提出了一种基于视觉注意机制的感兴趣区检测方法：使用分水岭方法分割图像区域；根据生物的视觉注意机制特性,选用中央周边差的采样方式提取图像特征,将不同维的图像特征融合为显著图；显著点经过竞争得到的注意焦点作为分水岭分割的种子点,然后融合显著图和分水岭分割区得到感兴趣区；遵循返回抑制和邻近优先的准则选择并转移注意焦点,从而计算区域的重要性或兴趣度.实验结果表明该方法符合生物的视觉注意机制,在自动检测感兴趣区时可以有效减少过分割,也能较好的处理大对象.

A novel visual attention mechanism based on approach of regions of interest (ROIs) detection was presented,which segmented image regions with the watershed algorithm.Image features were extracted by sampling the center-surround differences according to biological visual attention mechanism,which were combined into a saliency map.Competition among salient points in this map gave rise to a single focus of attention (FOA) which was selected as the seed point of watershed segmentation.ROIs were extracted by combining salient regions with watershed segmented regions.Based on inhibition of return and proximity,the FOA was selected and shifted to measure the importance or interest of the detected regions.The experimental results show that the proposed method complies with biological visual attention mechanism,which is also effective to reduce over-segmentation in auto-detecting ROIs and performs well for large objects.