一种基于形态学的X射线数字图像焊缝边缘检测方法

在分析X射线数字图像本身的特点之后,提出利用多尺度形态学梯度算子来获得梯度图像,并通过迭代阈值分割算法,获取原图像边缘的方法。实验证明这是一种检测X射线数字图像焊缝边缘的有效方法。     

一种基于小波特征的彩色图像分割算法

利用小波变换的多尺度对图像纹理特征良好表达的特点,用小波变换提升算法图像纹理特征进行提取,从而达到较好的分割效果。     

分水岭变换和统计区域合并的图像分割算法研究

提出了一种基于分水岭变换和统计区域合并的图像分割方法.该方法综合利用高斯低通滤波、分水岭变换和统计区域合并,先对原始图像提取分割标记,然后利用Meyer分水岭变换对标记分水岭进行分割,最后利用概率统计的方法对过分割区域进行合并.该算法通过调节尺度参数可以实现由粗到细(coarse-to-fine)的分割.实验结果表明,这种简单可行的算法在分割噪声图像时依然有良好的效果,具有较强的鲁棒性.     

基于多分辨率阈值的非均匀光照微视觉图像

 为了满足微纳和生物医学领域对微视觉图像目标信息高精度实时提取的需求,提出一种基于多分辨率阈值的非均匀光照微视觉图像实时分割技术．针对传统方法所存在的在非均匀光照微视觉图像分割过程中难以精确估计阈值和实时性差等问题,首先根据微视觉图像的梯度概率密度稀疏分布特性,建立灰度强度分布估计目标函数,并利用迭代加权最小二乘法实现灰度强度分布优化估计,达到非均匀光照补偿的目的;然后,在传统二维Otsu方法的基础上,利用小波变换的多分辨率分析能力有效减少阈值估计过程中的计算量,提高微视觉图像分割的实时性．实验研究表明,该方法能有效协调分割精度和速度之间的矛盾关系,具有不受光照影响的特点,能快速取得精确的图像分割效果．     

基于方向小波变换的镁熔液弱小气泡检测

运用方向小波的多尺度和多方向变换,对高温镁熔液表面弱小气泡进行检测。先对原始图像进行方向小波变换重构高频,抑制背景杂质,再作帧差和迭代阈值分割处理,最后运用数学形态学检测到弱小气泡,并将结果与传统小波检测进行比较。实验结果表明方向小波变换方法是有效的,而且方向小波更符合图像的方向特点,能够较好地检测出复杂背景中的弱小气泡。     

双重主动轮廓图像分割

本文首先利用小波变换得到原图像的粗分辨逼近,在粗分辨逼近中得到图像的一个粗尺度分割。由于逼近图像中噪声下降,尺寸减少,使得算法对参数的选取不太敏感,而且收敛速度加快。然后将第一次分割结果通过小波反变换返回到原始尺度上,将得到的近似轮廓曲线作为初始水平集函数再在原图像中演化得到更准确的分割,这样就得到了一种双重主动轮廓图像分割算法。由于初始轮廓曲线非常接近真实的轮廓曲线,所以很快就可以收敛到真实的轮廓。理论分析和数值结果表明,双重主动轮廓分割算法可以快速有效地分离出感兴趣目标。     

基于数学形态学的显微图像分割算法

分水岭算法是一种图像分割的强有力的工具,而其分割性能和待分割图像梯度的计算方法相关,LucVincent所提出的分水岭算法主要存在过分割的问题。文章提出用形态学混合开闭重建运算的尺度空间平滑算法对图像进行平滑滤波处理,以消除图像细节和噪声而保留重要的边缘轮廓,再对平滑后的图像进行梯度修正,并进行梯度极小值抑制,进一步消除造成过分割区域,然后对修正后的梯度边缘图像进行分水岭分割。实验结果表明,采用本文的算法对显微动物精细胞图像分割能较好的解决过分割问题。     

基于小波系数邻域特征的CT与MR图像融合方法

提出一种基于小波变换的像素级CT,MR医学图像融合方法,利用离散小波变换分别将两幅源图像进行多尺度分解,再用不同的小波系数邻域特征指导高频分量和低频分量的小波系数的融合,低频分量采用邻域方差指导,高频分量采用邻域能量指导,最后根据融合图像的各小波系数重构融合图像.实验表明:不论从主观感受,还是采用信息熵和平均梯度两项指标作为客观定量评价标准,该方法都优于传统的融合方法,获得的融合图像有效地综合了CT与MR图像信息,能够同时清晰地显示脑部骨组织和软组织.     

面向道路场景语义分割的移动窗口变换神经网络设计

道路场景语义分割是自动驾驶环境感知的一项重要任务。近年来,变换神经网络(Transformer)在计算机视觉领域开始应用并取得了很好的效果。针对复杂场景图像语义分割精度低、细小目标识别能力不足等问题,本文提出了一种基于移动窗口Transformer的多尺度特征融合的道路场景语义分割算法。该网络采用编码-解码结构,编码器使用改进后的移动窗口Transformer特征提取器对道路场景图像进行特征提取,解码器由注意力融合模块和特征金字塔网络构成,充分融合多尺度的语义特征。在Cityscapes城市道路场景数据集上进行验证测试,实验结果表明,与多种现有的语义分割算法进行对比,本文方法在分割精度方面有较大的提升。     

基于梯度滤波器的多尺度高精度微运动测量

微运动测量在微纳制造和生物医学等高技术领域担任了一个重要的角色,本文将梯度滤波器方法和多尺度方法相结合,提出了一种用于微运动测量的基于梯度滤波器的多尺度方法．在该方法中,利用多尺度金字塔迭代方法,对测量图像进行降采样和低通滤波,将较大像素的图像运动转化为多个小像素的图像运动进行估计,从而提高运动估计精度．提出的方法用于测量两个像素附近的MEMS微机械图像运动时,测量偏差达到了0．005个像素．模拟实验结果表明,这种基于梯度滤波器的多尺度方法能够实现高精度的微运动测量．     

基于多分辨率分析和分水岭的图像分割方法

提出了一种基于小波多分辨率分析和分水岭算法的图像分割方法.在小波分解后的低分辨率图像上进行分水岭分割,提高了分割的速度;由低分辨率图像返回到高分辨率图像时,采用了一种基于边缘信息的合并函数,避免了边缘信息的丢失,保证了分割的准确性.此外预处理过程中,在梯度图像上基于Rayleigh分布采用阈值处理的方法,有效抑制了高斯噪声对梯度图像的影响,避免了过分割.实验结果证明,本文所提出的基于小波多分辨率分析的图像分水岭分割算法能够很好地兼顾算法的效率和分割的准确性.     

Brain image segmentation using a combination of expectation‐maximization algorithm and watershed transform

Watershed transformation is an effective segmentation algorithm that originates from the mathematical morphology field. This algorithm is widely used in medical image segmentation because it produces complete division even under poor contrast. However, over‐segmentation is its most significant limitation. Therefore, this article proposes a combination of watershed transformation and the expectation‐maximization (EM) algorithm to segment MR brain images efficiently. The EM algorithm is used to form clusters. Then, the brightest cluster is considered and converted into a binary image. A Sobel operator applied on the binary image generates the initial gradient image. Morphological reconstruction is applied to find the foreground and background markers. The final gradient image is obtained using the minima imposition technique on the initial gradient magnitude along with markers. In addition, watershed segmentation applied on the final gradient magnitude generates effective gray matter and cerebrospinal fluid segmentation. The results are compared with simple marker controlled watershed segmentation, watershed segmentation combined with Otsu multilevel thresholding, and local binary fitting energy model for validation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 225–232, 2016     

A Method of Obtaining Catchment Basins with Contour Lines for Foam Image Segmentation

Foam image segmentation, represented by watershed algorithm, is wildly used in the extraction of bubble morphology features. H-minima transformation was proved to be effective in locating the catchment basins in the traditional watershed segmentation method. To further improve the accuracy of watershed segmentation, method of top-bottom-cap filters and method of morphological reconstruction were implied to marking the catchment basins. In this paper, instead of H-minima transformation, a method of contour lines is specially proposed to obtain the catchment basins for foam image segmentation by using top-bottom-cap filters and less morphological reconstruction. Experimental results in foam segmentation show that the proposed method is equally accurate but more efficient than the method of H-minima plus morphological reconstruction, and equally efficient but more accurate than the method of H-minima plus top-bottom-cap filters.     

