Pulse-coupled neural network shadow compensation
Pulse-coupled neural network shadow compensation
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DOI：
10.1117/12.342901
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发表时间：
1999-03
期刊：
影响因子：
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通讯作者：
John L. Johnson;Jaime R. Taylor;Matthew S. Anderson
中科院分区：
文献类型：
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作者：
John L. Johnson;Jaime R. Taylor;Matthew S. Anderson
脉冲耦合神经网络（PCNN）算法，当被修改为用作图像处理器时，提供了一种独特的乘法图像分解（PCNN因式分解）方法。因为因子分解是按场景对比度的级别排序的，所以前几个因子包含通常与阴影相关的强烈对比度。PCNN因子分解有效地自动发现场景阴影。这是进一步发展在这里作为一个计算有效的阴影补偿算法与说明性的例子，并示出显着更有效的直方图均衡。讨论了其优缺点。
The Pulsed Coupled Neural Network (PCNN) algorithm, when modified for use as an image processor, provides a unique method of multiplicative image decomposition (PCNN factorization). Because the factorization is ordered by levels of scene contrast, the first few factors contain the strong contrasts generally associated with shadows. The PCNN factorization effectively and automatically finds scene shadows. This is further developed here as a computationally effective shadow compensation algorithm with illustrative examples given, and is shown to be significantly more effective than histogram equalization. The advantage and disadvantages are discussed.