Mathematical morphology provides powerful nonlinear operators for a variety of image processing tasks such as filtering, segmentation, and edge detection. In this paper, we propose a way to use these nonlinear operators in an end-toend deep learning framework and illustrate them on different applications. We demonstrate on various examples that new layers making use of the morphological non-linearities are complementary to convolution layers. These new layers can be used to integrate the non-linear operations and pooling into a joint operation. We finally enhance results obtained in boundary detection using this new family of layers with just 0.01% of the parameters of competing state-of-the-art methods.
Image segmentation is the process of partitioning an image into a set of meaningful regions according to some criteria. Hierarchical segmentation has emerged as a major trend in this regard as it favors the emergence of important regions at different scales. On the other hand, many methods allow us to have prior information on the position of structures of interest in the images. In this paper, we present a versatile hierarchical segmentation method that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest while preserving the important structures in the image. Several applications are presented that illustrate the method versatility and efficiency.
The segmentation, seen as the association of a partition with an image, is a difficult task. It can be decomposed in two steps: at first, a family of contours associated with a series of nested partitions (or hierarchy) is created and organized, then pertinent contours are extracted. A coarser partition is obtained by merging adjacent regions of a finer partition. The strength of a contour is then measured by the level of the hierarchy for which its two adjacent regions merge. We present an automatic segmentation strategy using a wide range of stochastic watershed hierarchies. For a given set of homogeneous images, our approach selects automatically the best hierarchy and cut level to perform image simplification given an evaluation score. Experimental results illustrate the advantages of our approach on several real-life images datasets.
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