Detecting changes in 3-D shape using self-consistency

Leclerc, Yvan G.; Luong, Quang-Tuan; Fua, Pascal; Miyajima, Koji

doi:10.1109/cvpr.2000.855846

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…In the medical field, an example application is to track the change in the number and density of moles on a patient’s skin (for cancer prediction), which can be posed as a problem of point cloud correspondence [MHL09]. In remote sensing, one example is to track the change over time in the layout of cities and their land usage [LLFM00].…”

Section: Applications Of Correspondencementioning

confidence: 99%

A Survey on Shape Correspondence

Kaick¹,

Zhang²,

Hamarneh³

et al. 2011

Computer Graphics Forum

550

375

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We present a review of the correspondence problem and its solution methods, targeting the computer graphics audience. With this goal in mind, we focus on the correspondence of geometric shapes represented by point sets, contours or triangle meshes. This survey is motivated by recent developments in the field such as those requiring the correspondence of non-rigid or time-varying surfaces and a recent trend towards semantic shape analysis, of which shape correspondence is one of the central tasks. Establishing a meaningful shape correspondence is a difficult problem since it typically relies on an understanding of the structure of the shapes in question at both a local and global level, and sometimes also the shapes' functionality. However, despite its inherent complexity, shape correspondence is a recurrent problem and an essential component in numerous geometry processing applications. In this report, we discuss the different forms of the correspondence problem and review the main solution methods, aided by several classification criteria which can be used by the reader to objectively compare the methods. We finalize the report by discussing open problems and future perspectives.

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Section: Applications Of Correspondencementioning

confidence: 99%

A Survey on Shape Correspondence

Kaick¹,

Zhang²,

Hamarneh³

et al. 2011

Computer Graphics Forum

550

375

View full text Add to dashboard Cite

show abstract

“…However, with the method of Huertas and Nevatia, since the 3D models of the structures have to be individually generated manually, it is troublesome to create 3D models of all structures within the aerial images. Also, Leclerc and colleagues [8,9] detected structural changes by checking for significant differences in 3D models of structures, which were obtained from previous and current aerial images by using stereo analysis. With Leclerc's method, since a digital surface model (DSM) is generated and a 3D model of this is created, 3D models of individual structures need not be created.…”

Section: Introductionmentioning

confidence: 99%

Detecting changes of buildings using stereo matching from aerial images taken at different positions and times

Watanabe¹,

Miyajima

Inoue³

et al. 2004

Electron Comm Jpn Pt III

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SUMMARYGIS base maps are updated by using parallax to compare two aerial images taken at different times to interpret structural changes. Since an aerial image generally photographs a wide range, this interpretation work requires a great deal of time. Therefore, in this paper, the authors propose a method of automatically extracting structural changes with high precision to reduce the load of this interpretation work. Not only do the times differ at which the aerial images used for comparison were taken, but the shooting positions also frequently differ. This causes differences in shadow shapes and differences in structure viewing methods, and when aerial images are compared during automatic interpretation, it will cause the detection precision of structural changes to decrease. To deal with this problem, the authors eliminated the effects of differences in structure viewing methods by applying stereo matching and letting noncorresponding regions be structural change candidates. In addition, to correctly detect structural changes from those candidates, the authors used the outdoor light source model, which had been proposed by previous authors, to eliminate the effects of differences in shadow shapes. The results that were obtained by the proposed method showed that the detection precision of structural changes was approximately four times that of the conventional method.

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“…Figure 12 shows the sensitivity map for a desired detectable change of at least 50m given a left displacement of 20m. The dark regions indicate locations where the condition defined by (8) was not met. Conversely, the light region represents locations where at least a 50m geometric difference can be detected.…”

Section: Motion Vs Sensitivitymentioning

confidence: 99%

“…Unfortunately, these methods can only deal with man-made structures 1,3,11 , simple shapes 6,7,8 or two-dimensional maps 12 . They cannot validate the geometry of complex databases such as those required in synthetic vision systems.…”

Section: Figure 1 Esvs Conceptmentioning

confidence: 99%

“…Watanabe, Miyajima and Mukawa 11 have developed an algorithm to validate the presence and geometry of buildings, using a shadow and shading model. Leclerc et al 6,7,8 proposed a method that uses aerial images for detecting changes in shapes that can be modelled as single-valued functions. Zha, Tanaka and Hasegawa 12 have developed a technique for updating two-dimensional maps used by mobile robots.…”

Section: Figure 1 Esvs Conceptmentioning

confidence: 99%

See 1 more Smart Citation

<title>Online database updating by change detection</title>

Simard

Ferrie

2001

SPIE Proceedings

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Synthetic vision systems render artificial images of the world based on a database and position/attitude information of the aircraft. Due to both its static nature and inherent modelling errors, the database introduces anomalies in the synthetic imagery. Since it reflects at best a nominal state of the environment, it often requires updating via online measurements. The latter can vary from correction of pose and geometry to more complex operations such as marking the locations of detected obstacles. This paper presents an approach for detecting database geometric anomalies online. Since range sensors have a low update rate, they cannot be used for quick validation. Instead of range data, the proposed technique employs an imaging sensor, which can be of any type. It takes advantage of the fact that given a geometric model of the scene and known motion of the observer, the sensor image warping can be exactly predicted. If the geometry of the database is incorrect, the sensor image will not be correctly predicted and geometric differences will thus be detected. The algorithm is tested against simulated imagery and results show that it can correctly identify geometric anomalies. It can cope with known misalignment of the database and pose estimation errors. The technique is shown to be quite robust in low visibility conditions, given that the validated features are at least partially visible. It also automatically detects if a motion is sufficient for a given sensitivity.

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Detecting changes in 3-D shape using self-consistency

Cited by 6 publications

References 15 publications

A Survey on Shape Correspondence

A Survey on Shape Correspondence

Detecting changes of buildings using stereo matching from aerial images taken at different positions and times

<title>Online database updating by change detection</title>

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