scholarly journals Continuous Frames and the Kadison-Singer Problem

Author(s):  
Marcin Bownik
Keyword(s):  
1959 ◽  
Vol 85 (9) ◽  
pp. 83-86
Author(s):  
Lawrence P. Johnson ◽  
Herbert A. Sawyer

1975 ◽  
Vol 101 (7) ◽  
pp. 1606-1608
Author(s):  
Gerald M. Smith ◽  
George C. Ernst ◽  
Mahendra Maheshwari
Keyword(s):  

1964 ◽  
Vol 90 (3) ◽  
pp. 39-52
Author(s):  
Donald A. Sawyer ◽  
Linton E. Grinter

2021 ◽  
Vol 309 ◽  
pp. 01117
Author(s):  
A. Sai Hanuman ◽  
G. Prasanna Kumar

Studies on lane detection Lane identification methods, integration, and evaluation strategies square measure all examined. The system integration approaches for building a lot of strong detection systems are then evaluated and analyzed, taking into account the inherent limits of camera-based lane detecting systems. Present deep learning approaches to lane detection are inherently CNN's semantic segmentation network the results of the segmentation of the roadways and the segmentation of the lane markers are fused using a fusion method. By manipulating a huge number of frames from a continuous driving environment, we examine lane detection, and we propose a hybrid deep architecture that combines the convolution neural network (CNN) and the continuous neural network (CNN) (RNN). Because of the extensive information background and the high cost of camera equipment, a substantial number of existing results concentrate on vision-based lane recognition systems. Extensive tests on two large-scale datasets show that the planned technique outperforms rivals' lane detection strategies, particularly in challenging settings. A CNN block in particular isolates information from each frame before sending the CNN choices of several continuous frames with time-series qualities to the RNN block for feature learning and lane prediction.


2012 ◽  
Vol 182-183 ◽  
pp. 1863-1867
Author(s):  
Wei Liu ◽  
Xue Jun Xu ◽  
Bi Tao Fu ◽  
Xi Zhu

This paper presents an improved method to detect moving object and obtain the relative accurate location. First we detect the edge difference of continuous frames. Then we utilize the contour matching to find the edge pairs in order to reach a good detection of the moving object and location. The extensive experiments show that our method is robust and efficient to the moving object detection.


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