Topic
Opening
About: Opening is a research topic. Over the lifetime, 163 publications have been published within this topic receiving 11246 citations.
Papers published on a yearly basis
Papers
Book•
11 Feb 1984
TL;DR: This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
Abstract: Image Processing and Mathematical Morphology-Frank Y. Shih 2009-03-23 In the development of digital multimedia, the importance and impact of image processing and mathematical morphology are well documented in areas ranging from automated vision detection and inspection to object recognition, image analysis and pattern recognition. Those working in these ever-evolving fields require a solid grasp of basic fundamentals, theory, and related applications—and few books can provide the unique tools for learning contained in this text. Image Processing and Mathematical Morphology: Fundamentals and Applications is a comprehensive, wide-ranging overview of morphological mechanisms and techniques and their relation to image processing. More than merely a tutorial on vital technical information, the book places this knowledge into a theoretical framework. This helps readers analyze key principles and architectures and then use the author’s novel ideas on implementation of advanced algorithms to formulate a practical and detailed plan to develop and foster their own ideas. The book: Presents the history and state-of-the-art techniques related to image morphological processing, with numerous practical examples Gives readers a clear tutorial on complex technology and other tools that rely on their intuition for a clear understanding of the subject Includes an updated bibliography and useful graphs and illustrations Examines several new algorithms in great detail so that readers can adapt them to derive their own solution approaches This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
10,162 citations
TL;DR: Experimental results show that the proposed multi-focus image fusion algorithm can not only extract more important detailed information from source images, but also avoid the introduction of artificial information effectively.
Abstract: In this study, a new multi-focus image fusion algorithm based on the non-subsampled shearlet transform (NSST) is presented. First, an initial fused image is acquired by using a conventional multi-resolution image fusion method. The pixels of those source multi-focus images, which have smaller square error with the corresponding pixels of the initial fused image, are considered in the focused regions. Based on this principle, the focused regions are determined, and the morphological opening and closing are employed for post-processing. Then the focused regions and the focused border regions in each source image are identified and used to guide the fusion process in NSST domain. Finally, the fused image is obtained using the inverse NSST. Experimental results show that this proposed method can not only extract more important detailed information from source images, but also avoid the introduction of artificial information effectively. It significantly outperforms the discrete wavelet transform (DWT)-based fusion method, the non-subsampled contourlet-transformbased fusion method and the NSST-based fusion method (see Miao et al. 2011) in terms of both visual quality and objective evaluation.
173 citations
TL;DR: A novel multilevel features convolutional neural network (MLFCNN) architecture for image fusion that outperforms some state-of-the-art image fusion algorithms in terms of both qualitative and objective evaluations is proposed.
Abstract: Multifocus image fusion is an important technique that aims to generate a single clean image by fusing multiple input images. In this paper, we propose a novel multilevel features convolutional neural network (MLFCNN) architecture for image fusion. In the MLFCNN model, all features learned from previous layers are passed to the subsequent layer. Inside every path between the previous layer and the subsequent layer, we add a 1 × 1 convolution module to reduce the redundancy. In our method, the source images first are fed to our pre-trained MLFCNN model to obtain the initial focus map. Then, the initial focus map is performed by morphological opening and closing operations and followed by a Gaussian filter to obtain the final decision map. Finally, the fused all-in-focus image is generated based on a weighted-sum strategy with the decision map. The experimental results demonstrate that the proposed method outperforms some state-of-the-art image fusion algorithms in terms of both qualitative and objective evaluations.
97 citations
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TL;DR: In this paper, the morphological opening operator is defined, which consists in dilating the image previously eroded using the same structuring element, and the dual operator of morphological closing is defined.
Abstract: The erosion of an image not only removes all structures that cannot contain the structuring element but it also shrinks all the other ones. The search for an operator recovering most structures lost by the erosion leads to the definition of the morphological opening operator. The principle consists in dilating the image previously eroded using the same structuring element. In general, not all structures are recovered. For example, objects completely destroyed by the erosion are not recovered at all. This behaviour is at the very basis of the filtering properties of the opening operator: image structures are selectively filtered out, the selection depending on the shape and size of the SE. The dual operator of the morphological opening is the morphological closing. Both operators are at the basis of the morphological approach to image filtering developed in Chap. 8.
91 citations
TL;DR: This paper uses the binary target region as the feature and proposes a matching scheme for the target regions using binary morphological operations and employs a Bayesian decision fusion to fuse the similarities gained by different structuring elements to further enhance the recognition performance.
Abstract: Feature extraction and matching are two important steps in synthetic aperture radar automatic target recognition. This paper uses the binary target region as the feature and proposes a matching scheme for the target regions using binary morphological operations. The residuals between the testing target region and its corresponding template target regions are processed by the morphological opening operation. Then, a similarity measure is defined based on the residual remains to evaluate the similarities between different targets. Afterward, a Bayesian decision fusion is employed to fuse the similarities gained by different structuring elements to further enhance the recognition performance. The nonlinearity of the opening operation as well as the Bayesian decision fusion makes the proposed method robust to the nonlinear deformations of the target region. Experimental results on the moving and stationary target acquisition and recognition dataset demonstrate the validity of the proposed method.
79 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 2 |
| 2020 | 5 |
| 2019 | 10 |
| 2018 | 10 |
| 2017 | 5 |
| 2016 | 5 |