92,293 research outputs found
Optimization-based interactive segmentation interface for multiregion problems.
Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation. However, general-purpose tools have been lacking in terms of segmenting multiple regions simultaneously with a high degree of coupling between groups of labels. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently, these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. In a generalized form, the hierarchy for any given segmentation problem is specified in run-time, allowing different hierarchies to be quickly explored. We present an interactive segmentation interface, which uses generalized hierarchical max-flow for optimization-based multiregion segmentation guided by user-defined seeds. Applications in cardiac and neonatal brain segmentation are given as example applications of its generality
Optimization-based interactive segmentation interface for multiregion problems.
Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation. However, general-purpose tools have been lacking in terms of segmenting multiple regions simultaneously with a high degree of coupling between groups of labels. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently, these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. In a generalized form, the hierarchy for any given segmentation problem is specified in run-time, allowing different hierarchies to be quickly explored. We present an interactive segmentation interface, which uses generalized hierarchical max-flow for optimization-based multiregion segmentation guided by user-defined seeds. Applications in cardiac and neonatal brain segmentation are given as example applications of its generality
Example-Guided Style-Consistent Image Synthesis from Semantic Labeling
Example-guided image synthesis aims to synthesize an image from a semantic label map and an exemplary image indicating style. We use the term “style” in this problem to refer to implicit characteristics of images, for example: in portraits “style” includes gender, racial identity, age, hairstyle; in full body pictures it includes clothing; in street scenes it refers to weather and time of day and such like. A semantic label map in these cases indicates facial expres- sion, full body pose, or scene segmentation. We propose a solution to the example-guided image synthesis problem us- ing conditional generative adversarial networks with style consistency. Our key contributions are (i) a novel style consistency discriminator to determine whether a pair of im- ages are consistent in style; (ii) an adaptive semantic con- sistency loss; and (iii) a training data sampling strategy, for synthesizing style-consistent results to the exemplar. We demonstrate the efficiency of our method on face, dance and street view synthesis tasks
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
Exploiting synthetic data to learn deep models has attracted increasing
attention in recent years. However, the intrinsic domain difference between
synthetic and real images usually causes a significant performance drop when
applying the learned model to real world scenarios. This is mainly due to two
reasons: 1) the model overfits to synthetic images, making the convolutional
filters incompetent to extract informative representation for real images; 2)
there is a distribution difference between synthetic and real data, which is
also known as the domain adaptation problem. To this end, we propose a new
reality oriented adaptation approach for urban scene semantic segmentation by
learning from synthetic data. First, we propose a target guided distillation
approach to learn the real image style, which is achieved by training the
segmentation model to imitate a pretrained real style model using real images.
Second, we further take advantage of the intrinsic spatial structure presented
in urban scene images, and propose a spatial-aware adaptation scheme to
effectively align the distribution of two domains. These two modules can be
readily integrated with existing state-of-the-art semantic segmentation
networks to improve their generalizability when adapting from synthetic to real
urban scenes. We evaluate the proposed method on Cityscapes dataset by adapting
from GTAV and SYNTHIA datasets, where the results demonstrate the effectiveness
of our method.Comment: Add experiments on SYNTHIA, CVPR 2018 camera-ready versio
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