Title :
Displacement vector estimation with cellular neural networks
Author :
Feiden, Dirk ; Tetzlaff, Ronald
Author_Institution :
Inst. of Appl. Phys., Frankfurt Univ., Germany
fDate :
6/24/1905 12:00:00 AM
Abstract :
Displacement vector estimation is one of the open key problems in computer vision and video coding. For example, in computer vision, displacement vector estimation is usually the basis of some kind of motion estimation. Unfortunately, displacement vector estimation using statistical methods is always computationally complex, which might be a restriction in real-time processing. In this paper, we show that displacement vector estimation can be efficiently performed by using cellular neural networks (CNNs). In order to find CNN templates, therefore, we have used the new optimization method of iterative annealing
Keywords :
cellular neural nets; computational complexity; computer vision; estimation theory; iterative methods; motion estimation; real-time systems; simulated annealing; vectors; video coding; cellular neural networks; computational complexity; computer vision; displacement vector estimation; iterative annealing; motion estimation; optimization method; real-time processing; statistical methods; templates; video coding; Cellular neural networks; Computer vision; Couplings; Motion estimation; Neural networks; Object detection; Optimization methods; Physics; Polynomials; Video coding;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007455