Challenging eye segmentation using Triplet Markov spatial models

Benboudjema, Dalila; Othman, Nadia; Dorizzi, Bernadette; Pieczynski, Wojciech

doi:10.1109/icassp.2013.6637989

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…In comparison with classical works, this method achieves high performance in terms of parametric contours detection. Recently, the unsupervised segmentation approach based on Triplet Markov Field (TMF) [3] has shown its superiority over HMF for segmenting challenging eye image. However, Markov Fields based methods are very computationally expensive.…”

Section: Related Workmentioning

confidence: 99%

Markov Chains for unsupervised segmentation of degraded NIR iris images for person recognition

Yahiaoui

Monfrini

Dorizzi

2016

Pattern Recognition Letters

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Section: Related Workmentioning

confidence: 99%

Markov Chains for unsupervised segmentation of degraded NIR iris images for person recognition

Yahiaoui

Monfrini

Dorizzi

2016

Pattern Recognition Letters

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“…Mobile Information Systems built edge detectors based on a set of features including intensity, gradient, texture, and structure information to characterize the edge points and learned six class-specific boundary detectors with AdaBoost [43] for the localization of pupillary and limbic boundaries. Benboudjema et al [44] presented an implementation of triplet Markov fields (TMF) [45] for segmentation. Happold [46] trained a faststructured random forest [47] for learning generalized edge detectors.…”

Section: Background Of Iris Segmentationmentioning

confidence: 99%

An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning

Huang

Juan

2019

Mobile Information Systems

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Iris segmentation is a critical step in the entire iris recognition procedure. Most of the state-of-the-art iris segmentation algorithms are based on edge information. However, a large number of noisy edge points detected by a normal edge-based detector in an image with specular reflection or other obstacles will mislead the pupillary boundary and limbus boundary localization. In this paper, we present a combination method of learning-based and edge-based algorithms for iris segmentation. A well-designed Faster R-CNN with only six layers is built to locate and classify the eye. With the bounding box found by Faster R-CNN, the pupillary region is located using a Gaussian mixture model. Then, the circular boundary of the pupillary region is fit according to five key boundary points. A boundary point selection algorithm is used to find the boundary points of the limbus, and the circular boundary of the limbus is constructed using these boundary points. Experimental results showed that the proposed iris segmentation method achieved 95.49% accuracy on the challenging CASIA-Iris-Thousand database.

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“…TMCs have also been used for continuous hidden sequences in Kalman filtering [30], in prediction [31], or still optimal fast filtering in a particular class of switching systems [32]. Finally, let us mention that hidden Markov fields have also been extended to triplet Markov fields [33], and have been successfully applied to complex structure data classification [34], in SAR images processing [35], [36], [37], [38] or biometry [39].…”

Section: Introductionmentioning

confidence: 99%

Phasic Triplet Markov Chains

Boudaren

Monfrini

Pieczynski

et al. 2014

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Hidden Markov chains have been shown to be inadequate for data modeling under some complex conditions. In this work, we address the problem of statistical modeling of phenomena involving two heterogeneous system states. Such phenomena may arise in biology or communications, among other fields. Namely, we consider that a sequence of meaningful words is to be searched within a whole observation that also contains arbitrary one-by-one symbols. Moreover, a word may be interrupted at some site to be carried on later. Applying plain hidden Markov chains to such data, while ignoring their specificity, yields unsatisfactory results. The Phasic triplet Markov chain, proposed in this paper, overcomes this difficulty by means of an auxiliary underlying process in accordance with the triplet Markov chains theory. Related Bayesian restoration techniques and parameters estimation procedures according to the new model are then described. Finally, to assess the performance of the proposed model against the conventional hidden Markov chain model, experiments are conducted on synthetic and real data.

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Challenging eye segmentation using Triplet Markov spatial models

Cited by 7 publications

References 19 publications

Markov Chains for unsupervised segmentation of degraded NIR iris images for person recognition

Markov Chains for unsupervised segmentation of degraded NIR iris images for person recognition

An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning

Phasic Triplet Markov Chains

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