Game Theory based Framework for Synthetic Aperture Radar Image De-noising and Change Detection

Bingquan Huo,Fengling Yin 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.4

In this paper, we propose a novel game theory based framework for synthetic aperture radar image de-noising and segmentation based change detection. We find out the balance of the two aspects. The Nash game theory helps us find out the balance of segmentation accuracy and overall de-noising performance. In the de-noising part, we adopt the multi-diagonal matrix filter based algorithm to undertake the de-noising mission. Segmentation and change detection are finalized by the state-of-the-art methodologies in which the segmentation procedure transfers the difference map into the change map. As far as time-consuming is concerned, we compare the different methods for generating difference map. Fusion map is selected to be our difference map for image segmentation using fuzzy clustering. The experimental analysis shows the effectiveness and robustness of our propose framework with the comparison of other well-known change detection algorithms under the outer environment of noisy and noise-free. Finally, some potential optimization methods are discussed for future research.

Image Dehazing with Dark Channel Prior and Novel Estimation Model

Bingquan Huo,Fengling Yin 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.3

Single Image Dehazing technology is widely needed in many fields. In order to solve the problem, we propose an improved and modified framework for estimating the optical transmission t in hazy scenes in a given single input image. At first, a novel formulation to the t estimation is presented with the combination of constant albedo and dark channel prior knowledge. Later, we introduce the watershed segmentation methodology into the algorithm to separate the image into some gray level consistent parts based on the original image’s color distribution and feature difference. As a result, we could estimate the atmospheric light A better and avoid the important drawback of artifacts phenomenon. At last, through this effective estimation to t and A, the scene visibility is largely increased and the haze-free scene contrasts can be better recovered. The experimental analysis shows that compared with other state-of-the-art algorithms, our proposed algorithm can provide promising results to dark channel prior and get corresponding reliable estimation value t with the advantage of minimal halo artifacts and fewer unreal details. Our method is more effective and robust.

Medical and Natural Image Segmentation Algorithm using M-F based Optimization Model and Modified Fuzzy Clustering : A Novel Approach

Bingquan Huo,Guoxin Li,Fengling Yin 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.7

In this paper, we propose and present a novel algorithm for medical image segmentation (MIS). By analyzing the current state-of-the-art related algorithms, we introduce the multi-band active contour model based limit function to make the multilayer segmentation available. With the development of image segmentation technology, the development of medical image segmentation technology also got very big, because there is no find common, accepted effect ideal is suitable for medical image segmentation method, almost existing each kind of segmentation method has application in the field of medical image segmentation. Furtherly, with the optimized aims of being robust to the noise and avoiding the bad effluence on the result, we adopt the kernel method and new initialization curve. This model suffers from low noise robustness, and model algorithm is difficult to achieve. Integrated segmentation technology refers to two or more technology is used, combined with their own advantages, so they can on the accuracy or efficiency to achieve better performance than when using a single. A new penalty term is introduced to improve numerical stability and the step length is increased to improve efficiency. As far as the robustness and effectiveness are concerned, our method is better than the existing medical image segmentation algorithms. Experimental analysis verifies the success of our method.

Image Segmentation Using Mean Shift Based Fuzzy C-Means Clustering Algorithm : A Novel Approach

Bingquan Huo,Fengling Yin 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.5

With the fast development of image processing technique, segmentation related issues have gained special attention in the research community. In this paper, an improved FCM combining mean shift algorithm is proposed to improve the segmentation visual effects and efficiency of traditional FCM. Initially, image segmentation into many small homogeneous area using the mean shift algorithm is conducted, segmentation and uniform area, rather than pixels as the new node. Then, image local entropy is adopted to describe the new nodes spatial and gray feature. Finally, an exponential function which is able to well simulate human nonlinear visual reaction was used to measure the similarity between the new node and the cluster center node. The experimental result shows the effectiveness and robustness of our proposed FCM, further potential research is also discussed.

Research on Novel Image Classification Algorithm based on Multi-Feature Extraction and Modified SVM Classifier

Bingquan Huo,Fengling Yin 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.9

In this research paper, we conduct theoretical analysis and numerical analysis on novel image classification algorithm based on multi-feature extraction and modified SVM classifier. Image object classification and detection are two important basic problems in the study of computer vision, image segmentation, object tracking, behavior analysis and so on the basis of other high-level vision tasks. Existing image classification method can make full use of every single feature between the complementary characteristics of the extracted features of a large number of redundant information, which can lead to image classification accuracy is not high. For this, put forward an improved support vector machine (SVM) based on characteristics and integrated method of image classification. This method can extract comprehensive description of image content features, using principal component analysis to extract the characteristics of transformation, remove redundant information. The experimental result proves the effectiveness and feasibility of the proposed algorithm. In the final part, we conclude the paper and set up the prospect for the future research.

