Recovering an elevation map by stereo modeling of the aerial image sequence

Kang, Min Seok; Park, Rae-Hong; Lee, Kwae-Hi

doi:10.1117/12.181936

Cited by 10 publications

(14 citation statements)

References 5 publications

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“…We encode the behavior of the DG in a pdf to guide the definition of the energy function of the prior of a MRF for small baseline stereo. To complete the model based on a Bayesian approach, we also derive a likelihood function for the normalized cross-covariance (Kang et al, 1994) between any two matching points. Then, the correspondence problem is solved by finding the MAP solution using simulated annealing (Geman & Geman, 1984;Li et al, 1997) (Sec.…”

Section: R Fs For S Te R E O Ma Tchi Ngmentioning

confidence: 99%

Markov Random Fields in the Context of Stereo Vision

Tardón¹,

Barbancho²,

Alberola³

2011

Advances in Theory and Applications of Stereo Vision

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Section: R Fs For S Te R E O Ma Tchi Ngmentioning

confidence: 99%

Markov Random Fields in the Context of Stereo Vision

Tardón¹,

Barbancho²,

Alberola³

2011

Advances in Theory and Applications of Stereo Vision

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“…A PRA estimates motion on a pixel-by-pixel basis, whereas a BMA estimates motion on a block-by-block basis. Due to their implementation simplicity, block matching algorithms have been widely adopted by various video coding standards such as CCITT H.261 [5], ITU-T H.263 [6], and MPEG [7].…”

Section: Fast Algorithms For the Estimation Of Motion Vectorsmentioning

confidence: 99%

“…If high-resolution images are available, the current position can be obtained more accurately by the image matching algorithm, based on the Hausdorff distance (HD). Current position can also be estimated by matching the stored DEM with the recovered elevation model (REM), which is reconstructed from two successive image frames [6], [7]. This approach requires very high computational complexity, thus an energy function, defined in terms of the current position, DEM, and matching positions, is introduced to alleviate the computational load.…”

Section: Introductionmentioning

confidence: 99%

Hybrid estimation of navigation parameters from aerial image sequence

Sim

Jeong

Lee

et al. 1999

IEEE Trans. on Image Process.

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Abstract-This work presents a hybrid method for navigation parameter estimation using sequential aerial images, where navigation parameters represent the position and velocity information of an aircraft for autonomous navigation. The proposed hybrid system is composed of two parts: relative position estimation and absolute position estimation. Computer simulation with two different sets of real aerial image sequences shows the effectiveness of the proposed hybrid parameter estimation algorithm.

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“…The most difficult problem in stereo vision is the stereo correspondence problem. Once the corresponding points in a stereo image pair are identified, the 3D depth can be easily computed by triangulation [10,15,16].…”

Section: Introductionmentioning

confidence: 99%

“…However, all the iterative approaches described above need a good initial estimate to achieve good convergence result (typically, the initial estimate should be within three or four pixels to the true location to achieve good convergence). Kang, et al [16] proposed an area-based stereo-matching approach for aerial sequence images. At each pixel, block matching is performed.…”

Section: Introductionmentioning

confidence: 99%

Multipass hierarchical stereo matching for generation of digital terrain models from aerial images

Hung

Chen

Hung

et al. 1998

Machine Vision and Applications

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Abstract. This paper presents a new multi-pass hierarchical stereo-matching approach for generation of digital terrain models (DTMs) from two overlapping aerial images. Our method consists of multiple passes which compute stereo matches with a coarse-to-fine and sparse-to-dense paradigm. An image pyramid is generated and used in the hierarchical stereo matching. Within each pass, the DTM is refined by using the image pyramid from the coarse to the fine level. At the coarsest level of the first pass, a global stereo-matching technique, the intra-/inter-scanline matching method, is used to generate a good initial DTM for the subsequent stereo matching. Thereafter, hierarchical block matching is applied to image locations where features are detected to refine the DTM incrementally. In the first pass, only the feature points near salient edge segments are considered in block matching. In the second pass, all the feature points are considered, and the DTM obtained from the first pass is used as the initial condition for local searching. For the passes after the second pass, 3D interactive manual editing can be incorporated into the automatic DTM refinement process whenever necessary. Experimental results have shown that our method can successfully provide accurate DTM from aerial images. The success of our approach and system has also been demonstrated with a flight simulation software.

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Recovering an elevation map by stereo modeling of the aerial image sequence

Cited by 10 publications

References 5 publications

Markov Random Fields in the Context of Stereo Vision

Markov Random Fields in the Context of Stereo Vision

Hybrid estimation of navigation parameters from aerial image sequence

Multipass hierarchical stereo matching for generation of digital terrain models from aerial images

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