Nonlinear revision control for images

Chen, Hsiang-Ting; Wei, Li-Yi; Chang, C.S.

doi:10.1145/1964921.1965000

Cited by 26 publications

(36 citation statements)

References 28 publications

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“…Our approach can be combined with the image revision control system [Chen et al 2011] to merge different editing paths. An example is shown in Fig.…”

Section: Applications and Resultsmentioning

confidence: 99%

“…Grabler et al [2009] propose a system to automatically create stepby-step, visually appealing tutorials of complicated image editing processes. Chen et al [2011] propose a nonlinear revision control method for image editing, using a directed acyclic graph for managing and visualizing the editing process. Chen et al [2012] suggest an adaptive history, which automatically segments and groups a lengthy sequence of editing commands for easy navigation.…”

Section: Related Workmentioning

confidence: 99%

“…Having a clean and complete editing history available is required in many graphics and file management applications, such as image editing revision control [Chen et al 2011], automatic tutorial generation [Grabler et al 2009] and editing visualization [Heer et al 2008]. Retention of an editing history also leads to new possibilities in image editing, such as: adjusting the history to produce results with different variations from the source image; transferring the history to another image, etc.…”

Section: Introductionmentioning

confidence: 99%

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Inverse image editing

et al. 2013

ACM Trans. Graph.

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Applicationsre-edited image Figure 1: Given a source image and an edited copy (left), our system automatically recovers a semantic editing history (middle), which can be used for various applications, such as re-editing (right). In this case, the second editing step of the recovered history, involving hue modification, is altered to change the berries to a difference color. Image courtesy of Andrea Lein. AbstractWe study the problem of inverse image editing, which recovers a semantically-meaningful editing history from a source image and an edited copy. Our approach supports a wide range of commonlyused editing operations such as cropping, object insertion and removal, linear and non-linear color transformations, and spatiallyvarying adjustment brushes. Given an input image pair, we first apply a dense correspondence method between them to match edited image regions with their sources. For each edited region, we determine geometric and semantic appearance operations that have been applied. Finally, we compute an optimal editing path from the region-level editing operations, based on predefined semantic constraints. The recovered history can be used in various applications such as image re-editing, edit transfer, and image revision control. A user study suggests that the editing histories generated from our system are semantically comparable to the ones generated by artists.

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“…Our approach can be combined with the image revision control system [Chen et al 2011] to merge different editing paths. An example is shown in Fig.…”

Section: Applications and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inverse image editing

et al. 2013

ACM Trans. Graph.

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“…Besides the use in spot-the-difference games, our proposed metric potentially has many other applications, e.g., it can be used as a metric that measures significance of changes in an image revision control system [61]; it can also be used as a better way to find duplicated images for an image search engine. We believe the proposed model can also be applied in other areas of computer graphics, such as rendering acceleration, image retargeting, image tone mapping, etc.…”

Section: Discussionmentioning

confidence: 99%

Change Blindness Images

Wong

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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Change blindness refers to human inability to recognize large visual changes between images. In this paper, we present the first computational model of change blindness to quantify the degree of blindness between an image pair. It comprises a novel context-dependent saliency model and a measure of change, the former dependent on the site of the change, and the latter describing the amount of change. This saliency model in particular addresses the influence of background complexity, which plays an important role in the phenomenon of change blindness. Using the proposed computational model, we are able to synthesize changed images with desired degrees of blindness. User studies and comparisons to state-of-the-art saliency models demonstrate the effectiveness of our model.

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“…For example, histories for binary image files can be modeled using a directed acyclic graph to store temporal and semantic relationships (Chen et al, 2011). For video histories, Barnes et al (2010) proposed continuous zooming to support navigation in time.…”

Section: Interaction Logs and Graphical Historiesmentioning

confidence: 99%