Markov Random Field Modeling in Computer Vision

Li, S. Z.

doi:10.1007/978-4-431-66933-3

Cited by 982 publications

(791 citation statements)

References 195 publications

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“…SASI is based on the concepts of clique [20] and autocorrelation coefficient. In the following, SASI descriptor is introduced along with the background definitions.…”

Section: Definitionsmentioning

confidence: 99%

SASI: a generic texture descriptor for image retrieval

Carkacioglu

Yarman-Vural

2003

Pattern Recognition

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In this paper, a generic texture descriptor, namely, Statistical Analysis of Structural Information (SASI) is introduced as a representation of texture. SASI is based on statistics of clique autocorrelation coefficients, calculated over structuring windows. SASI defines a set of clique windows to extract and measure various structural properties of texture by using a spatial multi-resolution method. Experimental results, performed on various image databases, indicate that SASI is more successful then the Gabor Filter descriptors in capturing small granularities and discontinuities such as sharp corners and abrupt changes. Due to the flexibility in designing the clique windows, SASI reaches higher average retrieval rates compared to Gabor Filter descriptors. However, the price of this performance is increased computational complexity.

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“…SASI is based on the concepts of clique [20] and autocorrelation coefficient. In the following, SASI descriptor is introduced along with the background definitions.…”

Section: Definitionsmentioning

confidence: 99%

SASI: a generic texture descriptor for image retrieval

Carkacioglu

Yarman-Vural

2003

Pattern Recognition

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“…However, as the size of the neighborhood system increases, the number of unknown variables becomes too large to be solved using SNOPT. By exploiting Markov-Gibbs equivalence (Besag, 1974;Li, 1995), we can write the conditional probability in terms of clique potentials. We believe that by using the Gibbs formulation and by exploiting isotropy within cliques the number of unknown variables can be further reduced, so that we can utilize a MRF model based on a second-order neighborhood system (i.e., an 8-neighborhood).…”

Section: Discussionmentioning

confidence: 99%

“…MRFs have been used in computer vision and image processing for texture synthesis (Cross and Jain, 1983;Efros and Leung, 1999;Paget and Longstaff, 1998), image segmentation (Derin and Elliott, 1987), and image restoration (Geman and Geman, 1984;Li, 1995). The states of MRF texture models are all possible gray levels and directly observable.…”

Section: Related Workmentioning

confidence: 99%

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Markov Random Field Modeling of the Spatial Distribution of Proteins on Cell Membranes

Zhang¹

2007

Bull. Math. Biol.

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Cell membranes display a range of receptors that bind ligands and activate signaling pathways. Signaling is characterized by dramatic changes in membrane molecular topography, including the co-clustering of receptors with signaling molecules and the segregation of other signaling molecules away from receptors. Electron microscopy of immunogold-labeled membranes is a critical technique to generate topographical information at the 5-10 nm resolution needed to understand how signaling complexes assemble and function. However, due to experimental limitations, only two molecular species can usually be labeled at a time. A formidable challenge is to integrate experimental data across multiple experiments where there are from 10 to 100 different proteins and lipids of interest and only the positions of two species can be observed simultaneously. As a solution, we propose the use of Markov random field (MRF) modeling to reconstruct the distribution of multiple cell membrane constituents from pair-wise data sets. MRFs are a powerful mathematical formalism for modeling correlations between states associated with neighboring sites in spatial lattices. The presence or absence of a protein of a specific type at a point on the cell membrane is a state. Since only two protein types can be observed, i.e., those bound to particles, and the rest cannot be observed, the problem is one of deducing the conditional distribution of a MRF with unobservable (hidden) states. Here, we develop a multiscale MRF model and use mathematical programming techniques to infer the conditional distribution of a MRF for proteins of three types from observations showing the spatial relationships between only two types. Application to synthesized data shows that the spatial distributions of three proteins can be reliably estimated. Application to experimental data provides the first maps of the spatial relationship between groups of three different signaling molecules. The work is an important step toward a more complete understanding of membrane spatial organization and dynamics during signaling.

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“…The segmented images are expected to consist of regions within which the image content is homogeneous, while the contrast between neighboring regions is high. Typical methods falling into this category include region growing, watershed, some MRF-based methods [3], mean-shift [9] and the recently presented lossy data compression-based approach [10]. Segmentation methods based on the boundary or edge information are designed to exploit the discontinuity of the image features, such as difference in texture or pixel intensity, on the two sides of the boundary.…”

Section: Boundary (Edge) Informationmentioning

confidence: 99%

A human visual system-driven image segmentation algorithm

Peng¹,

Varshney²

2015

Journal of Visual Communication and Image Representation

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This paper presents a novel image segmentation algorithm driven by human visual system (HVS) properties. Quality metrics for evaluating the segmentation result, from both region-based and boundary-based perspectives, are integrated into an objective function. The objective function encodes the HVS properties into a Markov random fields (MRF) framework, where the just-noticeable difference (JND) model is employed when calculating the difference between the image contents. Experiments are carried out to compare the performances of three variations of the presented algorithm and several representative segmentation algorithms available in the literature. Results are very encouraging and show that the presented algorithms outperform the state-of-the-art image segmentation algorithms. Index Terms-Image segmentation, human visual system, Markov random fields, just-noticeable difference KEYWORDS: Abstract-This EDICS-ARS-RBS

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Markov Random Field Modeling in Computer Vision

Cited by 982 publications

References 195 publications

SASI: a generic texture descriptor for image retrieval

SASI: a generic texture descriptor for image retrieval

Markov Random Field Modeling of the Spatial Distribution of Proteins on Cell Membranes

A human visual system-driven image segmentation algorithm

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