Color Harmonization for Videos

Sawant, Neela; Mitra, Niloy J.

doi:10.1109/icvgip.2008.17

Cited by 19 publications

(16 citation statements)

References 5 publications

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“…From Figs. 10(c) and 10(d), we can see that using the harmonic schemes generated by the previous matching function, 1,2 the foreground colors are not adjusted. However, using our algorithm, they are changed to warm colors, as shown in Fig.…”

Section: Comparisons Of the Three Matching Functionsmentioning

confidence: 91%

“…2 Figure 9 shows some comparison results of the three algorithms. From the comparisons, we can see that the harmonic schemes that are obtained by our matching function match the hue distributions of the images better than the previous matching functions.…”

Section: Comparisons Of the Three Matching Functionsmentioning

confidence: 98%

“…Like the previous two works, 1,2 we also use the notion of the color harmony developed by Matsuda. 3 The eight harmonic templates defined in Matsuda 3 are shown in Fig.…”

Section: Color Harmonization For Images 31 Harmonic Schemesmentioning

confidence: 98%

“…Figure 12 shows the comparison of our approach with Sawant and Mitra. 2 It can be seen that, the histogramsplitting-based algorithm 2 greatly relies on the hue distribution of the image. This algorithm works well for images with sparse hue distribution, such as that shown in Fig.…”

Section: Visual Comparisons Of the Threementioning

confidence: 99%

“…From the comparisons, we can see that the harmonic schemes that are obtained by our matching function match the hue distributions of the images better than the previous matching functions. 1,2 Another problem is that using the previous matching functions, 1,2 only the T-and the X-type templates can be selected. The other five types of templates will never be selected because they employ absolute distance to measure the similarity of one template and the hue distribution.…”

Section: Comparisons Of the Three Matching Functionsmentioning

confidence: 99%

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Color harmonization for images

Tang

Miao

Wan

et al. 2011

J. Electron. Imaging

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Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Abstract. Color harmonization is an artistic technique to adjust a set of colors in order to enhance their visual harmony so that they are aesthetically pleasing in terms of human visual perception. We present a new color harmonization method that treats the harmonization as a function optimization. For a given image, we derive a cost function based on the observation that pixels in a small window that have similar unharmonic hues should be harmonized with similar harmonic hues. By minimizing the cost function, we get a harmonized image in which the spatial coherence is preserved. A new matching function is proposed to select the best matching harmonic schemes, and a new component-based preharmonization strategy is proposed to preserve the hue distribution of the harmonized images. Our approach overcomes several shortcomings of the existing color harmonization methods. We test our algorithm with a variety of images to demonstrate the effectiveness of our approach. © 2011 SPIE and IS&T.

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Section: Comparisons Of the Three Matching Functionsmentioning

confidence: 91%

Section: Comparisons Of the Three Matching Functionsmentioning

confidence: 98%

“…Like the previous two works, 1,2 we also use the notion of the color harmony developed by Matsuda. 3 The eight harmonic templates defined in Matsuda 3 are shown in Fig.…”

Section: Color Harmonization For Images 31 Harmonic Schemesmentioning

confidence: 98%

Section: Visual Comparisons Of the Threementioning

confidence: 99%

Section: Comparisons Of the Three Matching Functionsmentioning

confidence: 99%

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Color harmonization for images

Tang

Miao

Wan

et al. 2011

J. Electron. Imaging

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Saliency-Guided Consistent Color Harmonization

Baveye

Urban

Chamaret

et al. 2013

Lecture Notes in Computer Science

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The focus of this paper is automatic color harmonization, which amounts to re-coloring an image so that the obtained color palette is more harmonious for human observers. The proposed automatic algorithm builds on the pioneering works described in [3,12] where templates of harmonious colors are defined on the hue wheel. We bring three contributions in this paper: first, saliency [9] is used to predict the most attractive visual areas and estimate a consistent harmonious template. Second, an efficient color segmentation algorithm, adapted from [4], is proposed to perform consistent color mapping. Third, a new mapping function substitutes usual color shifting method. Results show that the method limits the visual artifacts of state-of-the-art methods and leads to a visually consistent harmonization.

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Example-based painting guided by color features

Huang

Zang

2010

Vis Comput

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Color Harmonization for Videos

Cited by 19 publications

References 5 publications

Color harmonization for images

Color harmonization for images

Saliency-Guided Consistent Color Harmonization

Example-based painting guided by color features

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