Detecting visually salient scene areas and deriving their relative spatial relations from continuous street-view panoramas

A salient scene is an area within an image that contains visual elements that stand out from surrounding areas.They are important for distinguishing landmarks in first-person-view(FPV)applications and determining spatial relations in images.The relative spatial relation between salient scenes acts as a visual guide that is easily accepted and understood by users in FPV applications.However,current digitally navigable maps and location-based services fall short of providing information on visual spatial relations for users.This shortcoming has a critical influence on the popularity and innovation of FPV applications.This paper addresses the issue by proposing a method for detecting visually salient scene areas(SSAs)and deriving their relative spatial relationships from continuous panoramas.This method includes three critical steps.First,an SSA detection approach is introduced by fusing region-based saliency derived from super-pixel segmentation and the frequency-tuned saliency model.The method focuses on a segmented landmark area in a panorama.Secondly,a street-view-oriented SSA generation method is introduced by matching and merging the visual SSAs from continuous panoramas.Thirdly,a continuous geotagged panorama-based referencing approach is introduced to derive the relative spatial relationships of SSAs from continuous panoramas.This information includes the relative azimuth,elevation angle,and the relative distance.Experiment results show that the error for the SSA relative azimuth angle is approximately±6°(with an average error of 2.67°),and the SSA relative elevation angle is approximately±4°(with an average error of 1.32°)when using Baidu street-view panoramas.These results demonstrate the feasibility of the proposed approach.The method proposed in this study can facilitate the development of FPV applications such as augmented reality(AR)and pedestrian navigation using proper spatial relation.

基于图像显著性区域的遥感图像机场检测

针对已有方法对图像逐像素进行分析的不足,将人眼的注意力选择计算模型引入到遥感图像的机场目标检测中,提出一种基于图像显著性区域的遥感图像中机场目标检测与识别的方法,以提高自动目标检测的效率.首先利用霍夫变换对遥感图像中是否存在机场目标进行初步筛选,然后利用改进后的基于图像的视觉显著性模型提取显著性区域,根据区域上的尺度不变特征变换特征并结合多层分类回归树完成机场目标的识别.实验结果表明,该方法比现有的其他机场检测方法具有速度快、识别率高、虚警率低的特点,同时对噪声有较强的鲁棒性.

基于自适应调节核函数的图像显著区域提取方法

目前的图像显著区域提取技术仅针对无噪声图像或者没有分析噪声对提取技术的影响。文章提出一种图像显著区域提取新方法,该方法将自适应调节核函数应用在图像显著区域获取中。根据具体图像像素点与周围小区域的差异性来判断该位置的显著性。差异性是与自适应调节核函数有关的单调下降函数。该算法采用多尺度融合的方法获取整幅图的显著区域,对无噪声图像进行显著区域提取分析取得了较好效果。在图像含噪时与两种现有显著区域获取方法进行比较,实验结果表明该算法同样对噪声具有很强的鲁棒性。

基于DCT域视觉的场馆监控图像显著区域检测

针对传统图像显著区域的目标运动检测能力不足,提出一种基于DCT域视觉的图像显著区域检测方法。以MCU为基本单位将监控图像分割成图块,利用DCT系数对颜色、亮度、纹理等特征向量进行提取计算得到全局对比度,运用最大类间方差法计算阈值将图像分割,获得视觉显著区域。同时,以全局背景判断图像中的注视区范围、注视点位置,计算注视点中最强的位置,完成显著图像的区域检测。实验证明,所提方法可以实时对出现在显著区域的目标进行检测,检测效率高。