We present a new method for decomposing an image into a set of soft color segments that are analogous to color layers with alpha channels that have been commonly utilized in modern image manipulation software. We show that the resulting decomposition serves as an effective intermediate image representation, which can be utilized for performing various, seemingly unrelated, image manipulation tasks. We identify a set of requirements that soft color segmentation methods have to fulfill, and present an in-depth theoretical analysis of prior work. We propose an energy formulation for producing compact layers of homogeneous colors and a color refinement procedure, as well as a method for automatically estimating a statistical color model from an image. This results in a novel framework for automatic and high-quality soft color segmentation that is efficient, parallelizable, and scalable. We show that our technique is superior in quality compared to previous methods through quantitative analysis as well as visually through an extensive set of examples. We demonstrate that our soft color segments can easily be exported to familiar image manipulation software packages and used to produce compelling results for numerous image manipulation applications without forcing the user to learn new tools and workflows.
Accurate representation of soft transitions between image regions is essential for high-quality image editing and compositing. Current techniques for generating such representations depend heavily on interaction by a skilled visual artist, as creating such accurate object selections is a tedious task. In this work, we introduce
semantic soft segments
, a set of layers that correspond to semantically meaningful regions in an image with accurate soft transitions between different objects. We approach this problem from a spectral segmentation angle and propose a graph structure that embeds texture and color features from the image as well as higher-level semantic information generated by a neural network. The soft segments are generated via eigendecomposition of the carefully constructed Laplacian matrix fully automatically. We demonstrate that otherwise complex image editing tasks can be done with little effort using semantic soft segments.
Due to the widespread use of compositing in contemporary feature films, green-screen keying has become an essential part of postproduction workflows. To comply with the ever-increasing quality requirements of the industry, specialized compositing artists spend countless hours using multiple commercial software tools, while eventually having to resort to manual painting because of the many shortcomings of these tools. Due to the sheer amount of manual labor involved in the process, new green-screen keying approaches that produce better keying results with less user interaction are welcome additions to the compositing artist’s arsenal. We found that—contrary to the common belief in the research community—production-quality green-screen keying is still an unresolved problem with its unique challenges. In this article, we propose a novel green-screen keying method utilizing a new energy minimization-based
color unmixing
algorithm. We present comprehensive comparisons with commercial software packages and relevant methods in literature, which show that the quality of our results is superior to any other currently available green-screen keying solution. It is important to note that, using the proposed method, these high-quality results can be generated using only one-tenth of the manual editing time that a professional compositing artist requires to process the same content having all previous state-of-the-art tools at one’s disposal.
We evaluated the impact of transrectal prostate needle biopsy (TPNB) on erectile function and on the prostate and bilateral neurovascular bundles using power Doppler ultrasonography imaging of the prostate. The study consisted of 42 patients who had undergone TPNB. Erectile function was evaluated prior to the biopsy, and in the 3rd month after the biopsy using the first five-item version of the International Index of Erectile Function (IIEF-5). Prior to and 3 months after the biopsy, the resistivity index of the prostate parenchyma and both neurovascular bundles was measured. The mean age of the men was 64.2 (47-78) years. Prior to TPNB, 10 (23.8%) patients did not have erectile dysfunction (ED) and 32 (76.2%) patients had ED. The mean IIEF-5 score was 20.8 (range: 2-25) prior to the biopsies, and the mean IIEF-5 score was 17.4 (range: 5-25; p < 0.001) after 3 months. For patients who were previously potent in the pre-biopsy period, the ED rate was 40% (n = 4/10) at the 3rd month evaluation. In these patients, all the resistivity index values were significantly decreased. Our results showed that TPNB may lead to an increased risk of ED. The presence of ED in men after TPNB might have an organic basis.
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