The focus measure is widely used in the shape from focus, passive autofocus, etc, and its performance determines the application results of these techniques. In this paper, a new focus measure that combines two existing focus measures is proposed to improve performance, including the sensitivity to defocus and the independence of image content. Comparative experiments are conducted to verify the effectiveness of the proposed focus measure. This paper provides a new idea for follow-up research on high-performance focus measures.
Imitating the visual characteristics of human eyes is one of the important tasks of digital image processing and computer vision. Feature correspondence of humanoid-eye binocular images is a prerequisite for obtaining the fused image. Human eyes are more sensitive to edge, because it contains much information. However, existing matching methods usually fail in producing enough edge corresponding pairs for humanoid-eye images because of viewpoint and view direction differences. To this end, we propose a novel and effective feature matching algorithm based on edge points. The proposed method consists of four steps. First, the SUSAN operator is employed to detect features, for its outstanding edge feature extraction capability. Second, the input image is constructed into a multi-scale structure based on image pyramid theory, which is then used to compute simplified SIFT descriptors for all feature points. Third, a novel multi-scale descriptor is constructed, by stitching the simplified SIFT descriptor of each layer. Finally, the similarity of multi-scale descriptors is measured by bidirectional matching, and the obtained preliminary matches are refined by subsequent procedures, to achieve accurate matching results. We respectively conduct qualitative and quantitative experiments, which demonstrate that our method can robustly match feature points in humanoid-eye binocular image pairs, and achieve favorable performance under illumination changes compared to the state-of-the-art.
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