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Finding saliency object via an integration approach

机译:通过集成方法找到显着性对象

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Saliency detection is a hot topic in the community of computer image and vision. In this paper, we present a new saliency detection method. Given an input image, our method first uses Harris corner detection technique to approximately locate the salient region, and then assign the saliency scores to each pixel, getting the center-prior based map. In addition, we employ Bayesian formula to further optimize it, obtaining the center-Bayesian map. On the other hand, we use the image boundary to generate boundary-based map. Finally, we merge them into a saliency map as our final saliency map. A large number of experimental results demonstrate that the proposed algorithm is superior to most existing algorithms.
机译:显着性检测是计算机图像和视觉社区中的热门话题。在本文中,我们提出了一种新的显着性检测方法。给定输入图像,我们的方法首先使用Harris角点检测技术大致定位显着区域,然后将显着性分数分配给每个像素,从而获得基于中心优先级的地图。另外,我们采用贝叶斯公式进一步优化它,获得中心贝叶斯图。另一方面,我们使用图像边界生成基于边界的地图。最后,我们将它们合并为显着图,作为最终的显着图。大量的实验结果表明,该算法优于大多数现有算法。

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