Proceedings ArticleDOI
Active hypercontours and contextual classification
Arkadiusz Tomczyk
- pp 256-261
TLDR
The idea of active hypercontours generalizes traditional active contour methods which are extensively developed in image analysis and can enable an incorporation of techniques specific for traditional contextual and non-contextual classification problems to active contours approach.Abstract:
In the paper a concept of active hypercontours as well as a formalized description of contextual classification and relationship between them are presented. The idea of active hypercontours generalizes traditional active contour methods which are extensively developed in image analysis. The proposed concepts can enable an incorporation of techniques specific for traditional contextual and non-contextual classification problems to active contour approach and vice versa.read more
Citations
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Journal ArticleDOI
Classification Using Geometric Level Sets
Kush R. Varshney,Alan S. Willsky +1 more
TL;DR: In this paper, a variational level set method is developed for the supervised classification problem, where nonlinear classifier decision boundaries are obtained by minimizing an energy functional that is composed of an empirical risk term with a margin-based loss and a geometric regularization term new to machine learning.
Journal Article
Adaptive Potential Active Hypercontours
TL;DR: In this article, the adaptive potential active hypercontours (APAH) algorithm is proposed for image classification. But the proposed method is not suitable for image segmentation and it requires the use of the potential hypercontour to evolve with the evolution of the image.
Journal ArticleDOI
Notes on a Linguistic Description as the Basis for Automatic Image Understanding
TL;DR: The crucial elements of the presented approach are the formalisation of human knowledge about the class of images that are to be automatically interpreted, a linguistic description and the realization of cognitive resonance.
Book ChapterDOI
Image Segmentation Using Adaptive Potential Active Contours
TL;DR: The presented approach allows to obtain contours of various topology and a new adaptation mechanism can be introduced to improve segmentation results.
References
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Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Journal ArticleDOI
Snakes : Active Contour Models
TL;DR: This work uses snakes for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest, and uses scale-space continuation to enlarge the capture region surrounding a feature.
Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Journal ArticleDOI
Active shape models—their training and application
TL;DR: This work describes a method for building models by learning patterns of variability from a training set of correctly annotated images that can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes).
Journal ArticleDOI
Geodesic active contours
TL;DR: A novel scheme for the detection of object boundaries based on active contours evolving in time according to intrinsic geometric measures of the image, allowing stable boundary detection when their gradients suffer from large variations, including gaps.