hybrid fusion
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Author(s):  
Anchal Kumawat ◽  
Sucheta Panda

Often in practice, during the process of image acquisition, the acquired image gets degraded due to various factors like noise, motion blur, mis-focus of a camera, atmospheric turbulence, etc. resulting in the image unsuitable for further analysis or processing. To improve the quality of these degraded images, a double hybrid restoration filter is proposed on the two same sets of input images and the output images are fused to get a unified filter in combination with the concept of image fusion. First image set is processed by applying deconvolution using Wiener Filter (DWF) twice and decomposing the output image using Discrete Wavelet Transform (DWT). Similarly, second image set is also processed simultaneously by applying Deconvolution using Lucy–Richardson Filter (DLR) twice followed by the above procedure. The proposed filter gives a better performance as compared to DWF and DLR filters in case of both blurry as well as noisy images. The proposed filter is compared with some standard deconvolution algorithms and also some state-of-the-art restoration filters with the help of seven image quality assessment parameters. Simulation results prove the success of the proposed algorithm and at the same time, visual and quantitative results are very impressive.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 74
Author(s):  
Sun Zhang ◽  
Bo Li ◽  
Chunyong Yin

The rising use of online media has changed the social customs of the public. Users have become accustomed to sharing daily experiences and publishing personal opinions on social networks. Social data carrying emotion and attitude has provided significant decision support for numerous tasks in sentiment analysis. Conventional methods for sentiment classification only concern textual modality and are vulnerable to the multimodal scenario, while common multimodal approaches only focus on the interactive relationship among modalities without considering unique intra-modal information. A hybrid fusion network is proposed in this paper to capture both inter-modal and intra-modal features. Firstly, in the stage of representation fusion, a multi-head visual attention is proposed to extract accurate semantic and sentimental information from textual contents, with the guidance of visual features. Then, multiple base classifiers are trained to learn independent and diverse discriminative information from different modal representations in the stage of decision fusion. The final decision is determined based on fusing the decision supports from base classifiers via a decision fusion method. To improve the generalization of our hybrid fusion network, a similarity loss is employed to inject decision diversity into the whole model. Empiric results on five multimodal datasets have demonstrated that the proposed model achieves higher accuracy and better generalization capacity for multimodal sentiment analysis.


2021 ◽  
pp. 1-16
Author(s):  
R. Sindhiya Devi ◽  
B. Perumal ◽  
M. Pallikonda Rajasekaran

In today’s world, Brain Tumor diagnosis plays a significant role in the field of Oncology. The earlier identification of brain tumors increases the compatibility of treatment of patients and offers an efficient diagnostic recommendation from medical practitioners. Nevertheless, accurate segmentation and feature extraction are the vital challenges in brain tumor diagnosis where the handling of higher resolution images increases the processing time of existing classifiers. In this paper, a new robust weighted hybrid fusion classifier has been proposed to identify and classify the tumefaction in the brain which is of the hybridized form of SVM, NB, and KNN (SNK) classifiers. Primarily, the proposed methodology initiates the preprocessing technique such as adaptive fuzzy filtration and skull stripping in order to remove the noises as well as unwanted regions. Subsequently, an automated hybrid segmentation strategy can be carried out to acquire the initial segmentation results, and then their outcomes are compiled together using fusion rules to accurately localize the tumor region. Finally, a Hybrid SNK classifier is implemented in the proposed methodology for categorizing the type of tumefaction in the brain. The hybrid classifier has been compared with the existing state-of-the-art classifier which shows a higher accuracy result of 99.18% while distinguishing the benign and malignant tumors from brain Magnetic Resonance (MR) images.


Author(s):  
Yashowardhan Shinde ◽  
Akalbir Singh Chadha ◽  
Ajitkumar Shitole
Keyword(s):  

2021 ◽  
Vol 10 (22) ◽  
pp. 5315
Author(s):  
Takashi Hirai ◽  
Toshitaka Yoshii ◽  
Kenichiro Sakai ◽  
Hiroyuki Inose ◽  
Masato Yuasa ◽  
...  

Various studies have found a high incidence of early graft dislodgement after multilevel corpectomy. Although a hybrid fusion technique was developed to resolve implant failure, the hybrid and conventional techniques have not been clearly compared in terms of perioperative complications in patients with severe ossification of the posterior longitudinal ligament (OPLL) involving three or more levels. The purpose of this study was to compare clinical and radiologic outcomes between anterior cervical corpectomy with fusion (ACCF) and anterior hybrid fusion for the treatment of multilevel cervical OPLL. We therefore retrospectively reviewed the clinical and radiologic data of 53 consecutive patients who underwent anterior fusion to treat cervical OPLL: 30 underwent ACCF and 23 underwent anterior hybrid fusion. All patients completed 2 years of follow-ups. Implant migration was defined as subsidence > 3 mm. There were no significant differences in demographics or clinical characteristics between the ACCF and hybrid groups. Early implant failure occurred significantly more frequently in the ACCF group (5 cases, 16.7%) compared with the hybrid group (0 cases, 0%). The fusion rate was 80% in the ACCF group and 100% in the hybrid group. Although both procedures can achieve satisfactory neurologic outcomes for multilevel OPLL patients, hybrid fusion likely provides better biomechanical stability than the conventional ACCF technique.


Author(s):  
Maarten Manse

Abstract This article investigates Dutch colonial practices on the Moluccan island of Seram in the late nineteenth and early twentieth centuries. Seram’s mountainous interior was the domain of ungoverned, peripatetic Alfurs who engaged in headhunting. For a long time, they were rendered untouched by colonialism and administered through coastal intermediaries. After 1900, renewed imperial-civilizational vigour demanded the direct incorporation and ‘civilization’ of Seram’s stateless spaces. A series of expeditions subjected the Alfurs to registration, categorization, and taxation, which this article argues were seen as pivotal, moralizing tools of colonial social-engineering, used to inscribe subjected people into the state and instil compliant and ‘productive’ behaviour. However, rather than a replacement of indigenous orders with European modernity, colonization produced a hybrid fusion of colonial strategies of domination with indigenous cultural practices of state-evasion. This article demonstrates that colonial governance was a site of interaction, in which colonial developmentalism and modernity were actively negotiated and challenged.


2021 ◽  
pp. 101121
Author(s):  
Xinwei Huang ◽  
Nannan Li ◽  
Qing Xia ◽  
Shuai Li ◽  
Aimin Hao ◽  
...  

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