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Image segmentation using Scale-Space Random Walks
2009 16th International Conference on Digital Signal Processing, 2009Many 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.
Dimitrios Androutsos +2 more
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Localization Scale Selection for Scale-Space Segmentation
2005In this work the relation between scale-space image segmentation and selection of the localization scale is examined first, and a scale selection approach is consequently proposed in the segmentation context. Considering the segmentation part, gradient watersheds are applied to the non-linear scale-space domain followed by a grouping operation.
Sokratis Makrogiannis +1 more
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Bifurcation of Segment Edge Curves in Scale Space
2012In this paper, we aim to develop a criterion to select scale parameters, which control pre-smoothing for edge detection. We first formalise the Canny edge detector which extracts the zeros of bilinear form of the first- and the second-order derivatives of image intensity.
Atsushi Imiya +3 more
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Size-adapted microcalcification segmentation in mammography utilizing scale-space signatures
Computerized Medical Imaging and Graphics, 2010The purpose of this study is size-adapted segmentation of individual microcalcifications in mammography, based on microcalcification scale-space signature estimation, enabling robust scale selection for initialization of multiscale active contours. Segmentation accuracy was evaluated by the area overlap measure, by comparing the proposed method and two
Spyros Skiadopoulos +6 more
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Scale-space approximated convolutional neural networks for retinal vessel segmentation
Computer Methods and Programs in Biomedicine, 2019Retinal fundus images are widely used to diagnose retinal diseases and can potentially be used for early diagnosis and prevention of chronic vascular diseases and diabetes. While various automatic retinal vessel segmentation methods using deep learning have been proposed, they are mostly based on common CNN structures developed for other tasks such as ...
Soochahn Lee +2 more
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Fuzzy homogeneity and scale-space approach to color image segmentation
Pattern Recognition, 2003Image segmentation is the procedure in which the original image is partitioned into homogeneous regions, and has many applications. In this paper, a fuzzy homogeneity and scale-space approach to color image segmentation is proposed. A color image is transformed into fuzzy domain with maximum fuzzy entropy principle.
Jiguang Li, Heng-Da Cheng
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Segmenting by Compression Using Linear Scale-Space and Watersheds
1999Automatic segmentation is performed using watersheds of the gradient magnitude and compression techniques. Linear Scale-Space is used to discover the neighbourhood structure and catchment basins are locally merged with Minimum Description Length. The algorithm can form a basis for a large range of automatic segmentation algorithms based on watersheds ...
Sporring, Jon, Olsen, Ole Fogh
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Retinal vessel segmentation using Morphological Angular Scale-Space
2012 Third International Conference on Emerging Applications of Information Technology, 2012In 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.
Rohit Kamal Chatterjee, Anirban Kundu
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1995
A segmentation scheme based on tracing objects and borders through scale space is proposed. Scale space allows to create a hierarchical representation of input data which can be used to tessellate input space into objects with closed and orientable borders. For analyzing the structure of scale space, a neural network approach using synchronizing neural
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A segmentation scheme based on tracing objects and borders through scale space is proposed. Scale space allows to create a hierarchical representation of input data which can be used to tessellate input space into objects with closed and orientable borders. For analyzing the structure of scale space, a neural network approach using synchronizing neural
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Hierarchical image segmentation using a watershed scale-space tree
7th International Conference on Image Processing and its Applications, 1999The 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, R.V. Aldridge
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