In this paper, we propose a novel approach to simplify sketch drawings. The core problem is how to group sketchy strokes meaningfully, and this depends on how humans understand the sketches. The existing methods mainly rely on thresholding low-level geometric properties among the strokes, such as proximity, continuity and parallelism. However, it is not uncommon to have strokes with equal geometric properties but different semantics. The lack of semantic analysis will lead to the inability in differentiating the above semantically different scenarios. In this paper, we point out that, due to the gestalt phenomenon of
closure
, the grouping of strokes is actually highly influenced by the interpretation of regions. On the other hand, the interpretation of regions is also influenced by the interpretation of strokes since regions are formed and depicted by strokes. This is actually a chicken-or-the-egg dilemma and we solve it by an iterative cyclic refinement approach. Once the formed stroke groups are stabilized, we can simplify the sketchy strokes by replacing each stroke group with a smooth curve. We evaluate our method on a wide range of different sketch styles and semantically meaningful simplification results can be obtained in all test cases.
Tone mapping is a commonly used technique that maps the set of colors in high‐dynamic‐range (HDR) images to another set of colors in low‐dynamic‐range (LDR) images, to fit the need for print‐outs, LCD monitors and projectors. Unfortunately, during the compression of dynamic range, the overall contrast and local details generally cannot be preserved simultaneously. Recently, with the increased use of stereoscopic devices, the notion of binocular tone mapping has been proposed in the existing research study. However, the existing research lacks the binocular perception study and is unable to generate the optimal binocular pair that presents the most visual content. In this paper, we propose a novel perception‐based binocular tone mapping method, that can generate an optimal binocular image pair (generating left and right images simultaneously) from an HDR image that presents the most visual content by designing a binocular perception metric. Our method outperforms the existing method in terms of both visual and time performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.