Superpixel guided structure sparsity for multispectral and hyperspectral image fusion over couple dictionary

Zhang, Feng; Zhang, Kai

doi:10.1007/s11042-019-7188-1

Cited by 15 publications

(8 citation statements)

References 32 publications

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“…Meanwhile, the permutation of the EEG signals will be taken into consideration. It is notable that the proposed approach relates to the following areas including network security [44]- [58], optimization [59]- [65], graphics [66]- [71], recommendation system [72]- [74], multimedia [75]- [82], automation control [83]- [87], networking [88]- [101], classification [102]- [106], image fusion [107], [108], image retrieval [109]- [114], and simulation [115]- [117]. WEIKUAN JIA is currently an Associate Professor with Shandong Normal University.…”

Section: Discussionmentioning

confidence: 99%

Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network

et al. 2020

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Electroencephalogram (EEG) have been extensively analyzed to identify the characteristics of epileptic seizures in the literature. However, most of these studies focus on the properties of single channel EEG data while neglecting the association between signals from diverse channels. To bridge this gap, we propose an EEG instance matching-based epilepsy classification approach by introducing one convolutional neural network (CNN). First of all, each pair of EEG signals are exploited to form one 2 dimensional matrix, which could be used to reveal the interaction between them. Secondly, the generated matrices are fed into the proposed CNN that would discriminate the input representations. To evaluate the performance of the presented approach, the comparison experiments between the state-of-the-art techniques and our work are conducted on publicly available epilepsy EEG benchmark database. Experimental results indicate that the proposed algorithm could yield the performance with an average accuracy of 99.3%, average sensitivity of 99.5%, and average specificity 99.6%.

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Section: Discussionmentioning

confidence: 99%

Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network

et al. 2020

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“…Therefore, we take the lead in proposing a clustering algorithm based on topological characteristics, We call it Density Peak Clustering Algorithm Considering Topological Features (DPCTF). As an important feature of graphs, topological features play an important role in image processing; for example, topologies are often introduced when drawing satellite maps [36][37][38][39][40][41][42][43]. However, in the field of clustering research, topological features are rarely applied.…”

Section: Density Peak Clustering Algorithm Considering Topological Fementioning

confidence: 99%

Density Peak Clustering Algorithm Considering Topological Features

Zheng

Luo

et al. 2020

Electronics

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The clustering algorithm plays an important role in data mining and image processing. The breakthrough of algorithm precision and method directly affects the direction and progress of the following research. At present, types of clustering algorithms are mainly divided into hierarchical, density-based, grid-based and model-based ones. This paper mainly studies the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm, which is a new clustering method based on density. The algorithm has the characteristics of no iterative process, few parameters and high precision. However, we found that the clustering algorithm did not consider the original topological characteristics of the data. We also found that the clustering data is similar to the social network nodes mentioned in DeepWalk, which satisfied power-law distribution. In this study, we tried to consider the topological characteristics of the graph in the clustering algorithm. Based on previous studies, we propose a clustering algorithm that adds the topological characteristics of original data on the basis of the CFSFDP algorithm. Our experimental results show that the clustering algorithm with topological features significantly improves the clustering effect and proves that the addition of topological features is effective and feasible.Electronics 2020, 9, 459 2 of 16The K-means algorithm is an algorithm that adopts the alternative minimization method to solve non-convex optimization problems [11,12] and it is a representative of the prototype-based clustering method of objective functions. It divides a given data set into K clusters designated by users and has a high execution efficiency. However, the K-means algorithm needs to specify the number of clustering centers in advance. Different K values have a great impact on the experimental results and, in the actual operation, multiple attempts are required to obtain the optimal K value. Firstly, the objectivity of the K value makes the clustering results mostly different. Secondly, this algorithm is not friendly to non-convex data sets and it is difficult to obtain satisfactory clustering results.The AP clustering algorithm [13,14] is an unsupervised clustering algorithm. The algorithm realizes the clustering process through the interaction between data points. The advantages of the algorithm are that there is no need to define the number of classes in advance, the clustering center can be obtained adaptively through the iterative process, and the location and number of class centers can be identified automatically from the data points, so as to maximize the similarity sum of all similar points. At the same time, the algorithm has disadvantages. Since the AP algorithm needs to calculate the similarity between each pair of data objects in advance, when there are many data objects, the memory footprint will be high. Moreover, its high time complexity O(N3) is one of its shortcomings.The DBSCAN algorithm [15,16] is a clustering algorithm based on data point density. It gets rid of the constraint o...

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“…The target fruit contour is fitted by the center position and the maximum radius, as shown in figure 7, and finally the target fruit is accurately identified and positioned. It is notable that, the proposed method relates to the following areas including graphics [41], [42], [44]- [47], multimedia [40], [48]- [54], automation control [55]- [58], classification [59]- [62], image fusion [63], [64], image retrieval [65]- [70], and other fruit recognition [71]- [75].…”

Section: Target Area Identificationmentioning

confidence: 99%

Fast Recognition and Location of Target Fruit Based on Depth Information

Duan

Luo

et al. 2019

IEEE Access

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In order to realize the recognition and localization of the target fruit more accurately and efficiently, an optimized graph-based recognition algorithm based on image depth information is proposed. Firstly, the gradient information of the depth image is obtained from the acquired depth apple image, the gradient vector is reflected from the 3D space to the 2D space, and then rotated 90 degrees clockwise, and the regular region forms the center of the vortex, which is the target fruit center to determine target fruit location. In the RGB space, the graph-based segmentation algorithm is introduced. By constructing the adaptive threshold, the mapping relationship between the number of centers and the parameters of k and min is established to find the target fruit region. Finally, scan the maximum radius of the super pixel area where the center of the circle is located, and fit the target fruit to realize the recognition and localization of the apple. The experimental results show that the target recognition rate is 96.61%, and the recognition time has been reduced significantly.

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Superpixel guided structure sparsity for multispectral and hyperspectral image fusion over couple dictionary

Cited by 15 publications

References 32 publications

Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network

Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network

Density Peak Clustering Algorithm Considering Topological Features

Fast Recognition and Location of Target Fruit Based on Depth Information

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