Research on the magnetic resonance imaging brain tumor segmentation algorithm based on DO-UNet

With the social and economic development and the improvement of people's living standards, smart medical care is booming, and medical image processing is becoming more and more popular in research, of which brain tumor segmentation is an important branch of medical image processing. However, the manual segmentation method of brain tumors requires a lot of time and effort from the doctor and has a great impact on the treatment of patients. In order to solve this problem, we propose a DO-UNet model for magnetic resonance imaging brain tumor image segmentation based on attention mechanism and multi-scale feature fusion to realize fully automatic segmentation of brain tumors. Firstly, we replace the convolution blocks in the original U-Net model with the residual modules to prevent the gradient disappearing. Secondly, the multi-scale feature fusion is added to the skip connection of U-Net to fuse the low-level features and high-level features more effectively. In addition, in the decoding stage, we add an attention mechanism to increase the weight of effective information and avoid information redundancy. Finally, we replace the traditional convolution in the model with DO-Conv to speed up the network training and improve the segmentation accuracy. In order to evaluate the model, we used the BraTS2018, BraTS2019, and BraTS2020 datasets to train the improved model and validate it online, respectively. Experimental results show that the DO-UNet model can effectively improve the accuracy of brain tumor segmentation and has good segmentation performance.     

基于标记的多尺度分水岭视频目标分割算法

针对视频目标提取的问题,提出了基于标记的多尺度分水岭视频目标分割算法.该算法以帧间变化检测为基础,通过改进的最小Tsallis交叉熵进行去噪、滤波,经形态学处理后得到运动目标初始二值掩模,并利用初始二值掩模得到用于分水岭算法的前景与背景标记,用该标记修正当前帧的多尺度形态学梯度图像,最后进行分水岭分割,得到具有精确边界的视频对象.实验结果表明,该算法能有效地分割和提取视频序列中的单个、多个以及快速运动的目标,继承了变化检测和分水岭算法速度快的优点,克服了分水岭容易产生过分割的缺点,具有较强的适用性.     

一种新的边缘保持分水岭的图像分割算法

为了达到抑制分水岭过分割和保持物体边缘信息不受破坏的双重目的,提出了一种新的边缘保持水岭(Watershed)算法.首先,根据K-均值聚类将图像分成多块;然后利用噪声标准差构造相对应的双边滤波器平滑每块图像;接着计算形态学梯度,对梯度图像进行H-minima标记;最后对标记图像进行分水岭分割.该算法将双边滤波和分水岭算法相结合,有效地抑制了过分割并且较好得保持了物体边缘信息.     

A Novel Defect Inspection Method for Semiconductor Wafer Based on Magneto-Optic Imaging

The defects of semiconductor wafer may be generated from the manufacturing processes. A novel defect inspection method of semiconductor wafer is presented in this paper. The method is based on magneto-optic imaging, which involves inducing eddy current into the wafer under test, and detecting the magnetic flux associated with eddy current distribution in the wafer by exploiting the Faraday rotation effect. The magneto-optic image being generated may contain some noises that degrade the overall image quality, therefore, in this paper, in order to remove the unwanted noise present in the magneto-optic image, the image enhancement approach using multi-scale wavelet is presented, and the image segmentation approach based on the integration of watershed algorithm and clustering strategy is given. The experimental results show that many types of defects in wafer such as hole and scratch etc. can be detected by the method proposed in this paper.     

一种基于马尔可夫随机场的运动目标检测算法

本文提出了一种基于马尔可夫随机场的运动目标检测算法.针对传统时间分割使用主观固定阁值的缺点,使用马尔可夫随机场模型对差分图像建模,并提出了一种新的模型阶次选择算法,以及一种可以加速收敛过程的随机场迭代算法.采用期望最大算法获取高斯分布参数并检测运动变化区域,利用形态学运算修正时间分割模板;空间分割部分提出了基于人眼视觉特征的改进分水岭算法,有效地解决了过分割问题;最后对时、空间分割结果进行信息融合处理,从而得到完整的运动目标.仿真实验结果证明了本文算法可以有效地分割视频运动目标.