A Learning Framework for Morphological Operators using Counter-Harmonic Mean

We present a novel framework for learning morphological operators using counter-harmonic mean. It combines concepts from morphology and convolutional neural networks. A thorough experimental validation analyzes basic morphological operators dilation and erosion, opening and closing, as well as the much more complex top-hat transform, for which we report a real-world application from the steel industry. Using online learning and stochastic gradient descent, our system learns both the structuring element and the composition of operators. It scales well to large datasets and online settings.Comment: Submitted to ISMM'1

A Fast, Memory-Efficient Alpha-Tree Algorithm using Flooding and Tree Size Estimation

The alpha-tree represents an image as hierarchical set of alpha-connected components. Computation of alpha-trees suffers from high computational and memory requirements compared with similar component tree algorithms such as max-tree. Here we introduce a novel alpha-tree algorithm using 1) a flooding algorithm for computational efficiency and 2) tree size estimation (TSE) for memory efficiency. In TSE, an exponential decay model was fitted to normalized tree sizes as a function of the normalized root mean squared deviation (NRMSD) of edge-dissimilarity distributions, and the model was used to estimate the optimum memory allocation size for alpha-tree construction. An experiment on 1256 images shows that our algorithm runs 2.27 times faster than Ouzounis and Soille's thanks to the flooding algorithm, and TSE reduced the average memory allocation of the proposed algorithm by 40.4%, eliminating unused allocated memory by 86.0% with a negligible computational cost

Constructive links between some morphological hierarchies on edge-weighted graphs

International audienceIn edge-weighted graphs, we provide a unified presentation of a family of popular morphological hierarchies such as component trees, quasi flat zones, binary partition trees, and hierarchical watersheds. For any hierarchy of this family, we show if (and how) it can be obtained from any other element of the family. In this sense, the main contribution of this paper is the study of all constructive links between these hierarchies

A study of observation scales based on Felzenswalb-Huttenlocher dissimilarity measure for hierarchical segmentation

International audienceHierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. This HGB method computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should merge according to the dissimilarity. In order to generalize this method, we first propose an algorithm to compute the intervals which contain all the observation scales at which the associated regions should merge. Then, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy, we present various strategies to select a significant observation scale in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method

Quantification of valvular regurgitation by cardiac blood pool scintigraphy: correlation with catheterization

The diagnosis of valvular regurgitation (R) is usually based on clinical signs. Quantification conventionally requires catheterization (C). We have quantified R with cardiac blood pool scintigraphy (CBPS) and compared the results with those obtained by C. Regurgitant fraction (RF) determined by C was calculated with the technique of Dodge. Forward output was measured by thermodilution or cardiogreen dilution. The RF at CBPS was obtained by the stroke index ratio (SIR) minus 1.2 divided by SIR, where SIR is the ratio of the stroke counts of left venticle over those of the right ventricle. Stroke counts are calculated directly from the time-activity curves. Each time-activity curve was obtained by drawing one region of interest around each diastolic image. The correction factor (1.2) was calculated from a large normal population. 22 patients had aortic R, 7 mitral R, 12 both, 8 patients had no evidence of regurgitation. RF of the patients with R varied from 27 to 71% (x = 42%) at C and from 26 to 74% (Y = 41%) at CBPS. Linear regression shows a good correlation coefficient (r = 0.82). The regression equation is y = 0.93x + 1.8. No correlation was found between RF (CBPS or C) and the severity of R assessed visually from angiography. In conclusion: CBPS, a non-invasive method, allows easy and repeatable determination of RF and correlates well with data obtained at catheterizatio

Climbing: A Unified Approach for Global Constraints on Hierarchical Segmentation

International audienceThe paper deals with global constraints for hierarchical segmentations. The proposed framework associates, with an input image, a hierarchy of segmentations and an energy, and the subsequent optimization problem. It is the first paper that compiles the different global constraints and unifies them as Climbing energies. The transition from global optimization to local optimization is attained by the h-increasingness property, which allows to compare parent and child partition energies in hierarchies. The laws of composition of such energies are established and examples are given over the Berkeley Dataset for colour and texture segmentation

Image decompositions and transformations as peaks and wells

10 pages to be submitted to ISMM2011International audienceAn image may be decomposed as a difference between an image of peaks and an image of wells. Applying a morphological operator to these two components before reconstructing a final image produces interesting filters for grey tone or binary images. This decomposition depends upon the point of view from where the image is considered

Attribute Controlled Reconstruction and Adaptive Mathematical Morphology

ISBN : 978-3-642-38293-2International audienceIn this paper we present a reconstruction method controlled by the evolution of attributes. The process begins from a marker, propagated over increasing quasi-flat zones. The evolution of several increasing and non-increasing attributes is studied in order to select the appropriate region. Additionally, the combination of attributes can be used in a straightforward way. To demonstrate the performance of our method, three applications are presented. Firstly, our method successfully segments connected objects in range images. Secondly, input-adaptive structuring elements (SE) are defined computing the controlled propagation for each pixel on a pilot image. Finally, input-adaptive SE are used to assess shape features on the image. Our approach is multi-scale and auto-dual. Compared with other methods, it is based on a given attribute but does not require a size parameter in order to determine appropriate regions. It is useful to extract objects of a given shape. Additionally, our reconstruction is a connected operator since quasi-flat zones do not create new contours on the image