge Detector
Sabine Müller,Peter Ochs,Joachim Weickert,Norbert Graf +3 more
- pp 117-128
TLDR
A remarkable feature of the proposed method is the ability to correct some erroneous labels, when computer generated initial labels are considered, which allows it to improve state-of-the-art methods for motion segmentation in videos by 5–10 % with respect to the F-measure (Dice score).Abstract:
Recent advances on convex relaxation methods allow for a flexible formulation of many interactive multi-label segmentation methods. The building blocks are a likelihood specified for each pixel and each label, and a penalty for the boundary length of each segment. While many sophisticated likelihood estimations based on various statistical measures have been investigated, the boundary length is usually measured in a metric induced by simple image gradients. We show that complementing these methods with recent advances of edge detectors yields an immense quality improvement. A remarkable feature of the proposed method is the ability to correct some erroneous labels, when computer generated initial labels are considered. This allows us to improve state-of-the-art methods for motion segmentation in videos by 5–10 % with respect to the F-measure (Dice score).read more
Citations
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Journal ArticleDOI
Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering
TL;DR: This work states this joint problem as a co-clustering problem that is principled and tractable by existing algorithms, and demonstrates the effectiveness of this approach by combining bottom-up motion segmentation by grouping of point trajectories with high-level multiple object tracking by clustering of bounding boxes.
Proceedings ArticleDOI
Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation
TL;DR: In this article, a primal feasible heuristic is proposed for a reasonably efficient inference in instances of higher-order lifted multicut problem instances defined on point trajectory hypergraphs for motion segmentation.
Journal ArticleDOI
Benchmarking Wilms' tumor in multisequence MRI data: why does current clinical practice fail? Which popular segmentation algorithms perform well?
Sabine Müller,Iva Farag,Joachim Weickert,Yvonne Braun,André Lollert,Jonas Dobberstein,Andreas M. Hötker,Norbert Graf +7 more
TL;DR: The first heterogeneous Wilms’ tumor benchmark data set is presented, which contains multisequence MRI data sets before and after chemotherapy, along with ground truth annotation, approximated based on the consensus of five human experts.
Journal ArticleDOI
Interactive segmentation: a scalable superpixel-based method
TL;DR: This paper proposes a fast and efficient new interactive segmentation method called superpixel α fusion (SαF), which uses superpixel oversegmentation and support vector machine classification to get a fast calculation and an accurate segmentation.
Book ChapterDOI
Self-supervised Sparse to Dense Motion Segmentation
TL;DR: This model does not require pre-training and operates at test time on single frames, and can be trained in a sequence specific way to produce high quality dense segmentations from sparse and noisy input.
References
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A Computational Approach to Edge Detection
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
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Scale-space and edge detection using anisotropic diffusion
Pietro Perona,Jitendra Malik +1 more
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Active contours without edges
Tony F. Chan,Luminita A. Vese +1 more
TL;DR: A new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets is proposed, which can detect objects whose boundaries are not necessarily defined by the gradient.
Journal ArticleDOI
Efficient Graph-Based Image Segmentation
TL;DR: An efficient segmentation algorithm is developed based on a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image and it is shown that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties.