Improving Optical Flow Using Residual and Sobel Edge Images

Vaudrey, Tobi; Wedel, Andreas; Chen, Chia-Yen; Klette, Reinhard

doi:10.1007/978-3-642-11577-6_27

Cited by 3 publications

References 16 publications

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“…Once the HDR content is processed by Dong's HDR HVS modeling module [2], different parallel feature channels (namely motion, color, brightness intensity, and texture orientations) are extracted. As suggested in [3], for extracting motion feature map, an optical flow based approach is used (residual flow detection method of [15,16]) to ensure high performance in bright image areas and minimize the effect of brightness flickering between video frames. For each of the color, intensity and orientation features as proposed in [2,5], a Gaussian pyramid is built in different scales and these pyramids are later combined across scales to form a feature map.…”

Section: Hdr Saliency Attributesmentioning

confidence: 99%

A learning-based visual saliency fusion model for High Dynamic Range video (LBVS-HDR)

Banitalebi-Dehkordi

Yuanyuan

Pourazad

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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Saliency prediction for Standard Dynamic Range (SDR) videos has been well explored in the last decade. However, limited studies are available on High Dynamic Range (HDR) Visual Attention Models (VAMs). Considering that the characteristic of HDR content in terms of dynamic range and color gamut is quite different than those of SDR content, it is essential to identify the importance of different saliency attributes of HDR videos for designing a VAM and understand how to combine these features. To this end we propose a learning-based visual saliency fusion method for HDR content (LVBS-HDR) to combine various visual saliency features. In our approach various conspicuity maps are extracted from HDR data, and then for fusing conspicuity maps, a Random Forests algorithm is used to train a model based on the collected data from an eye-tracking experiment. Performance evaluations demonstrate the superiority of the proposed fusion method against other existing fusion methods.Index Terms-High Dynamic Range video, HDR, visual attention model, saliency prediction

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Section: Hdr Saliency Attributesmentioning

confidence: 99%

A learning-based visual saliency fusion model for High Dynamic Range video (LBVS-HDR)

Banitalebi-Dehkordi

Yuanyuan

Pourazad

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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“…Further it is assumed that using the textural part for the computation of the optical flow is more robust against shadow and shading artefacts. A study comparing various filters is given in (Vaudrey et al, 2009). …”

Section: Illumination Changesmentioning

confidence: 99%

Optical Flow for Glacier Motion Estimation

Vogel

Bauder

Schindler

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Quantitative measurements of glacier flow over time are an important ingredient for glaciological research, for example to determine the mass balances and the evolution of glaciers. Measuring glacier flow in multi-temporal images involves the estimation of a dense set of corresponding points, which in turn define the flow vectors. Furthermore glaciers exhibit rather difficult radiometry, since their surface usually contains homogeneous areas as well as weak texture and contrast. To date glacier flow is usually observed by manually measuring a sparse set of correspondences, which is labor-intensive and often yields rather irregular point distributions, with the associated problems of interpolating over large areas. In the present work we propose to densely compute motion vectors at every pixel, by using recent robust methods for optic flow computation. Determining the optic flow, i.e. the dense deformation field between two images of a dynamic scene, has been a classic, long-standing research problem in computer vision and image processing. Sophisticated methods exist to optimally balance data fidelity with smoothness of the motion field. Depending on the strength of the local image gradients these methods yield a smooth trade-off between matching and interpolation, thereby avoiding the somewhat arbitrary decision which discrete anchor points to measure, while at the same time mitigating the problem of gross matching errors. We evaluate our method by comparing with manually measured point wise ground truth.

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Improving Optical Flow Using Residual and Sobel Edge Images

Cited by 3 publications

References 16 publications

A learning-based visual saliency fusion model for High Dynamic Range video (LBVS-HDR)

A learning-based visual saliency fusion model for High Dynamic Range video (LBVS-HDR)

Optical Flow for Glacier Motion Estimation

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