Projection based method for segmentation of human face and its evaluation

2002-12-01
Baskan, S
Bulut, MM
Atalay, Mehmet Volkan
Show full item record
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
152
views
0
downloads
We detect facial features and then circumscribe each facial feature with the smallest rectangle possible by using vertical and horizontal gray value projections of pixels. The result is evaluated with respect to the manually located enclosing rectangle on the images of a publicly available database.
Facial feature segmentation, Face detection, Gray value projections, Segmentation evaluation
https://hdl.handle.net/11511/41496
PATTERN RECOGNITION LETTERS
Department of Computer Engineering, Article

Suggestions

Segmentation of human face using gradient-based approach
Baskan, S; Bulut, MM; Atalay, Mehmet Volkan (2001-01-23)
This paper describes a method for automatic segmentation of facial features such as eyebrows, eyes, nose, mouth and ears in colour images. This work is an initial step for wide range of applications based on feature-based approaches, such as face recognition, lip-reading, gender estimation, facial expression analysis, etc. Human face can be characterised by its skin colour and nearly elliptical shape. For this purpose, face detection is performed using colour and shape information. Uniform illumination is a...
REGION-BASED IMAGE SEGMENTATION VIA GRAPH CUTS
Cigla, Cevahir; Alatan, Abdullah Aydın (2008-01-01)
A graph theoretic color image segmentation algorithm is proposed, in which the popular normalized cuts image segmentation method is improved with modifications on its graph structure. The image is represented by a weighted undirected graph, whose nodes correspond to over-segmented regions, instead of pixels, that decreases the complexity of the overall algorithm. In addition, the link weights between the nodes are calculated through the intensity similarities of the neighboring regions. The irregular distri...
Multi-image region growing for integrating disparity maps
Leloglu, UĞUR MURAT; Halıcı, Uğur (1999-01-01)
In this paper, a multi-image region growing algorithm to obtain planar 3-D surfaces in the object space from multiple dense disparity maps, is presented. A surface patch is represented by a plane equation and a set of pixels in multiple images. The union of back projections of all pixels in the set onto the infinite plane, forms the surface patch. Thanks to that hybrid representation of planar surfaces, region growing (both region aggregation and region merging) is performed on all images simultaneously. Pl...
Segmentation based distortion equalizing image coding
İmre, Evren; Severcan, Mete; Department of Electrical and Electronics Engineering (2002)
In image coding, measures of distortion based on image statistics may be disappointing, if properties of human visual system are not considered. Common problems are smoothed edges and different reconstruction qualities of different regions in the image. This study aims to achieve image coding with perceptually good reconstruction quality, by seeking solutions to the above problems. Proposed scheme, distortion equalizing coder (DEC), is based on contour- texture approach. Image is segmented, and, contour and...
Edge strength functions as shape priors in image segmentation
Erdem, Erkut; Erdem, Aykut; Tarı, Zehra Sibel (2005-12-01)
Many applications of computer vision requires segmenting out of an object of interest from a given image. Motivated by unlevel-sets formulation of Raviv, Kiryati and Sochen [8] and statistical formulation of Leventon, Grimson and Faugeras [6], we present a new image segmentation method which accounts for prior shape information. Our method depends on Ambrosio-Tortorelli approximation of Mumford-Shah functional. The prior shape is represented by a by-product of this functional, a smooth edge indicator functi...
Citation Formats
S. Baskan, M. Bulut, and M. V. Atalay, “Projection based method for segmentation of human face and its evaluation,” PATTERN RECOGNITION LETTERS, pp. 1623–1629, 2002, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41496.
METU IIS - Integrated Information Service open@metu.edu.tr