Visual Data Recognition and Modeling Based on Local Markovian Models

Haindl, Michal

doi:10.1007/978-1-4471-2353-8_14

Cited by 27 publications

(6 citation statements)

References 24 publications

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“…Multispectral textured image mosaics are locally represented by four causal directional multispectral random field models recursively evaluated for each pixel. A single local texture model is expressed as a stationary, causal, uncorrelated, noise-driven, 3-D, and autoregressive process [23] Y r = γX r + e r (2) where γ = [A 1 , . .…”

Section: B Markovian Parameter Space Segmenter Familymentioning

confidence: 99%

Benchmarking of Remote Sensing Segmentation Methods

Mikeš

Haindl

Scarpa

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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We present the enrichment of the Prague Texture Segmentation Data-Generator and Benchmark (PTSDB) to include the assessment of the remote sensing (RS) image segmenters. The PTSDB tool is a Web-based (http://mosaic.utia.cas.cz) service designed for real-time performance evaluation, mutual comparison, and ranking of various supervised or unsupervised static or dynamic image segmenters. PTSDB supports rapid verification and development of new segmentation approaches. The RS datasets contain ten spectral Advanced Land Imager (ALI) satellite images, their RGB subsets, and very-high-resolution GeoEye RGB images, with optional additive-noise-resistance checking. Alternative setting options allow us to also test scale, rotation, or illumination invariance. The meaningfulness of the newly proposed dataset is demonstrated by testing and comparing several RS segmentation algorithms, and showing that the benchmark figures provide a solid framework for the fair and critical comparison among different techniques.

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Section: B Markovian Parameter Space Segmenter Familymentioning

confidence: 99%

Benchmarking of Remote Sensing Segmentation Methods

Mikeš

Haindl

Scarpa

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The X-ray mammographic tissue is locally modeled by its dedicated independent Gaussian noise-driven autoregressive random field two-dimensional texture model (2DCAR), which is a rare exception among Markovian random field model family that can be completely analytically solved [35,36]. Apart from that, this descriptive model has good modeling performance, all statistics can be evaluated recursively, and the model is very fast to evaluate.…”

Section: Adaptive Textural Modelmentioning

confidence: 99%

“…This type of a neighbourhood system is also called a functional neighbourhood system and its application is illustrated in Figure 2. Its optimal configuration can be found analytically using the Bayesian statistics see [36] for details. Furthermore, e r denotes white Gaussian noise with zero mean and a constant but unknown variance σ 2 , and X r is a support vector of Y r−s where s ∈ I c r .…”

Section: Adaptive Textural Modelmentioning

confidence: 99%

“…Furthermore, e r denotes white Gaussian noise with zero mean and a constant but unknown variance σ 2 , and X r is a support vector of Y r−s where s ∈ I c r . The method uses a locally adaptive version of this 2DCAR model [36], where its recursive statistics are modified by an exponential forgetting factor, i.e., a constant smaller than 1 which is used to weight the older data.…”

Section: Adaptive Textural Modelmentioning

confidence: 99%

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Digital Mammogram Enhancement

Haindl¹,

Reměs²

2015

Mammography Techniques and Review

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“…For quadratic B-spline channels and θ = 1 16 this happens for λ ≥ 11 32 . A binary labelling problem as formulated in (24) (see also [28]) is efficiently solved by graph-cut algorithms [3]. Using graph-cut for determining the activation of channels, we obtain the graph-cut channel smoothing algorithm as given in Alg.…”

Section: Channel Smoothing Without Corner Roundingmentioning

confidence: 99%

Adaptive Filtering Using Channel Representations

Felsberg

2011

Computational Imaging and Vision

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This review article gives an overview on adaptive filtering methods based on channel representations. The framework of channel representations and its relation to density estimation is introduced. The fast and accurate scheme of virtual shift decoding is introduced and applied in several variants of channel smoothing:• channel smoothing with alpha-synthesis for improving stability of edge-enhancing filtering • orientation adaptive channel smoothing with applications to coherence-enhancing filtering • channel smoothing using graph-cuts for improving filtering results at corners • channel-coded feature maps (CCFMs) which lead to a significant speed-up of channel averaging • CCFM-based smoothing based on optimal parameters derived from a novel uncertainty relationFor each method, an algorithmic description and some examples of results are provided, together with discussions and references of the original papers. Cross connections to other articles in this volume are given where appropriate.

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Visual Data Recognition and Modeling Based on Local Markovian Models

Cited by 27 publications

References 24 publications

Benchmarking of Remote Sensing Segmentation Methods

Benchmarking of Remote Sensing Segmentation Methods

Digital Mammogram Enhancement

Adaptive Filtering Using Channel Representations

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