基于YCbCr空间对大壁虎脑图谱的分割

确定大壁虎的各种运动行为与脑内控制其运动的具体的皮层组织对应关系是壁虎机器人的制作关键。通过对其脑图谱的制备,可以观察到脑内皮层组织的丰富信息。针对HE染色的大壁虎脑图谱自身的独特性,经过推敲,提出一种基于YCbCr空间的对图谱进行分割的方法。该方法将图谱由RGB空间转换为YCbCr空间,将亮度分量独立出来,不仅解决了拍摄过程中由于光线改变所带来的问题,而且阈值分明,分割彻底,鲁棒性好。其结果表明基于YCbCr空间的分割方法是分割效果非常好的方法。     

A segmentation method for noisy photoelectric image

In order to improve the ability of noisy photoelectric image segmentation and satisfy the requirement of human visual apperception, a noisy photoelectric image segmentation method is proposed in this paper. Firstly, the basic principle of FCM algorithm is analyzed in detail. Secondly, on the basis of PCM algorithm, only such pixels affected by noise lesser that centralized in the diagonal area of two-dimensional histogram is actualized image processing, and the probability of the pixel that lie in such area is obtained. Thirdly, objective function, membership matrix and cluster centers are renovated on the basis of PCM algorithm. Finally, experiment contrast between our method and other methods is executed, the results of experiment indicate that our method has relatively better segmentation quality and faster segmentation speed.     

基于局部和全局信息的自适应水平集图像分割

针对仅采用局部或全局信息无法快速准确分割灰度不均匀图像的问题,提出了一种基于局部和全局信息的自适应水平集图像分割模型。首先,利用图像局部信息和全局信息建立局部能量项和全局能量项,并且利用演化曲线轮廓内外小邻域的灰度均值差作为自变量,建立了权重函数模型,实现了局部能量项和全局能量项之间权重的自适应调整,提高了模型分割灰度不均匀图像的效率和准确性。其次,提出了一种新的能量惩罚项,避免了水平集函数的重新初始化,增强了数值计算的稳定性。最后,为验证模型的优越性,将模型与CV模型、LBF模型和LGIF模型进行了对比,并通过分割时间、迭代次数以及相似度等指标对分割结果进行了客观、定量分析。最终结果表明:该模型不但对初始轮廓具有较高鲁棒性,而且对灰度不均匀图像具有较高的分割准确性与分割效率。     

一种VideoSAR动目标阴影检测方法

在高帧率的视频合成孔径雷达(VideoSAR)成像模式获得的图像序列中,多普勒频移使运动目标在实际位置留下阴影,且相邻帧图像具有很强相关性。该文针对上述现象提出一种VideoSAR图像中动目标阴影检测的方法。首先,对每帧图像通过结合尺度不变特征变换(SIFT)和随机抽样一致性(RANSAC)算法实现配准并进行背景补偿,再采用CattePM模型抑制相干斑噪声。然后通过Tsallis灰度熵的最大化阈值分割方法自动分离目标和背景,获得二值图像。最后,对相邻多帧图像背景建模并差分,再结合三帧间差分法提取动目标阴影,结果标记至原帧图像相应位置。基于美国Sandia实验室公布的VideoSAR成像片段,实现了多个移动车辆的检测,验证了所提算法的有效性。     

基于等阻抗差分段法的变阻抗线电路模拟

新型Z箍缩驱动源系统采用变阻抗线结构形式的径向阻抗变换器进行能量传递和变换,为了获得最大的能量效率,需要应用电路模拟方法研究变阻抗线传递功率的特性。采用了等阻抗差分段法对变阻抗线进行了建模计算,与以往的等长度分段方法比较,在相同的分段数量下,等阻抗差分段法在阻抗变化的连续性上优于等长度分段法。给出了具有指数形式的变阻抗线的算例,分别采用两种分段法进行了计算,对计算结果的比较表明:等阻抗差分段法可以使计算更快地收敛到真实解。     

滑窗加权泽尼克矩特征的雷达目标识别技术

合成孔径雷达(SAR)图像的方位敏感性和相干噪声,影响SAR图像目标识别效果,针对此问题,提出了一种新的滑窗加权矩特征的雷达目标识别方法。利用三角剖分与生长切割算法得到将目标和阴影从相干噪声中分割出来的图像。根据泽尼克(Zernike)变换,计算Zernike矩,并提取滑窗加权Zernike矩作为特征不变量。最后,利用最近邻准则进行分类识别。仿真结果表明:利用滑窗加权Zernike矩作为特征向量,克服了SAR图像对方位的敏感性,有效地提高识别率,对SAR图像识别是有效的和稳健的。     

3D Multiphase Piecewise Constant Level Set Method Based on Graph Cut Minimization

Segmentation of three-dimensional (3D) complicated structures is of great importance for many real applications. In this work we combine graph cut minimization method with a variant of the level set idea for 3D segmentation based on the Mumford-Shah model. Compared with the traditional approach for solving the Euler-Lagrange equation we do not need to solve any partial differential equations. Instead, the minimum cut on a special designed graph need to be computed. The method is tested on data with complicated structures. It is rather stable with respect to initial value and the algorithm is nearly parameter free. Experiments show that it can solve large problems much faster than traditional approaches.     

A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation

Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient’s water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity.     

基于FGCM的冠脉OCT图像纤维斑块分割算法

冠脉OCT图像中的纤维斑块精确分割对于冠心病的诊断具有重大意义。针对模糊C均值算法邻域信息和空间信息利用率低,在分割具有弱边界的纤维化斑块过程中容易产生过分割的情况,提出了基于自定义窗口的邻域信息项和具有全局约束项G的FCM算法,即FGCM算法。首先,对OCT图像进行预处理,去除噪声。然后,采用自定义窗口获取像素强度的邻域信息,并将邻域信息和创造性提出的全局约束项G添加到FCM算法,使用FGCM算法分割纤维化斑块。最后,采用数学形态学方法作为后处理操作平滑斑块边界。通过对具有典型纤维化斑块特征的8个病例进行测试,与医生手动标记纤维化斑块(金标准)和其他文献的算法进行对比,测试结果显示所提算法分割精确度进一步提高,达到90%,能够进一步对血管狭窄程度进行定量分析,辅助医生制定诊断方案。