A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature detection from local energy
Pattern Recognition Letters
Algorithms for clustering data
Algorithms for clustering data
On the classification of image features
Pattern Recognition Letters
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Alignment by Maximization of Mutual Information
Alignment by Maximization of Mutual Information
3D-orientation space: filters and sampling
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Bone Segmentation and Fracture Detection in Ultrasound Using 3D Local Phase Features
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Spatio-temporal composite-features for motion analysis and segmentation
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Hi-index | 0.00 |
Most image analysis techniques are based upon low level descriptions of the data. It is important that the chosen representation is able to discriminate as much as possible among independent image features. In particular, this is of great importance in segmentation with deformable models, which must be guided to the target object boundary avoiding other image features. In this paper, we present a multiresolution method for the decomposition of a volumetric image into its most relevant visual patterns, which we define as features associated to local energy maxima of the image. The method involves the clustering of a set of predefined band-pass energy filters according to their ability to segregate the different features in the image. In this way, the method generates a set of composite-feature detectors tuned to the specific visual patterns present in the data. Clustering is accomplished by defining a distance metric between the frequency features that reflects the degree of alignment of their energy maxima. This distance is related to the mutual information of their responses' energy maps. As will be shown, the method is able to isolate the frequency components of independent visual patterns in 3D images. We have applied this composite-feature detection method to the initialization of active models. Among the visual patterns detected, those associated to the segmentation target are selected by user interaction to define the initial state of a geodesic active model. We will demonstrate that this initialization technique facilitates the evolution of the model to the proper boundary.