Structuring element

About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.

Structuring Elements Following the Optical Flow

Abstract: This paper deals with the combination of classical morphological tools and motion compensation techniques. Morphological operators have proven to be efficient for filtering and segmenting still images. For video sequences however, using motion information to modify the morphological processing is necessary. In previous work, iterative frame by frame segmentation using motion information has been developed in various forms. In this paper, motion is used at a very low level, by locally modifying the shape of the structuring element in a video sequence considered as a 3D data block. Motion adapted morphological tools are described and their use is demonstrated on video sequences. Moreover, the features of the motion model best suited to our purpose are also discussed.

A new class of morphological pyramids for multiresolution image analysis

Abstract: We study nonlinear multiresolution signal decomposition based on morphological pyramids. Motivated by a problem arising in multiresolution volume visualization, we introduce a new class of morphological pyramids. In this class the pyramidal synthesis operator always has the same form, i.e. a dilation by a structuring element A, preceded by upsampling, while the pyramidal analysis operator is a certain operator R A (n) indexed by an integer n, followed by downsampling. For n = 0, R A (n) equals the erosion e A with structuring element A, whereas for n > 0, R A (n) equals the erosion e A followed by n conditional dilations, which for n→ oo is the opening by reconstruction. The resulting pair of analysis and synthesis operators is shown to satisfy the pyramid condition for all n. The corresponding pyramids for n = 0 and n = 1 are known as the adjunction pyramid and Sun-Maragos Pyramid, respectively. Experiments are performed to study the approximation quality of the pyramids as a function of the number of iterations n of the conditional dilation operator.

Software implementation of 2D gray-level dilation using SKIPSM

Abstract: The earlier papers on SKIPSM (separated-kernel image processing using finite state machines) concentrated mainly on implementations using pipelined hardware. Because of the potential for significant speed increases, the technique has even more to offer for software implementations. However, the gigantic structuring elements (e.g., 51 by 51 in one pass) readily available in binary morphology using SKIPSM are not practical in gray-level morphology. Nevertheless, useful structuring element sizes can be achieved. This paper describes two such applications: dilation with a 7 by 7 square and a 7 by 7 octagon. Previous 2-D SKIPSM implementations had one row machine and one column machine. Two of the implementations described here follow this pattern, but the other has four machines: row, column, and the two 45-degree diagonals. In operation, all of these are one-pass algorithms: The next pixel is 'fetched' from the input device, the two (or four) machines are updated in turn, and the resulting output pixel is written to the output device. All neighborhood information needed for processing is encoded in the state vectors of the finite-state machines. Therefore, no intermediate image stores are needed. Furthermore, even the input and output image stores can be eliminated if the image processor can keep up with the input pixel rate. Comparisons are provided between these finite-state-machine implementations and conventional implementation of the 2-step and 4-step decompositions, all based on the same structuring elements.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Edge detection using morphological amoebas in noisy images

Abstract: Edge detection is a significant step in image processing. Morphological edge detectors developed until now used a fixed structuring element (SE) on all the image pixels; however, they cannot consider the local features of an image due to the fixed SE and we should choose an appropriate SE by lots of experiments. In this paper, new morphological edge detectors using amoebas, dynamic structuring elements which adapt their shapes to image contours, are proposed. The experimental results show that amoeba-based edge detectors have better performance than corresponding classic edge detectors. The proposed methods have less sensitivity to noise while detecting more details of image than other morphological edge detectors with a fixed SE.