Mobile Social Helping Platform of LBS

Chen Yuefeng,Li Bingquan,Gao sheng,Peng Linxi 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.1

Mobile Internet grows with demands of location information of mobile users . In order to overcome the limitation of sociality, we develop Mobile social helping platform (MSH) of LBS, which realize the sharing of sociality. First, we analyze the requirement of MSHP platform, then design its architecture and database, and finally discuss the key technologies of MSHP. The platform makes use of open source technology, implements and optimizes LBS service. The simulation results show the system has good portability and maintainability, which is easy to be commercialized.

Incorporating Topic Priors into Distributed Word Representations

Xin Zhang,Bingquan Liu,Baoxun Wang,Xiaolong Wang,Deyuan Zhang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.7

Representing words as continuous vectors enables the quantification of semantic relationships of words by vector operations, thereby has attracted much attention recently. This paper proposes an approach to combine continuous word representation and topic modeling, by encoding words based on their topic distributions in the hierarchical softmax, so as to introduce the prior semantic relevance information into the neural networks. The word vectors generated by our model are evaluated with respect to word relevance and the document relevance. Experimental results show that our approach is promising for further improving the quality of word vectors.

Research on Novel Single Image Super-resolution Algorithm through Regularization Approach and Joint Learning Theory : Theoretical Analysis and Applications

Fengling Yin,Bingquan Huo 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.6

In this research paper, theoretical analysis and applications of a novel single image super-resolution algorithm through regularization approach and joint learning is introduced. Digital image during the process of obtaining the optical fuzzy, movement deformation and degradation factors such as random noise, the influence of the resulting often degradation image, sometimes its resolution is difficult to meet the actual demand of engineering or military applications. In this paper, we combine the joint learning theory together with the regularization standard, through parameter selection, error estimation with omission and solution analysis steps. The proposed framework is based on modified super-resolution model and novel error estimation metrics. In the experiment section, we compare our proposed algorithm with other state-of-the-art and popularly adopted methodologies and use the well-known test image databases to conduct the experiment. The experimental result shows the feasibility and effectiveness of the algorithm. In the future, we plan to do more in-depth research on the parameter selection part to modify our method.

Research on Robust Adaptive and Efficient Control System : A Theoretical Approach

Fengling Yin,Bingquan Huo 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.6

In this paper, a robust adaptive repetitive control algorithm is presented for periodically time-varying systems. Periodic time-varying parameter estimation through periodic learning algorithm, and the uncertainty of aperiodic was robust adaptive method. Different from the existing repetitive control, this paper introduces the design of a new variable cycle number control. Convergence error when the number increase will gradually decrease due to the cyclical repetition character system, in order to ensure the global asymptotic stability. Further, this method is applied to a class of nonlinearly parameterized systems with non-parametric disturbances, and the tracking error converges asymptotically. The results verify the simulation model of the inverse pendulum. In addition, it is proved that the proposed design method is applied to eliminate the influence of approximation error of neural network. Theoretical analysis shows that the system output is convergent to the desired one and all signals in the network based robust adaptive repetitive control system are bounded. The experimental result illustrates the effectiveness of our proposed methodology.

Anti-Inflammatory Activity of Epimedium brevicornu Maxim Ethanol Extract

Shan Huang,Ning Meng,Bingquan Chang,Xianghua Quan,RuiYing Yuan,Bin Li 한국식품영양과학회 2018 Journal of medicinal food Vol.21 No.7

Epimedium brevicornu Maxim has been used as a traditional herbal drug in China. In this study, the anti-inflammatory effects of E. brevicornu Maxim ethanol extract (EBME) were investigated in RAW264.7 macrophages and mice challenged with lipopolysaccharide (LPS). Results showed that EBME attenuated inflammation by decreasing the production of several proinflammatory mediators, such as nitric oxide (NO), prostaglandin (PG) E2, inducible nitric oxide synthase, and cyclooxygenase-2, in LPS-stimulated RAW264.7 macrophages. EBME increased the expression of heme oxygenase-1 (HO-1) and promoted the nuclear translocation of nuclear factor erythroid 2-related factor 2. The inhibitory effects of EBME on LPS-stimulated NO and PGE2 expression were partially reversed by HO-1 inhibitor. EBME also elicited an anti-inflammatory effect by inhibiting the production of tumor necrosis factor-α, interleukin (IL)-1β, and IL-6 in LPS-induced peritonitis. Therefore, EBME exhibited anti-inflammatory effects in vitro and in vivo.