Efficient image fusion with approximate sparse representation

2016 ◽  
Vol 14 (04) ◽  
pp. 1650024 ◽  
Author(s):  
Yang Bin ◽  
Yang Chao ◽  
Huang Guoyu
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
State Of The Art ◽  
Experimental Results ◽  
Computational Time ◽  
Selection Strategy ◽  
Image Patches

In this paper, an efficient approximate sparse representation (SR) algorithm with multi-selection strategy is used to solve the image fusion problem. We have shown that the approximate SR is effective for image fusion even if the sparse coefficients are not the sparsest ones possible. A multi-selection strategy is used to accelerate the process of generating the approximate sparse coefficients which are used to guide the fusion of image patches. The relative parameters are also investigated experimentally to further reduce the computational time. The proposed method is compared with some state-of-the-art image fusion approaches on several pairs of multi-source images. The experimental results exhibit that the proposed method is able to yield superior fusion results with less consumption time.

scholarly journals Visual Tracking Using Max-Average Pooling and Weight-Selection Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Suguo Zhu ◽  
Junping Du
Keyword(s):  
Particle Filter ◽  
Sparse Representation ◽  
Visual Tracking ◽  
Spatial Information ◽  
State Of The Art ◽  
Experimental Results ◽  
Selection Strategy ◽  
Illumination Change ◽  
Spatial Pooling ◽  
Hybrid Framework

Many modern visual tracking algorithms incorporate spatial pooling, max pooling, or average pooling, which is to achieve invariance to feature transformations and better robustness to occlusion, illumination change, and position variation. In this paper, max-average pooling method and Weight-selection strategy are proposed with a hybrid framework, which is combined with sparse representation and particle filter, to exploit the spatial information of an object and make good compromises to ensure the correctness of the results in this framework. Challenges can be well considered by the proposed algorithm. Experimental results demonstrate the effectiveness and robustness of the proposed algorithm compared with the state-of-the-art methods on challenging sequences.

scholarly journals Loss-based active learning via double-branch deep network

2021 ◽  
Vol 18 (5) ◽  
pp. 172988142110449
Author(s):  
Qiang Fang ◽  
Xin Xu ◽  
Dengqing Tang
Keyword(s):  
Active Learning ◽  
Visual Recognition ◽  
State Of The Art ◽  
Experimental Results ◽  
Research Interest ◽  
Selection Strategy ◽  
Network Architectures ◽  
Data Annotation ◽  
Deep Network ◽  
Effectiveness And Efficiency

Due to the limitation of data annotation and the ability of dealing with label-efficient problems, active learning has received lots of research interest in recent years. Most of the existing approaches focus on designing a different selection strategy to achieve better performance for special tasks; however, the performance of the strategy still needs to be improved. In this work, we focus on improving the performance of active learning and propose a loss-based strategy that learns to predict target losses of unlabeled inputs to select the most uncertain samples, which is designed to learn a better selection strategy based on a double-branch deep network. Experimental results on two visual recognition tasks show that our approach achieves the state-of-the-art performance compared with previous methods. Moreover, our approach is also robust to different network architectures, biased initial labels, noisy oracles, or sampling budget sizes, and the complexity is also competitive, which demonstrates the effectiveness and efficiency of our proposed approach.

Variable Scale Compressive Tracking Based on Structural Constraint Sample

2014 ◽  
Vol 488-489 ◽  
pp. 1074-1078
Author(s):  
Lu Ping Zhang ◽  
Meng Cai ◽  
Biao Li ◽  
Lu Ping Wang
Keyword(s):  
State Of The Art ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Structural Constraint ◽  
Scale Problem ◽  
Scale Selection ◽  
Compressive Tracking ◽  
Image Patches ◽  
Basic Attitude ◽  
Selection Of

A variable scale compressive tracking algorithm based on structural constraint sample is presented to solve the variable scale problem in this paper. A number of scanning windows with different scales and positions are obtained by structural constraint sampling.Some sparse random sensing matrices with different scales that can be computed offline easily are adopted to extract the features of different foreground target and background sample image patches with relevant scales online, the sample patch having a maximal score is regarded as the new tracking result by classifying the compressive features via a naive bayesian classifier,meanwhile,to update the location and scale. Experimental results show the proposed algorithm performs favorably against state-of-the-art algorithms on challenging sequences in terms of the basic attitude and scale change, which is robust and does not depend on the scale selection of the initial tracking area.

