Abstract
A good photo is determined using various visual elements of photography and these elements have been implemented in mobile devices with functionalities including zooming, auto-focusing and auto-white-balancing. Although composition is an important element of a good photo and an interesting research topic, most composition-related functionalities have not been added to mobile devices. We propose a guide system for capturing good photos in mobile devices that considers composition elements. A photo composition mixture model (PCMM) is derived based on composition elements such as a Gaussian Mixture Model (GMM), and the best composition of current input is gradually determined by iterating the PCMM optimization. Experimental evaluations are conducted to show the usefulness of the proposed PCMM and its optimization performance. To show the efficiency of recomposition performance and speed, we compare our method with retargeting-based methods. By implementing our method in mobile devices, we show that our system offers valid user guidance for capturing a photo with good composition in realtime.
| Original language | English |
|---|---|
| Article number | 017001 |
| Journal | Optical Engineering |
| Volume | 51 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2012 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics
- Engineering(all)
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Interactive optimization of photo composition with Gaussian mixture model on mobile platform. / Sung, Hachon; Bae, Guntae; Cho, Sunyoung; Byun, Hyeran.
In: Optical Engineering, Vol. 51, No. 1, 017001, 01.01.2012.Research output: Contribution to journal › Article
TY - JOUR
T1 - Interactive optimization of photo composition with Gaussian mixture model on mobile platform
AU - Sung, Hachon
AU - Bae, Guntae
AU - Cho, Sunyoung
AU - Byun, Hyeran
PY - 2012/1/1
Y1 - 2012/1/1
N2 - A good photo is determined using various visual elements of photography and these elements have been implemented in mobile devices with functionalities including zooming, auto-focusing and auto-white-balancing. Although composition is an important element of a good photo and an interesting research topic, most composition-related functionalities have not been added to mobile devices. We propose a guide system for capturing good photos in mobile devices that considers composition elements. A photo composition mixture model (PCMM) is derived based on composition elements such as a Gaussian Mixture Model (GMM), and the best composition of current input is gradually determined by iterating the PCMM optimization. Experimental evaluations are conducted to show the usefulness of the proposed PCMM and its optimization performance. To show the efficiency of recomposition performance and speed, we compare our method with retargeting-based methods. By implementing our method in mobile devices, we show that our system offers valid user guidance for capturing a photo with good composition in realtime.
AB - A good photo is determined using various visual elements of photography and these elements have been implemented in mobile devices with functionalities including zooming, auto-focusing and auto-white-balancing. Although composition is an important element of a good photo and an interesting research topic, most composition-related functionalities have not been added to mobile devices. We propose a guide system for capturing good photos in mobile devices that considers composition elements. A photo composition mixture model (PCMM) is derived based on composition elements such as a Gaussian Mixture Model (GMM), and the best composition of current input is gradually determined by iterating the PCMM optimization. Experimental evaluations are conducted to show the usefulness of the proposed PCMM and its optimization performance. To show the efficiency of recomposition performance and speed, we compare our method with retargeting-based methods. By implementing our method in mobile devices, we show that our system offers valid user guidance for capturing a photo with good composition in realtime.
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UR - http://www.scopus.com/inward/citedby.url?scp=84891325947&partnerID=8YFLogxK
U2 - 10.1117/1.OE.51.1.017001
DO - 10.1117/1.OE.51.1.017001
M3 - Article
AN - SCOPUS:84891325947
VL - 51
JO - Optical Engineering
JF - Optical Engineering
SN - 0091-3286
IS - 1
M1 - 017001
ER -