Fast Method for Multiple Human Face Segmentation in Color Image

Adipranata, Rudy; Eddy, Sean R.; Ballangan, Cherry; Ongkodjojo, Ronald Paska

doi:10.1109/fgcn.2008.36

Cited by 21 publications

(10 citation statements)

References 14 publications

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“…Let, , where is nonsingular matrix, the eigenvalue problem in eq. (2) is converted to symmetric eigenvalue problem as [1], (3) This is the general eigenvalue problem and can be solve for different values of . Let is the eigenvector with respect to eigenvalue .…”

Section: Generalized Eigenvalue Problems In Image Processingmentioning

confidence: 99%

“…In this, the eigenvalues are calculated using the singular value decomposition (SVD). In case of human face segmentation using elliptical shape [3], largest and smallest eigenvalue of covariance matrix represent the elliptical shape. The major and minor axial length of an ellipse is depending on the largest and smallest eigenvalue of covariance matrix of an image.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Approach for Estimation of Eigenvalues of Images

Gaidhane¹,

Hote²,

Singh³

2011

IJCA

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In this paper, a new approach for estimation of eigenvalues of images is presented. The proposed approach is based on the Gerschgorin's circles theorem. This approach is more efficient as there is no need of calculation of all real eigenvalues. It is also helpful for all type of images where the calculation of eigenvalues may be impractical. More importantly, anyone can come to the conclusion by visual inspection as it is a graphical method. The estimation of eigenvalues can be used to extract the important information of images for various applications.

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Section: Generalized Eigenvalue Problems In Image Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Approach for Estimation of Eigenvalues of Images

Gaidhane¹,

Hote²,

Singh³

2011

IJCA

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show abstract

“…Recently developed methods for human face segmentation are neural networks [10], Eigen faces with background learning [13], statistical approach [14], fuzzy pattern matching [11] and color information and geometric knowledge as discussed by Rudy Adipranata et al [12]. The main goal of face segmentation is to identify if, there exists a face in a given image and to extract the same for processing.…”

Section: Introductionmentioning

confidence: 99%

Human Face Image Segmentation using Level Set Methodology

Kumaravel¹,

Karthik²,

Sivraj³

et al. 2012

IJCA

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Face segmentation plays an important role in various applications such as human computer interaction, video surveillance, biometric systems, and face recognition for purposes including authentication and authorization. The accuracy of face classification system depends on the correctness of segmentation. Robustness of the face classification system is determined by the segmentation algorithm used, and the effectiveness in segmenting images of similar kind. This paper explains the level set based segmentation for human face images. The process is done in two stages: In order to get better accuracy, binarization of the image to be segmented is performed. Next, segmentation is applied on the image. Binarization is the process of setting pixel intensity values greater than some threshold value to "on" and the rest to "off". This process converts the input image into binary image which is used for segmentation. Second process is image segmentation for eliminating the background portion from the binarized image which is obtained after the binarization of the original image. Conventional approaches use separate methods for binarization and segmentation. In this paper we investigate the use of recently introduced convex optimization methods, selective local/global segmentation (SLGS) algorithm [16] for simultaneous binarization and segmentation. The approach is tested in MATLAB and satisfactory results were obtained.

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“…For nearest neighbor method case, the implemented result images shows blocking artifact, while the others give blurred result images. Therefore, the main purpose of the proposed method is to remove blocking artifacts while providing pleasant subjective results [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%