Study of dynamical processes with tensor-based spatiotemporal image processing techniques

Jähne, Bernd; Haußecker, Horst; Scharr, Hanno; Spies, Hagen; Schmundt, Dominik; Schurr, Uli

doi:10.1007/bfb0054750

Cited by 35 publications

(24 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Strong experimental evidence exists, backing up this assumption [Jähne et al, 1989[Jähne et al, , 1998]. Only through this similarity in transport can results gained for the transport of heat be applied to the transport of mass.…”

Section: Introductionmentioning

confidence: 95%

See 1 more Smart Citation

A surface renewal model to analyze infrared image sequences of the ocean surface for the study of air‐sea heat and gas exchange

2004

Self Cite

View full text Add to dashboard Cite

[1] Thermographic techniques are presented that directly measure the temperature difference across the thermal boundary layer at the sea surface, the probability density function of surface renewal, the net heat flux, and the heat transfer velocity during nighttime. The techniques are based on a model of surface renewal. Through the use of digital image processing techniques, temporally and spatially highly resolved measurements are feasible, limited only by the thermal imager. We present laboratory measurements from the Heidelberg Aeolotron and field measurements from the GasExII cruise taken at a spatial resolution of 3 mm and temporal resolution of 10 ms. The net heat flux estimates of the thermographic techniques and micrometeorological methods agree with an error less than 5% for conditions in which the surface renewal model is applicable. Experimental evidence is presented for the probability density function of surface renewal to be best described by a logarithmic normal distribution. At moderate and high wind speeds when the influence of surface films is not significant, surface renewal seems to be an adequate model for air-water heat exchange.

show abstract

Section: Introductionmentioning

confidence: 95%

“…where Sc h and Sc g are the Schmidt numbers of heat and gas respectively and n the Schmidt number exponent [Jähne et al, 1998]. From this equation the gas transfer velocity k g can be deduced from the heat transfer velocity k h since the Schmidt numbers are known.…”

Section: Introductionmentioning

confidence: 99%

A surface renewal model to analyze infrared image sequences of the ocean surface for the study of air‐sea heat and gas exchange

2004

Self Cite

View full text Add to dashboard Cite

show abstract

“…Using the structure tensor we estimate the orientation ( ) r x within a local neighborhood U irrespective of the sign of v. From its components the symmetric tensor can be constructed in the following way [7] :…”

Section: Tensor Representation Of Orientationmentioning

confidence: 99%

Motion Estimation Using a General Purpose Neural Network Simulator for Visual Attention

Vintila

Tsotsos

2007

2007 IEEE Workshop on Applications of Computer Vision (WAC v '07)

View full text Add to dashboard Cite

show abstract

“…The robust motion segmentation method based on frame differencing and level set based curve evolution was the key to success in Zhang et al's technique (see Zhang et al 115 ). The main idea in the frame differencing technique was to calculate the 3D structural tensor in the spatial-temporal domain (see Jahne et al 111 ). For an image sequence f (x, t), where x = {x 1 , x 2 }, the 3D structural tensor (a 3×3 matrix) was computed as the convolution of the product of gradient of f and its transpose by the Gaussian function h. This was mathematically given as:…”

Section: Motion Segmentation Using Frame Difference Via Pde (Zhang/uw )mentioning

confidence: 99%

Modeling Segmentation via Geometric Deformable Regularizers, Pde and Level Sets in Still and Motion Imagery: A Revisit

Suri

Reden

et al. 2001

Int. J. Image Grap.

View full text Add to dashboard Cite

Partial Differential Equations (PDEs) have dominated image processing research recently. The three main reasons for their success are: first, their ability to transform a segmentation modeling problem into a partial differential equation framework and their ability to embed and integrate different regularizers into these models; second, their ability to solve PDEs in the level set framework using finite difference methods; and third, their easy extension to a higher dimensional space. This paper is an attempt to survey and understand the power of PDEs to incorporate into geometric deformable models for segmentation of objects in 2D and 3D in still and motion imagery. The paper first presents PDEs and their solutions applied to image diffusion. The main concentration of this paper is to demonstrate the usage of regularizers in PDEs and level set framework to achieve the image segmentation in still and motion imagery. Lastly, we cover miscellaneous applications such as: mathematical morphology, computation of missing boundaries for shape recovery and low pass filtering, all under the PDE framework. The paper concludes with the merits and the demerits of PDEs and level set-based framework for segmentation modeling. The paper presents a variety of examples covering both synthetic and real world images.

show abstract

Study of dynamical processes with tensor-based spatiotemporal image processing techniques

Cited by 35 publications

References 7 publications

A surface renewal model to analyze infrared image sequences of the ocean surface for the study of air‐sea heat and gas exchange

A surface renewal model to analyze infrared image sequences of the ocean surface for the study of air‐sea heat and gas exchange

Motion Estimation Using a General Purpose Neural Network Simulator for Visual Attention

Modeling Segmentation via Geometric Deformable Regularizers, Pde and Level Sets in Still and Motion Imagery: A Revisit

Contact Info

Product

Resources

About