An Adaptation Method for Morphological Opening Filters with a Smoothness Penalty on Structuring Elements

2013 ◽  
Vol E96.A (6) ◽  
pp. 1468-1477
Author(s):  
Makoto NAKASHIZUKA ◽  
Yu ASHIHARA ◽  
Youji IIGUNI
Keyword(s):  
Adaptation Method ◽  
Morphological Opening

Lokan?ti: Method of Adaption and New Vocabulary

2017 ◽  
Vol 34 (1) ◽  
pp. 85-114
Author(s):  
Ujjwal Kumar
Keyword(s):  
Fourteenth Century ◽  
Adaptation Method

In this paper I have made an attempt to discuss the adaptation method and new vocabulary employed and introduced by the Lokan?ti (Ln). This text was composed in Burma most probably by Catru?gabala around the fourteenth century CE. In premodern Burma Ln was used in monasteries to inculcate guidance on worldly affairs and everyday morality to the Burmese householders in general and to the Buddhist monks in particular.

scholarly journals Image Restoration by Learning Morphological Opening-Closing Network

2020 ◽  
Vol 4 (1) ◽  
pp. 87-107
Author(s):  
Ranjan Mondal ◽  
Moni Shankar Dey ◽  
Bhabatosh Chanda
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
State Of The Art ◽  
Source Code ◽  
Back Propagation ◽  
Image Features ◽  
Main Difficulty ◽  
The Right ◽  
Right Order ◽  
Morphological Opening

AbstractMathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing. Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net

scholarly journals Special Finite Elements with Adaptive Strain Field on the Example of One-Dimensional Elements

2021 ◽  
Vol 11 (2) ◽  
pp. 609
Author(s):  
Tadeusz Chyży ◽  
Monika Mackiewicz
Keyword(s):  
Finite Elements ◽  
Strain Field ◽  
Main Idea ◽  
Deformation Field ◽  
Geometrical Parameters ◽  
Single Element ◽  
One Dimensional ◽  
New Type ◽  
Self Adaptation ◽  
Adaptation Method

The conception of special finite elements called multi-area elements for the analysis of structures with different stiffness areas has been presented in the paper. A new type of finite element has been determined in order to perform analyses and calculations of heterogeneous, multi-coherent, and layered structures using fewer finite elements and it provides proper accuracy of the results. The main advantage of the presented special multi-area elements is the possibility that areas of the structure with different stiffness and geometrical parameters can be described by single element integrated in subdivisions (sub-areas). The formulation of such elements has been presented with the example of one-dimensional elements. The main idea of developed elements is the assumption that the deformation field inside the element is dependent on its geometry and stiffness distribution. The deformation field can be changed and adjusted during the calculation process that is why such elements can be treated as self-adaptive. The application of the self-adaptation method on strain field should simplify the analysis of complex non-linear problems and increase their accuracy. In order to confirm the correctness of the established assumptions, comparative analyses have been carried out and potential areas of application have been indicated.

An Event-Driven Spatiotemporal Domain Adaptation Method for DVS Gesture Recognition

2021 ◽  
pp. 1-1
Author(s):  
Yuhan Zhang ◽  
Lindong Wu ◽  
Weihua He ◽  
Ziyang Zhang ◽  
Chen Yang ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Domain Adaptation ◽  
Event Driven ◽  
Adaptation Method
Sign in / Sign up

Export Citation Format

Share Document