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标题: Around the World in 80 Seconds [打印本页]

作者: 彬彬 时间: 2011-12-29 08:56
标题: Around the World in 80 Seconds
1 Introduction and Related Work

In the famous book “around the world in 80 days” published by

Jules Verne in 1873, two characters aim to perform a world tour

in 80 days. Inspired by Verne’s adventure, we develop a system

to virtually circumnavigate the world in 80 seconds, by collecting

and re-arranging a large collection of Internet images, in a fully

automatic manner, as shown in Figure 1.

Existing methods cannot handle our application. For example,

R. Pergeaux and A. Profit1 need a considerable amount of time

and efforts to manually select and align the pictures. S***cture-

from-motion algorithms (e.g. [Snavely et al. 2006]) can deal with

thousands of images but would require an intractable amount of

processing time and memory for world-scale scenes. In contrast

to scene summarization (e.g. [Simon et al. 2007]), the scene and

the physical locations of the images are changing during the tour.

[Sivic et al. 2008] developed a system to navigate in a set of im-

ages, but it does not take into account geographic data (e.g. GPS

position), temporal data (e.g. acquisition date) or higher-level in-

formation (e.g. objects/buildings present in the pictures or repre-

sentative image selection).

2 Our approach

We automatically collect millions of pictures from Flickr and

grouped the images from the same city into one cluster. A special

sub-cluster “street”, detected by [Oliva and Torralba 2001], is in-

cluded within each city as a connector between landmarks or cities.

Our goal is to build an image sequence with visually smooth transi-

tion. This aim can be transformed into a graph problem which finds

the shortest path from a node in the first cluster to a node in the last

cluster. We define the similarity between two images, I and I

′ by:

d(I; I

′

) = fs(I; I

′

) + cfc(I; I

′

) + vfv(I; I

′

) (1)

where fs measures the s***cture similarity from GIST features

[Oliva and Torralba 2001], fc is the 2 distance between the global

color histograms (i.e. color/tone similarity), fv is the spatial dis-

tance between the vanishing points [Kong et al. 2009], c and v

are the relative weights which are set to 10 and 100 respectively.

Given the shortest path as the skeleton path, the user might want to

adjust the number of images in the sequence while maintaining the

1http://www.youtube.com/watch?v=2N8NaUHR5XI

smooth transition in the image sequence. This can be achieved by

dynamically adding/dropping nodes from the skeleton path based

on the edge weights in real-time.

Our system can also, if desired, automatically find the representa-

tive images within each (sub)cluster. This selection is performed by

[Simon et al. 2007] and will be presented in the Paris tour.

3 Experiments

Figure 1 presents a subset of our result image sequence for

“Jules Verne’s world tour” (world scale). The whole sequence

contains 80 images and constitutes a 80-second world tour

movie. Additional results and video sequences are presented on

http://graphics.ethz.ch/&tilde;jebazin/WT80sec/, es-

pecially a trans-US journey from New-York state to California

(country scale), a tour through the 10 most popular landmarks of

Paris (city scale) and a tour in Ueno Park in Japan along the four

seasons (temporal tour). The whole image sequences were obtained

within a minute, given the similarity graph.

4 Conclusion

In this paper, we have presented an original system that generates,

in a fully automatic manner, a visually smooth image sequence

from Internet images to visualize a traveling tour. Our system al-

lows real-time dynamic adjustment of the number of images pre-

sented in the sequence, interactive path definition by the user and

automatically selects the representative images of landmarks. We

demonstrated our system with tours of various graphical configura-

tions and scales and also extended it to the temporal domain.

References

KONG, H., AUDIBERT, J.-Y., AND PONCE, J. 2009. Vanishing

point detection for road detection. In CVPR’09.

OLIVA, A., AND TORRALBA, A. 2001. Modeling the shape of the

scene: a holistic representation of the spatial envelope. IJCV’01.

SIMON, I., SNAVELY, N., AND SEITZ, S. M. 2007. Scene sum-

marization for online image collections. In ICCV’07.

SIVIC, J., KANEVA, B., TORRALBA, A., AVIDAN, S., AND

FREEMAN, W. T. 2008. Creating and exploring a large photo-

realistic virtual space. In Workshop on Internet Vision (WIV’08).

SNAVELY, N., SEITZ, S. M., AND SZELISKI, R. 2006. Photo

tourism: Exploring photo collections in 3D. In SIGGRAPH’06.

作者: 菜刀吻电线 时间: 2012-4-3 23:26
爱咋咋地！

作者: 奇 时间: 2012-4-28 23:21
路过、路过、快到鸟，列位请继续...ing

作者: 奇 时间: 2012-7-29 23:27
先垫一块，再说鸟

作者: 铁锹 时间: 2012-7-30 09:03
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Unity引擎滑稽3D跑酷玩儿命跑

非同一般的新GPS系统增强现实

作者: C.R.CAN 时间: 2012-10-24 23:24
我是老实人,我来也!

作者: C.R.CAN 时间: 2013-2-11 23:18
路过、路过、快到鸟，列位请继续...ing

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