A mathematical analysis of the motion coherence theory

Yuille, Alan; Grzywacz, Norberto M.

doi:10.1007/bf00126430

“…According to the consistency theory, the relaxation labeling process increases the overall consistency by reducing local ambiguities [11], [20]. During the experiments, we verified that the labeling update process converge to an unambiguous solution starting from an arbitrary initialization.…”

Section: Convergence Of Relaxation Labelingmentioning

confidence: 60%

Image Registration Using Advanced Topology Preserving Relaxation Labeling

Kim¹,

Park²,

Su

³

et al. 2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

0

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“…Velocity coherence is a particular way of imposing smoothness on the underlying transformation. The concept of motion coherence is proposed in the motion coherence theory [38] which is intuitively interpreted as that points close to one another tend to move coherently. Examples of velocity fields with two different levels of motion coherence for two different point correspondences are illustrated in Figure 4.…”

Section: Motion Coherent Based Geometric Constraintmentioning

confidence: 99%

Remote Sensing Image Registration Using Multiple Image Features

Yang¹,

Pan²,

Yang³

et al. 2017

Preprint

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Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

show abstract

“…Velocity coherence is a particular way of imposing smoothness on the underlying transformation. The concept of motion coherence is proposed in the motion coherence theory [35] which is intuitively interpreted as that points close to one another tend to move coherently. Examples of velocity fields with two different levels of motion coherence for two different point correspondences are illustrated in Figure 4.…”

Section: Motion Coherent Based Geometric Constraintmentioning

confidence: 99%

Remote Sensing Image Registration Using Multiple Image Features

Yang

¹

,

Pan

²

,

Yang

³

et al. 2017

View full text Add to dashboard Cite

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

show abstract

A mathematical analysis of the motion coherence theory

Cited by 172 publications

References 40 publications

Image Registration Using Advanced Topology Preserving Relaxation Labeling

Image Registration Using Advanced Topology Preserving Relaxation Labeling

Remote Sensing Image Registration Using Multiple Image Features

Remote Sensing Image Registration Using Multiple Image Features

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