基于近似高斯金字塔的视觉注意模型快速算法

利用输入图像的近似高斯金字塔,将经典的基于显著性的视觉注意模型改造为时空开销更小的版本,从而使其更加适合在嵌入式实时系统中实现.首先采用矩形窗口近似圆形窗口,矩形平均算子近似高斯卷积核;然后采用“先做行累加,再做列累加”的方法来实现矩形平均算子,并直接采样计算出各个特征通道的显著性分布图,该算法关于输入图像像素点个数具有线性时间复杂度;最后,还给出了在显著性分布图中抑制已提取区域显著性的快速算法.在Berkeley分割图像库上的实验结果表明,该方法极大地减小了系统实现的时空开销,且输出结果的误差在可接受范围内.提出的用矩形窗口近似圆形窗口,用矩形平均算子近似高斯卷积核的方法,还适用于其他需要在嵌入式实时系统中实现的图像处理问题.

Fast Visual Attention Model Algorithm Based on Approximate Gaussian Pyramids

Classical saliency-based visual attention models are adapted for embedding real-time systems with less time and space costs based on approximate Gaussian pyramids of the input image. Firstly, the circular window and discrete Gaussian convolution are approximated by rectangular window and rectangular average operator respectively. Then, rectangular average operator is implemented through "row accumulation followed by column accumulation". And conspicuity maps of each channel are calculated and sampled at desired intervals directly with linear computational complexity on the number of the input pixels. At last, a fast algorithm for inhibiting the saliency of extracted regions in the saliency map is proposed. Experimental results in the images from Berkeley segmentation dataset validate that the proposed methods have much less computational costs with acceptable outputting errors. The two approximate methods in this paper can also be applied in other image processing problems in embedding real-time systems.