A Lip Contour Extraction Method Using Localized Active Contour Model with Automatic Parameter Selection

Liu, Xin; Cheung, Yiu-ming; Li, Meng; Liu, Hailin

doi:10.1109/icpr.2010.1053

Cited by 32 publications

(25 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome this problem, in many methods, large training sets are used for training [9], several parameters (sometimes very sensitive to initialization) need to be tuned [3], or time consuming preprocessing steps must be taken. In our system, we avoid such pre-processing tasks, thus making it more general and independent of the database set.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…A large category of techniques referred to as model-based, build a model of the lips and its configurations are described by a set of model parameters. These techniques include snakes [2], active contour models [3], deformable templates [4] and several other parametric models [5]. The advantage of these techniques lies on the fact that important features are represented in a low-dimensional parameter space.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An unconstrained method for lip detection in color images

Skodras

Fakotakis

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

The use of visual information derived from accurate lip extraction, can provide features invariant to noise perturbation for speech recognition systems and can be also used in a wide variety of applications. Unlike many current automatic lip reading systems which impose several restrictions on users, our efforts are towards an unconstrained system. In this paper we introduce a method using k-means color clustering with automatically adapted number of clusters, for the extraction of the lip area. The method's performance is improved by previously applying nearest neighbor color segmentation. The extracted lip area is morphologically processed and fitted by a best-fit ellipse. The points of interest (keypoints) of the mouth area are extracted, while a corner detector for fine tuning of mouth corners is applied. Experimental tests have shown that the algorithm works very well under natural conditions and accurate extraction of lip keypoints is feasible.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An unconstrained method for lip detection in color images

Skodras

Fakotakis

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

show abstract

“…Furthermore, parameters are not spatially adaptive, failing to capture local image content. Keuper et al [14] and Liu et al [15] propose a method for dynamic adjustment of active contour parameters, applicable on the detection of cell nuclei and lip boundaries, respectively. Both methods require a priori knowledge considering the shape of the target region.…”

Section: Previous Workmentioning

confidence: 99%

Towards Self-Parameterized Active Contours for Medical Image Segmentation with Emphasis on Abdomen

Mylona

Savelonas

Maroulis

2013

Abdomen and Thoracic Imaging

View full text Add to dashboard Cite

Medical doctors are typically required to segment medical images by means of computational tools, which suffer from parameters that are empirically selected through a cumbersome and time-consuming process. This chapter presents a framework for automated parameterization of region-based active contour regularization and data fidelity terms, which aims to relieve medical doctors from this process, as well as to enhance objectivity and reproducibility. Leaned on an observed isomorphism between the eigenvalues of structure tensors and active contour parameters, the presented framework automatically adjusts active contour parameters so as to reflect the orientation coherence in edge regions by means of the "orientation entropy." To this end, the active contour is repelled from randomly oriented edge regions and is navigated towards structured ones, accelerating contour convergence. Experiments are conducted on abdominal imaging domains, which include colon and lung images. The experimental evaluation demonstrates that the presented framework is capable of speeding up contour convergence, whereas it achieves high-quality segmentation results, albeit in an unsupervised fashion.

show abstract

“…Additionally, Keuper et al [4] and Liu et al [5] proposed a dynamic parameter adjustment, primarily dependent on the shape of the object of interest. Iakovidis et al [6] and Hsu et al [7] presented a framework for optimization of active contour parameters based on genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Self-adjusted active contours using multi-directional texture cues

Mylona

Savelonas

Maroulis

2013

2013 IEEE International Conference on Image Processing

View full text Add to dashboard Cite

Parameterization is an open issue in active contour research, associated with the cumbersome and time-consuming process of empirical adjustment. This work introduces a novel framework for self-adjustment of region-based active contours, based on multi-directional texture cues. The latter are mined by applying filtering transforms characterized by multi-resolution, anisotropy, localization and directionality. This process yields to entropy-based image "heatmaps", used to weight the regularization and data fidelity terms, which guide contour evolution. Experimental evaluation is performed on a large benchmark dataset as well as on textured images. Τhe segmentation results demonstrate that the proposed framework is capable of accelerating contour convergence, maintaining a segmentation quality which is comparable to the one obtained by empirically adjusted active contours.

show abstract

A Lip Contour Extraction Method Using Localized Active Contour Model with Automatic Parameter Selection

Cited by 32 publications

References 6 publications

An unconstrained method for lip detection in color images

An unconstrained method for lip detection in color images

Towards Self-Parameterized Active Contours for Medical Image Segmentation with Emphasis on Abdomen

Self-adjusted active contours using multi-directional texture cues

Contact Info

Product

Resources

About