ID 原文 译文
19555 实验结果表明，该算法可以显著降低离线阶段数据采集的工作量，同时可以取得较高的定位精度。 The extensive experiments demonstrate that the proposed method is capable of not only constructing an accurate radio map at a low manual cost, but also achieving a high localization accuracy.
19556 针对从单目视觉图像中估计深度信息时存在的预测精度不够准确的问题，该文提出一种基于金字塔池化网络的道路场景深度估计方法。 Considering the problem that the prediction accuracy is not accurate enough when the depth information is recovered from the monocular vision image, a method of depth estimation of road scenes based on pyramid pooling network is proposed.
19557 该方法利用4个残差网络块的组合提取道路场景图像特征，然后通过上采样将特征图逐渐恢复到原始图像尺寸，多个残差网络块的加入增加网络模型的深度； Firstly, using a combination of four residual network blocks, the roadscene image features are extracted, and then through the sampling, the features are gradually restored to theoriginal image size, and the depth of the residual block is increased.
19558 考虑到上采样过程中不同尺度信息的多样性，将提取特征过程中各种尺寸的特征图与上采样过程中相同尺寸的特征图进行融合。 Considering the diversity of information indifferent scales, the features with same sizes extracted from the sampling process and the feature extraction process are merged.
19559 此外，对4个残差网络块提取的高级特征采用金字塔池化网络块进行场景解析，最后将金字塔池化网络块输出的特征图恢复到原始图像尺寸并与上采样模块的输出一同输入预测层。 In addition, pyramid pooling network blocks are added to the advanced features extracted by four residual network blocks for scene analysis, and the feature graph output of pyramid pooling network blocks is finally restored to the original image size and input prediction layer together with the output of the upper sampling module.
19560 通过在KITTI数据集上进行实验，结果表明该文所提的基于金字塔池化网络的道路场景深度估计方法优于现有的估计方法。 Through experiments on KITTI data set, the results show that the proposed method is superior to the existing method.
19561 针对基于图模型的显著性检测算法中节点间特征差异描述不准确的问题，该文提出一种目标紧密性与区域同质性策略相结合的图像显著性检测算法。 Considering the inaccurate description of feature differences between nodes in the graph-based saliency detection algorithm, an image saliency detection algorithm combining object compactness and regional homogeneity strategy is proposed.
19562 区别于常用的图模型，该算法建立更贴近人眼视觉系统的稀疏图结构与新颖的区域同质性图结构，以便描述图像前景内部的关联性与前景背景间的差异性，从而摒弃众多节点的冗余连接，强化节点局部空间关系； Different from the commonly used graph-based model, a sparse graph-based structure closer to the human visual system and a novel regional homogeneity graph-based structure are established. They are used to describe the correlation within the foreground and the difference between foreground and background. Therefore, many redundant connections of nodes are eliminated and the local spatial relationship of nodes is strengthened.
19563 并且结合聚类簇紧密性采取流形排序的方式形成显著图，利用背景区域簇的相似性，引入背景置信度进行显著性优化，最终得到精细的检测结果。 Then the clusters are combined to form a saliency map by meansof manifold ranking. Finally, the background confidence is introduced for saliency optimization by the similarity of the background region clusters and the final detection result is obtained.
19564 在4个基准数据集上与4种基于图模型的流行算法对比，该算法能清晰地突出显著区域，且在多种综合指标评估中，具备更优越的性能。 Compared with 4 popular graph-based algorithms on the four benchmark datasets, the proposed algorithm can highlight the salient regionsclearly and has better performance in the evaluation of multiple comprehensive indicators.