Incremental Points-to Analysis for Java via Edit Propagation

2015 ◽  
pp. 164-178
Author(s):  
Yuting Chen ◽  
Qiuwei Shi ◽  
Weikai Miao
Keyword(s):  
Edit Propagation

scholarly journals Efficient affinity-based edit propagation using K-D tree

2009 ◽  
Vol 28 (5) ◽  
pp. 1-6 ◽  
Author(s):  
Kun Xu ◽  
Yong Li ◽  
Tao Ju ◽  
Shi-Min Hu ◽  
Tian-Qiang Liu
Keyword(s):  
Edit Propagation

Learn Sparse Dictionaries for Edit Propagation

2016 ◽  
Vol 25 (4) ◽  
pp. 1688-1698 ◽  
Author(s):  
Xiaowu Chen ◽  
Jianwei Li ◽  
Dongqing Zou ◽  
Qinping Zhao
Keyword(s):  
Edit Propagation

scholarly journals Edit propagation using geometric relationship functions

2014 ◽  
Vol 33 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Paul Guerrero ◽  
Stefan Jeschke ◽  
Michael Wimmer ◽  
Peter Wonka
Keyword(s):  
Edit Propagation ◽  
Geometric Relationship

Manifold preserving edit propagation

2012 ◽  
Vol 31 (6) ◽  
pp. 1-7 ◽  
Author(s):  
Xiaowu Chen ◽  
Dongqing Zou ◽  
Qinping Zhao ◽  
Ping Tan
Keyword(s):  
Edit Propagation

A Novel Edit Propagation Algorithm via $$ L_0 $$ Gradient Minimization

2015 ◽  
pp. 402-410
Author(s):  
Zhenyuan Guo ◽  
Haoqian Wang ◽  
Kai Li ◽  
Yongbing Zhang ◽  
Xingzheng Wang ◽  
...  
Keyword(s):  
Edit Propagation ◽  
Propagation Algorithm

scholarly journals Efficient affinity-based edit propagation using K-D tree

2009 ◽  
Author(s):  
Kun Xu ◽  
Yong Li ◽  
Tao Ju ◽  
Shi-Min Hu ◽  
Tian-Qiang Liu
Keyword(s):  
Edit Propagation

scholarly journals Manifold Learning Techniques for Editing Motion Capture Data

2021 ◽  
Author(s):  
◽  
Christopher Dean
Keyword(s):  
Motion Capture ◽  
Manifold Learning ◽  
Fundamental Problem ◽  
Character Animation ◽  
Data Driven ◽  
Motion Capture Data ◽  
Edit Propagation ◽  
Learning Techniques ◽  
Non Local ◽  
The Difference

<p>Streamlining the process of editing motion capture data and keyframe character animation is a fundamental problem in the animation field. This paper explores a new method for editing character animation, by using a data-driven pose distance as a falloff to interpolate new poses seamlessly into the sequence. This pose distance is the measure given by Green's function of the pose space Laplacian. The falloff shape and timing extent are naturally suited to the skeleton's range of motion, replacing the need for a manually customized falloff spline. This data-driven falloff is somewhat analogous to the difference between a generic spline and the ``magic wand'' selection in an image editor, but applied to the animation domain. It also supports powerful non-local edit propagation in which edits are applied to all similar poses in the entire animation sequence.</p>

scholarly journals User-guided Image Editing using Radial Basis Functions

2021 ◽  
Author(s):  
◽  
Evgeny Patrikeev
Keyword(s):  
Radial Basis Functions ◽  
Basis Functions ◽  
Image Point ◽  
Image Editing ◽  
Image Region ◽  
Image Deformation ◽  
User Input ◽  
Advantages And Disadvantages ◽  
Edit Propagation ◽  
Radial Basis

<p>Good image editing tools that modify colors of specified image regions or deform the depicted objects have always been an important part of graphics editors. Manual approaches to this task are too time-consuming, while fully automatic methods are not robust enough. Thus, the ideal editing method should include a combination of manual and automated components. This thesis shows that radial basis functions provide a suitable “engine” for two common image editing problems, where interactivity requires both reasonable performance and fast training.  There are many freeform image deformation methods to be used, each having advantages and disadvantages. This thesis explores the use of radial basis functions for freeform image deformation and compares it to a standard approach that uses B-spline warping.  Edit propagation is a promising user-guided color editing technique, which, instead of requiring precise selection of the region being edited, accepts color edits as a few brush strokes over an image region and then propagates these edits to the regions with similar appearance. This thesis focuses on an approach to edit propagation, which considers user input as an incomplete set of values of an intended edit function. The approach interpolates between the user input values using radial basis functions to find the edit function for the whole image.  While the existing approach applies the user-specified edits to all the regions with similar colors, this thesis presents an extension that propagates the edits more selectively. In addition to color information of each image point, it also takes the surrounding texture into account and better distinguishes different objects, giving the algorithm more information about the user-specified region and making the edit propagation more precise.</p>

Temporally coherent video editing using an edit propagation matrix

2014 ◽  
Vol 43 ◽  
pp. 1-10 ◽  
Author(s):  
Tatsuya Yatagawa ◽  
Yasushi Yamaguchi
Keyword(s):  
Video Editing ◽  
Edit Propagation ◽  
Propagation Matrix
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