Structuring element

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

New approach for old movie restoration

Abstract: The role in which old movies play to the society is a priceless part of culture and history. These movies, due mainly to the storage conditions, may present a type of local defect with two characteristics: it does not affect the whole frame; and it is thin along the time axis. In this work, we attack the local defect detection problem and defects in consecutive images with any intersection using an opening by temporal surface, followed by a spatial geodesic reconstruction by dilation using structuring element Bs. The opening by surface, for binary images, extracts the connect components which area is greater than a specific threshold, and for gray level images, it evaluates each connected component produced by successive thresholds of the images, through binary operations. The restoration process using opening by surface can be subdivided in two steps: 1) opening by temporal surface applied to the image sequences that extracts connected components or domes with area greater than or equal to a specific threshold S; 2) spatial reconstruction by geodesic dilation using structuring element applied to a single image. This technique is being applied to a move collection (1920 decade) of the Brazilian ex-president Arthur Bernardes provided by the Arquivo Publico Mineiro in Belo Horizonte, MG, Brazil.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Morphological neural networks for automatic target detection by simulated annealing learning algorithm

Abstract: A practical neural network model for morphological filtering and a simulated annealing optimal algorithm for the network parameters training are proposed in this paper. It is pointed out that the optimal designing process of the morphological filtering network in fact is the optimal learning process of adjusting network parameters (structuring element, or SE for short) to accommodate image environment. Then the network structure may possess the characteristics of image targets, and so give specific information to the SE. Morphological filters formed in this way become certainly intelligent and can provide good filtering results and robust adaptability to complex changing image. For application to motional image target detection, dynamic training algorithm is applied to the designing process using asymptotic shrinking error and appropriate network weights adjusting. Experimental results show that the algorithm has invariant property with respect to shift, scale and rotation of moving target in continuing detection of moving targets.

Image denoising algorithm based on PSO optimizing structuring element

Abstract: A new image denoising algorithm is proposed to deal with information loss in the conventional morphological image denoising process. The algorithm uses median operation to improve morphological operations' performance, which called median closing operation. It gives a mathematical model of the structuring element unit (SEU) composed of a zero square matrix. The particle swarm optimization (PSO) algorithm is employed for choosing the size of structuring element. The value of peak signal to noise ratio (PSNR) is taken as a fitness function, and the transformed value of the particle's position is taken as the size of the structuring element. Experimental results show that the denoising performance of the proposed algorithm has obvious superiority than conventional morphological algorithm. It can overcome the inherent deficiency of conventional morphological operations, adaptively obtain the size of the structuring element, and effectively remove impulse noise from images, especially for the image whose signal to noise ratio value is relatively low. So it has a good prospect in image processing.

Automatic urban road extraction on DSM data based on fuzzy ART, region growing, morphological operations and radon transform

Abstract: In recent years, an automatic urban road extraction, as part of Intelligent Transportation research, has attracted the researchers due to the important role for the next modern transportation where urban area plays the main role within the transportation system. In this work, we propose a new combination of fuzzy ART clustering, Region growing, Morphological Operations and Radon transform (ARMOR) for automatic extraction of urban road networks from the digital surface model (DSM). The DSM data, which is based-on the elevation of surface, overcome a serious building's shadow problem as in the aerial photo image. Due to the different elevation between the road and the buildings, the thresholding technique yields a fast initial road extraction. The threshold values are obtained from Fuzzy ART clustering of the geometrical points in the histogram. The initial road is then expanded using region growing. Though most of the road regions are extracted, it contains a lot of non-road areas and the edge is still rough. A fast way to smoothing the region is by employing the morphology closing operation. Furthermore, we perform the road line filter by opening operation with a line shape structuring element, where the line orientation is obtained from the Radon Transform. Finally, the road network is constructed based-on B-Spline from the extracted road skeleton. The experimental result shows that the proposed method running faster and increases the quality and the accuracy about 10% higher than the highest result of the compared method.