+ Site Statistics
References:
54,258,434
Abstracts:
29,560,870
PMIDs:
28,072,757
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Maximum likelihood estimation of markov-process blob boundaries in noisy images



Maximum likelihood estimation of markov-process blob boundaries in noisy images



IEEE Transactions on Pattern Analysis and Machine Intelligence 1(4): 372-384



Effective and elegant procedures have recently appeared in the published literature for determining by computer a highly variable blob boundary in a noisy image [1]-[3]. In this paper we point out that if the blob boundary is modeled as a Markov process and the additive noise is modeled as a white Gaussian noise field, then maximization of the joint likelihood of the hypothesized blob boundary and all of the image data results in roughly the same blob boundary determination as does one of the aforementioned deterministic formulations [2]. However, the formulation in this paper provides insights into and optimal parameter values for the functions involved and reveals suboptimalities in some of the formulations appearing in the literature. More generally, we agree that maximization of the joint likelihood of the hypothesized blob boundary and of the entire picture function is a fundamental approach to boundary estimation or the estimation of linear features (roads, rivers, etc.) in images, and provides a powerful mechanism for designing sequential, parallel, or other boundary estimation algorithms. The ripple filter, an advanced form of region growing, is briefly introduced and illustrates one of a number of alternative algorithms for maximizing the likelihood function. Hence, this likelihood maximization approach provides a unified view for seemingly different approaches, such as sequential boundary finding and region growing. Bounds on the accuracy of boundary estimation are readily derived with this formulation and are presented.

(PDF emailed within 0-6 h: $19.90)

Accession: 054277967

Download citation: RISBibTeXText

PMID: 21868872

DOI: 10.1109/tpami.1979.4766946


Related references

Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood. IEEE Transactions on Image Processing 8(7): 954-963, 2008

Maximum likelihood estimation of structural wave components from noisy data. Journal of the Acoustical Society of America 111(4): 1709-1717, 2002

Maximum likelihood estimation of aggregated Markov processes. Proceedings. Biological Sciences 264(1380): 375-383, 1997

Nonparametric identification and maximum likelihood estimation for hidden Markov models. Biometrika 103(2): 423-434, 2016

Maximum-penalized-likelihood estimation for independent and Markov-dependent mixture models. Biometrics 48(2): 545-558, 1992

Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods. Ecology Letters 10(7): 551-563, 2007

T-2 maximum likelihood estimation from multiple spin-echo magnitude images. Magnetic Resonance in Medicine 36(2): 287-293, 1996

A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images. IEEE Transactions on Medical Imaging 28(2): 165-172, 2009

A Framework for the Comparison of Maximum Pseudo Likelihood and Maximum Likelihood Estimation of Exponential Family Random Graph Models. Social Networks 31(1): 52-62, 2009

Estimation of the wood solid volume coefficient from CCD camera images by the maximum likelihood classification. Transactions of the Faculty of Forestry, Estonian Agricultural University (36): 107-115, 2003

Ab initio maximum likelihood reconstruction from cryo electron microscopy images of an infectious virion of the tailed bacteriophage P22 and maximum likelihood versions of Fourier Shell Correlation appropriate for measuring resolution of spherical or cylindrical objects. Journal of Structural Biology 167(3): 185-199, 2009

Estimation of heritability of growth traits in Barbary lambs using three methods (minimum variance quadratic (MIVQUE), maximum likelihood (ML) and restricted maximum likelihood analysis (REML)). Cahiers Options Mediterraneennes 6: 101-106, 1994

Maximum likelihood estimation-based denoising of magnetic resonance images using restricted local neighborhoods. Physics in Medicine and Biology 56(16): 5221-5234, 2011

Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images. Magnetic Resonance Imaging 30(10): 1512-1518, 2013

Maximum likelihood estimation of structure parameters from high resolution electron microscopy images. Part II: a practical example. Ultramicroscopy 104(2): 107-125, 2005