Computer Vision, Graphics, and Image Processing
Why mathematical morphology needs complete lattices
Signal Processing
Theoretical Aspects of Gray-Level Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
On the Statistical Properties of the F-measure
QSIC '04 Proceedings of the Quality Software, Fourth International Conference
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Grey-level hit-or-miss transforms-Part I: Unified theory
Pattern Recognition
A comparative study on multivariate mathematical morphology
Pattern Recognition
Template matching based on a grayscale hit-or-miss transform
IEEE Transactions on Image Processing
Practical implementation of super-resolution approach for SD-to-HD video up-conversion
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Spatial and spectral morphological template matching
Image and Vision Computing
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The morphological hit-or-miss transform (HMT) is a powerful tool for digital image analysis. Its recent extensions to grey level images have proven its ability to solve various template matching problems. In this paper we explore the capacity of various existing approaches to work in very noisy environments and discuss the generic methods used to improve their robustness to noise. We also propose a new formulation for a fuzzy morphological HMT which has been especially designed to deal with very noisy images. Our approach is validated through a pattern matching problem in astronomical images that consists of detecting very faint objects: low surface brightness galaxies. Despite their influence on the galactic evolution model, these objects remain mostly misunderstood by the astronomers. Due to their low signal to noise ratio, there is no automatic and reliable detection method yet. In this paper we introduce such a method based on the proposed hit-or-miss operator. The complete process is described starting from the building of a set of patterns until the reconstruction of a suitable map of detected objects. Implementation, running cost and optimisations are discussed. Outcomes have been examined by astronomers and compared to previous works. We have observed promising results in this difficult context for which mathematical morphology provides an original solution.