Salient Building Detection Based on SVM

提出了一种针对自然图像中显著性建筑物的检测方法.首先,采用自底向上的注意力机制,对图像进行Haar小波分解,对得到的HL,LH分量进行平方求和,得到增强图像,然后对该增强图像在垂直方向上进行侧投影,基于得到的投影曲线进行多层阈值分割,找到显著性建筑物候选区域.进而,利用Sobel算子进行水平边缘与垂直边缘的检测,并统计较长的水平边缘与垂直边缘的数目,组成特征矢量.最后利用线性支持向量机对特征进行分类.实验证明了所提算法的有效性.This paper focuses on detecting salient buildings in a scenery image. A method based on bottom-up attention mechanism is proposed to detect salient buildings. Firstly, Haar wavelet decomposition is used to obtain the enhanced image which is the sum of the square of LH sub-image and HL sub-image. Secondly, the enhanced image is projected in the vertical direction to obtain the projection profile, and building candidates are separated from the background based on multi-level thresholding. Thirdly, the structure statistic features of buildings are extracted based on Sobel operator. The feature vector is formed by the number of long horizontal edges and that of vertical edges. Finally, linear support vector machines are used to classify buildings and the others. The proposed approach has been experimented on many real-world images with promising results.国家自然科学基金项目(60635050,60405004);; 国家自然科学创新研究群体基金项目(60021302

基于视觉显著性的车载单目相机自运动估计及前车尺度估计方法

提出一种基于视觉显著性的车载单目相机自运动估计及前车尺度估计方法。首先，针对车载相机自运动估计，通过视觉显著性计算方法检测并去除含有噪声的单目图像序列中的运动目标，同时考虑图像的纹理区域和平滑区域，利用加权显著图保留有用特征点，进而对车载相机进行鲁棒的自运动估计。其次，将前车距离转化为前车尺度估计问题，通过描述子匹配与李代数中正则化的强度匹配相结合的方法最小化损失函数，通过设计视觉注意力机制选择有纹理无遮挡的图像块，并对选定的图像块中的像素赋权以减轻被噪声破坏像素的影响，从而实现鲁棒、准确的尺度估计。最后，利用多个具有挑战性的数据集对所提方法进行分析验证。结果表明，单目相机自运动估计方法达到了基于立体相机方法的水平，前车尺度估计方法在充分发挥强鲁棒性优势的同时保证了预测精度

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies