基于交叉皮质模型的单幅图像阴影检测算法

图像中含有阴影区域对后续处理任务影响较大, 根据阴影特性, 提出基于交叉皮质模型(Intersecting cortical model, ICM)的单幅图像阴影检测算法. 通过在点火连接矩阵构造上考虑邻域像素值依赖关系, 融入局部二值模式(Local binary pattern, LBP)表征的纹理信息形成了Te-ICM模型. 根据阴影检测流程,利用模型迭代特性, 通过设计停止条件自动检测本影, 在本影修复后生成附着半影. 同时优化模型参数, 设计了基于分层聚类直方图划分的阈值下降策略. 仿真结果表明: 对于典型影像集, Te-ICM模型及相应参数设计可以较好地实现阴影检测, 输出阴影掩模准确度高, 为后续阴影去除提供了基础.

Single Image Shadow Detection Based on Intersecting Cortical Model

Shadow is an integral part of many natural images, which can pose tough problems and limitations for further image processing tasks. By the analysis of shadow characteristics, a single image shadow detection method based on the intersecting cortical model (ICM) is proposed. Neurons in ICM possess dynamical spiking properties have the capability to segment the image naturally. We modify the linking matrix among neurons and combine the local texture features shown by local binary patterns (LBP) to make the Te-ICM for segment of shadow regions. The new model possesses the capability of taking adjacent pixel information into the firing matrix. The optimized parameters produced by the modified hierarchical clustering histogram partition method lead to the shadow detection sequences. We build an automatic stopping condition for umbra and penumbra iterations. Experimental results demonstrate that the output shadow mask keeps the size and shape of original objects well for typical image dataset, and that the proposed method can find wide applications to monochromatic or chromatic images containing one or more shadow regions, yielding high-quality results for further shadow removal operation.