Image Fusion Method Based on Multi-scale Directional Fast Guided Filter and Convolutional Sparse Representation

2019 ◽  
Vol 12 (3) ◽  
pp. 263-269
Author(s):  
Liu Xian-Hong ◽  
Chen Zhi-Bin
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Filter Bank ◽  
Guided Filter ◽  
Fusion Method ◽  
Edge Preserving ◽  
Directional Information ◽  
Multi Scale ◽  
Directional Filter ◽  
Image Fusion Method

Background: A multi-scale multidirectional image fusion method is proposed, which introduces the Nonsubsampled Directional Filter Bank (NSDFB) into the multi-scale edge-preserving decomposition based on the fast guided filter. Methods: The proposed method has the advantages of preserving edges and extracting directional information simultaneously. In order to get better-fused sub-bands coefficients, a Convolutional Sparse Representation (CSR) based approximation sub-bands fusion rule is introduced and a Pulse Coupled Neural Network (PCNN) based detail sub-bands fusion strategy with New Sum of Modified Laplacian (NSML) to be the external input is also presented simultaneously. Results: Experimental results have demonstrated the superiority of the proposed method over conventional methods in terms of visual effects and objective evaluations. Conclusion: In this paper, combining fast guided filter and nonsubsampled directional filter bank, a multi-scale directional edge-preserving filter image fusion method is proposed. The proposed method has the features of edge-preserving and extracting directional information.

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

scholarly journals Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

2019 ◽  
Vol 11 (16) ◽  
pp. 1933 ◽  
Author(s):  
Yangyang Li ◽  
Ruoting Xing ◽  
Licheng Jiao ◽  
Yanqiao Chen ◽  
Yingte Chai ◽  
...  
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Sample Selection ◽  
Spatial Relations ◽  
Speckle Noise ◽  
Classification Performance ◽  
Selection Strategy ◽  
Training Set ◽  
Polarimetric Synthetic Aperture Radar ◽  
Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

Multi-modality image fusion combining sparse representation with guidance filtering

2021 ◽  
Vol 25 (6) ◽  
pp. 4393-4407
Author(s):  
Qiu Hu ◽  
Shaohai Hu ◽  
Fengzhen Zhang
Keyword(s):  
Image Fusion ◽  
Sparse Representation

scholarly journals Automatic Detection of Discrimination Actions from Social Images

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 325
Author(s):  
Zhihao Wu ◽  
Baopeng Zhang ◽  
Tianchen Zhou ◽  
Yan Li ◽  
Jianping Fan
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Automatic Detection ◽  
Experimental Results ◽  
Practical Approach ◽  
Detection And Identification ◽  
Art Methods ◽  
Image Set ◽  
Social Images ◽  
Relationship Identification

In this paper, we developed a practical approach for automatic detection of discrimination actions from social images. Firstly, an image set is established, in which various discrimination actions and relations are manually labeled. To the best of our knowledge, this is the first work to create a dataset for discrimination action recognition and relationship identification. Secondly, a practical approach is developed to achieve automatic detection and identification of discrimination actions and relationships from social images. Thirdly, the task of relationship identification is seamlessly integrated with the task of discrimination action recognition into one single network called the Co-operative Visual Translation Embedding++ network (CVTransE++). We also compared our proposed method with numerous state-of-the-art methods, and our experimental results demonstrated that our proposed methods can significantly outperform state-of-the-art approaches.

scholarly journals A Termination Criterion for Probabilistic Point Clouds Registration

Signals ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 159-173
Author(s):  
Simone Fontana ◽  
Domenico Giorgio Sorrenti
Keyword(s):  
State Of The Art ◽  
Point Clouds ◽  
Computational Time ◽  
Termination Criteria ◽  
Practical Usefulness ◽  
Local Point ◽  
Termination Criterion ◽  
Probabilistic Point ◽  
Excessive Number ◽  
Number Of Iterations

Probabilistic Point Clouds Registration (PPCR) is an algorithm that, in its multi-iteration version, outperformed state-of-the-art algorithms for local point clouds registration. However, its performances have been tested using a fixed high number of iterations. To be of practical usefulness, we think that the algorithm should decide by itself when to stop, on one hand to avoid an excessive number of iterations and waste computational time, on the other to avoid getting a sub-optimal registration. With this work, we compare different termination criteria on several datasets, and prove that the chosen one produces very good results that are comparable to those obtained using a very large number of iterations, while saving computational time.

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