Topic
Structuring element
About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.
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24 Mar 2020
TL;DR: In this article, a defect extraction method based on mathematical morphology is proposed for X-ray detection of hubs, where a larger square structuring element and a small threshold are used firstly to obtain all potential defect areas of the hub, and then a new threshold is then decided to get the final defect extraction results.
Abstract: A356 aluminum alloy is a material widely used in the production of automobile wheels. Internal defects such as gas holes and shrinkage cavities are likely to develop in the process of low pressure casting. X-ray images of the hub are able to provide some information on such defects. This paper proposes a defect extraction method which is built on mathematical morphology. It involves three operations, i.e., the top-hat transform, the top-hat reconstruction transform and the dilation reconstruction. A larger square structuring element and a small threshold are used firstly to obtain all potential defect areas of the hub. A structuring element of a suitable size are applied to different defect areas in subsequent extraction. A new threshold is then decided to get the final defect extraction results. The experimental results show that the above defect extraction method not only works on X-ray hub images, but is robust against the interference caused by noises and hub geometry, and hence can potentially be extensively applied to X-ray detection of hubs.
3 citations
17 Jun 2019
TL;DR: It is found that the spatial resolution and noise of the filtered images were influenced by the size of the Wiener filter kernel, threshold of edge detection, and size of structuring element.
Abstract: The aims of this study were to investigate the noise reduction in a CT image using a modified Wiener filtering-edge detection method. We modified the noise reduction algorithm of a combination of the Wiener filter and edge detection by addition of a dilation stage after edge detection. We then evaluated kernel size of the Wiener filter, threshold values in the edge detection, and size of structuring elements in the dilation process. Images of adult anthropomorphic and self-built wire phantoms were acquired by the new 4-row multislice CT Toshiba Alexion™. The images of the anthropomorphic phantom were used for a visual evaluation, while the images of the wire-phantom were used to obtain the spatial resolution and noise of the images. A Wiener filter-edge detection filter coupled with dilation, potentially reduced more CT noise. We found that the spatial resolution and noise of the filtered images were influenced by the size of the Wiener filter kernel, threshold of edge detection, and size of structuring element.
3 citations
01 Jan 2007
TL;DR: A novel method is introduced that integrates the knowledge of a face detector inside the shape and the appearance models by using a ’virtual structuring element’ (VSE) that provides increased performance in both accuracy and robustness over standard active appearance models applied to different environments.
Abstract: Face analysis in a real-world environment is a complex task as it should deal with challenging problems such as pose variations, illumination changes, and complex backgrounds. The use of active appearance models for facial feature detection is often successful in restricted environments, but the performance decreases when applied in unconstrained environments. Therefore, in this paper, we introduce a novel method that integrates the knowledge of a face detector inside the shape and the appearance models by using what we call a ’virtual structuring element’ (VSE). In this way, the possible settings of the active appearance models are constrained in an appearance-driven manner. The use of a virtual structuring element in an active appearance model provides increased performance in both accuracy and robustness over standard active appearance models applied to different environments.
3 citations
01 Dec 2016
TL;DR: Experiments show that these operators indeed approximate continuous-domain operators better than the standard operators on sampled one-dimensional signals, and that they may be applied to signals using structuring elements smaller than the distance between samples.
Abstract: We present a new approach to approximate continuous-domain mathematical morphology operators. The approach is applicable to irregularly sampled signals. We define a dilation under this new approach, where samples are duplicated and shifted according to the flat, continuous structuring element. We define the erosion by adjunction, and the opening and closing by composition. These new operators will significantly increase precision in image measurements. Experiments show that these operators indeed approximate continuous-domain operators better than the standard operators on sampled one-dimensional signals, and that they may be applied to signals using structuring elements smaller than the distance between samples. We also show that we can apply the operators to scan lines of a two-dimensional image to filter horizontal and vertical linear structures.
3 citations
01 Jan 2014
TL;DR: The novel approach is used to find the SE from the image itself using freeman chain code, which is then followed by the Morphological Gradient method to detect edges and an experimental result shows all the prominent edges efficiently.
Abstract: Edges are regions of interest where there is a sudden change in intensity. These features play an important role in object identification methods commonly used for many applications in computer vision and pattern recognition. This paper presents methods for edge detection using morphological operator. Morphological edge detector heavily relies on the choice of the structuring element (SE) and the results will vary from one SE to other. Therefore, the novel approach is used to find the SE from the image itself using freeman chain code, which is then followed by the Morphological Gradient method to detect edges. The proposed method is very simple, efficient and fast. An experimental result on various images shows all the prominent edges efficiently.
3 citations