Sequential sparse representation for mitotic event recognition

2013 ◽  
Vol 49 (14) ◽  
pp. 869-871 ◽  
Author(s):  
A.A. Liu ◽  
T. Hao ◽  
Z. Gao ◽  
Y.T. Su ◽  
Z.X. Yang
Keyword(s):  
Sparse Representation ◽  
Event Recognition ◽  
Mitotic Event

scholarly journals “Pattern Recognition” special issue: Sparse representation for event recognition in video surveillance

2013 ◽  
Vol 46 (7) ◽  
pp. 1748-1749 ◽  
Author(s):  
Huiyu Zhou ◽  
Jianguo Zhang ◽  
Liang Wang ◽  
Zhengyou Zhang ◽  
Lisa M. Brown
Keyword(s):  
Pattern Recognition ◽  
Sparse Representation ◽  
Video Surveillance ◽  
Event Recognition ◽  
Special Issue

Confidence Inflation and Event Recognition With Young and Older Adult Eyewitnesses

2004 ◽  
Author(s):  
Jeffrey S. Neuschatz ◽  
Michael P. Toglia ◽  
Elizabeth L. Preston ◽  
James M. Lampinen ◽  
Joseph S. Neuschatz ◽  
...  
Keyword(s):  
Older Adult ◽  
Event Recognition

Prediction method for outliers over data stream based on sparse representation

2010 ◽  
Vol 30 (11) ◽  
pp. 2956-2958
Author(s):  
Xue-song XU ◽  
Ling-juan LI ◽  
Li-wei GUO
Keyword(s):  
Sparse Representation ◽  
Data Stream ◽  
Prediction Method

Ultrasonic Block Compressed Sensing Imaging Reconstruction Algorithm Based on Wavelet Sparse Representation

2020 ◽  
Vol 16 (3) ◽  
pp. 262-272
Author(s):  
Guangzhi Dai ◽  
Zhiyong He ◽  
Hongwei Sun
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Linear Array ◽  
Imaging System ◽  
Ultrasonic Imaging ◽  
Large Data ◽  
Reconstruction Algorithm ◽  
Array Transducer ◽  
Total Data ◽  
Block Compressed Sensing

Background: This study is carried out targeting the problem of slow response time and performance degradation of imaging system caused by large data of medical ultrasonic imaging. In view of the advantages of CS, it is applied to medical ultrasonic imaging to solve the above problems. Objective: Under the condition of satisfying the speed of ultrasound imaging, the quality of imaging can be further improved to provide the basis for accurate medical diagnosis. Methods: According to CS theory and the characteristics of the array ultrasonic imaging system, block compressed sensing ultrasonic imaging algorithm is proposed based on wavelet sparse representation. Results: Three kinds of observation matrices have been designed on the basis of the proposed algorithm, which can be selected to reduce the number of the linear array channels and the complexity of the ultrasonic imaging system to some extent. Conclusion: The corresponding simulation program is designed, and the result shows that this algorithm can greatly reduce the total data amount required by imaging and the number of data channels required for linear array transducer to receive data. The imaging effect has been greatly improved compared with that of the spatial frequency domain sparse algorithm.

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