Results 241 to 250 of about 81,920 (279)

Hierarchical image segmentation using a watershed scale-space tree

open access: closed7th International Conference on Image Processing and its Applications, 1999
The watershed transformation is a useful morphological segmentation tool which has been used in a variety of grey-scale image processing applications. However, a major problem with the watershed transformation is that it produces a severe over-segmentation due to the great number of minima embedded in the image or its gradient, and therefore it is ...
Mark Fisher
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Scale-space characteristics for image segmentation

Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology. TENCON 2001 (Cat. No.01CH37239), 2002
For successful image segmentation, it is vital to discover optimal discriminating features that contrast one region from the other, or contrast salient edges from the background. We propose a new method for image segmentation based on three discriminating features: average gradient magnitude, uniformity of gradient magnitude and uniformity of gradient ...
M. Rahman   +2 more
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Segmenting multisensor aerial images in class–scale space

Pattern Recognition, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lofy, Brian, Sklansky, Jack
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Motion-driven object segmentation in scale-space

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999
In this paper we present a method for motion segmentation, in which accurate grouping of pixels undergoing the same motion is targeted. In the presented technique true object edges are first obtained by combining anisotropic diffusion of the original image with edge detection and contour reconstruction in the inherent scale-space.
E.M. Izquierdo, M. Ghanbari
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Image segmentation by analysis of scale-space

Proceedings. XV Brazilian Symposium on Computer Graphics and Image Processing, 2003
We present a technique in image segmentation based on a hierarchical analysis of scale-space. The image hierarchy is a tree of critical lakes and provides a set of nested partitions. We analyze the scale-space from fine to coarse levels according to specific attributes.
Garcia de Carvalho, Marco António   +2 more
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Image segmentation using Scale-Space Random Walks

2009 16th International Conference on Digital Signal Processing, 2009
Many methods for supervised image segmentation exist. One such algorithm, Random Walks, is very fast and accurate when compared to other methods. A drawback to Random Walks is that it has difficulty producing accurate and clean segmentations in the presence of noise.
Richard Rzeszutek   +2 more
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Acoustic-phonetic segment classification and scale-space filtering

ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
Scale-space filtering represents one method for automatically extracting both coarse and fine-grained units from the speech signal. We examine the acoustic-phonetic structure of segments obtained by scale-space filtering of cochleagrams, and report on the correspondences between scale-space segments which are automatically derived and hand-marked ...
M. Withgott, S. Bagley, R. Lyon, M. Bush
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Retinal vessel segmentation using Morphological Angular Scale-Space

2012 Third International Conference on Emerging Applications of Information Technology, 2012
In this paper, segmentation of retinal vessel is done using an innovative procedure named as Morphological Angular Scale-Space (MASS). A linear structuring element rotated at different angles determines the connected components and ensuring that connectivity is not lost across the vessels.
Anirban Kundu, Rohit Kamal Chatterjee
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Scale Space Hierarchy of Segments

2009
In this paper, we develop a segmentation algorithm using configurations of singular points in the linear scale space. We define segment edges as a zero-crossing set in the linear scale space using the singular points. An image in the linear scale space is the convolution of the image and the Gaussian kernel.
Haruhiko Nishiguchi   +2 more
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