Salient object detection via boosting object-level distinctiveness and saliency refinement

Many salient object detection approaches share the common drawback that they cannot uniformly highlight heterogeneous regions of salient objects, and thus, parts of the salient objects are not discriminated from background regions in a saliency map. In this paper, we focus on this drawback and accordingly propose a novel algorithm that more uniformly highlights the entire salient object as compared to many approaches. Our method consists of two stages: boosting the object-level distinctiveness and saliency refinement. In the first stage, a coarse object-level saliency map is generated based on boosting the distinctiveness of the object proposals in the test images, using a set of object-level features and the Modest AdaBoost algorithm. In the second stage, several saliency refinement steps are executed to obtain a final saliency map in which the boundaries of salient objects are preserved. Quantitative and qualitative comparisons with state-of-the-art approaches demonstrate the superior performance of our approach.     

Saliency detection for panoramic landscape images of outdoor scenes

Saliency detection has been researched for conventional images with standard aspect ratios, however, it is a challenging problem for panoramic images with wide fields of view. In this paper, we propose a saliency detection algorithm for panoramic landscape images of outdoor scenes. We observe that a typical panoramic image includes several homogeneous background regions yielding horizontally elongated distributions, as well as multiple foreground objects with arbitrary locations. We first estimate the background of panoramic images by selecting homogeneous superpixels using geodesic similarity and analyzing their spatial distributions. Then we iteratively refine an initial saliency map derived from background estimation by computing the feature contrast only within local surrounding area whose range and shape are changed adaptively. Experimental results demonstrate that the proposed algorithm detects multiple salient objects faithfully while suppressing the background successfully, and it yields a significantly better performance of panorama saliency detection compared with the recent state-of-the-art techniques.     

3D-RABLC：一种基于LQI置信度的三维空间定位求精算法

提出一种基于LQI置信度的三维空间定位求精算法(3D-RABLC)。通过大量节点实验,获得节点间一跳RSSI值与距离的关系、LQI与分组错误率的关系,依此划分LQI置信度,对测得的RSSI值进行过滤,建立三维多跳求精模型或弥补求精方法对置信度低的RSSI值进行修正。节点实验表明,该算法大大降低了RSSI测距误差,比已有三维定位算法具有更好的定位精度。     

激光冲击20CrNiMo钢表面微造型摩擦学性能研究

为了探索激光冲击20CrNiMo钢表面微造型摩擦学性能,从表层微观结构和表面宏观形貌两个角度,研究了激光冲击强化的方法对20CrNiMo钢表面摩擦磨损性能的影响。采用激光以不同搭接率搭接冲击20CrNiMo钢,用透射电子显微镜观察激光搭接冲击20CrNiMo钢试样表层微观组织结构,取得了试样表层晶粒状态的照片;采用CETR UMT摩擦磨损试验机对其进行摩擦磨损试验,取得了试样表面摩擦系数和磨损量的数据。结果表明,激光冲击强化作用可以导致20CrNiMo钢试样表层晶粒细化,在磨损过程中,晶粒细化层明显抑制表面疲劳磨损的产生,从而提高其耐磨性能;激光冲击搭接率越高,金属材料表面的摩擦磨损性能越好。     

计算机机房的环境要求及解决方法

介绍了现代计算机机房的温度、湿度、洁净度、接地等技术问题以及针对计算机机房的浪涌过电压而采取的防护措施。     

一种有限元自适应剖分的误差计算方法的实现

本文在总结有限元自适应剖分的误差计算方法的基础上,提出了一种新的误差定义,并通过计算实例证明其可行性。文中还定性地讨论了预置的误差控制值ε对计算效果的影响。     

基于Sobel算子的医学图像边缘检测研究

边缘检测是图像处理过程的关键技术.由于医学图像的特殊性,检测边缘的准确性对疾病的诊断和治疗有着重大的影响.针对传统Sobel算法存在定位不精确、提取边缘较粗等不足,提出了一种改进算法.算法在传统Sobel算子模板基础上增加了45°方向和135°方向两个模板,提高了边缘定位的精度,采取局部梯度均值作为阈值对初始梯度图像进行局部梯度筛选,局部弱边缘得到增强,然后对处理后梯度图像进行细化和提取,得到边缘图像.实验证明,算法获取的图像边缘与传统Sobel算法相比,具有定位准确、边连续性好和边缘较细等优点,在医学图像处理中具有一定的实用性.     

铜基形状记忆合金的晶粒细化方法及机理探讨

介绍了国内外铜基形状记忆合金的晶粒细化方法，并探讨了晶粒细化的机理     

快速辐射度算法的实现研究

当虚拟场景发生变化时,扩展的逐步求精算法作为一种快速的辐射度算法,大量减少了重新计算辐射度所花费的时间。对扩展的逐步求精算法进行研究,并利用OpenGL对算法进行了实现。通过在实验中对虚拟场景的表面反射率和几何属性进行改变,获得了一些证明算法有效性的数据,从而验证了该算法的有效性。     

激光热力强化电化学沉积试验研究

为了进一步揭示激光的热力效应对电化学沉积的强化作用,构建了激光电化学复合沉积试验系统,进行了理论分析和实验验证。采用激光循环往复的扫描方式照射沉积区域制备沉积层试样,对沉积过程中的力效应和热效应进行测试,最后采用扫描电子显微镜对沉积层的表面形貌和截面形貌进行观察对比。结果表明,激光的热力效应能加快金属离子的还原反应,促进晶核形成和晶粒细化,在激光能量为0.2mJ（20kHz）时,能获得良好的沉积速率（0.198mg/min）;在激光能量为0.4mJ（20kHz）时,沉积层的拉伸强度性能较好,达到256.38 MPa。此研究结果对电解加工技术的发展是有一定帮助的。