卷积特征可视化

基于matlab软件的指纹识别程序，数据库。(Matlab-based program fingerprint recognition software, database.)

提出了一种基于图像边缘连接的线段检测方法。数字图像中的线段是由很多线段图元组成,在同一 条线段中,它们的方向是相同的并且受到真实线段的方向约束。因此可以通过提取小的线段图元, 然后分析它们 的连接特性, 进行线段的检测。首先用ADM 算法对图像进行边缘检测, 得到单像素宽的边缘 然后在4 个方向上 进行线段的检测 对初步得到的结果进行合并和验证, 去掉短的线段, 连接临近的线段。经过实验证明, 该方法能 够快速地提取图像中的线段,并且对噪声具有一定的鲁棒性(A line segment connecting a detection method based on image edges. The segment of the digital image is composed by many segments elements, their orientation is the same and by the real line segment direction constraint in the same segment. By extracting a small segment Element can then analyze their connection characteristics for the detection of the line segment. First with ADM algorithm for edge detection on the image to obtain a single-pixel-wide edge then in the four directions on the detection of the line segments merger and verification of the preliminary results obtained, removing the short line segments, the line segments connected near. The experiments show that this method can quickly extract the line segments in the image, and certain robustness to noise)

基于Maltab的gui图像配准程序。输入图像需为灰度图像。(matlab-based image registration)

本算法有图像融合算法，聚类分析算法，去噪算法，运动模糊算法(This algorithm with image fusion algorithm, clustering analysis algorithm, the denoising algorithm, fuzzy algorithm)

用matlab编写的区域生长法的程序，通过选择种子和阈值，可以进行图像分割和检查瑕疵(Written by matlab region growing process, by selecting the seeds and thresholds for image segmentation and inspection for defects)

第二代Curvelet变换及其在图像融合中的应用研究，第二代Curvelet较第一代有更好的发展前景(Second-generation Curvelet Transform and Its Application in Image Fusion of applied research, the second-generation Curvelet better than the first generation of the development prospects of)

图像分割，完整程序，验证可行。新手上传，望支持。(image direct)

用PHD滤波的方法实现多目标跟踪，及对PHD滤波性能的检测。(PHD filter method using multi-target tracking, and performance testing for the PHD filter.)

此程序为计算雷达目标特性角闪烁特性的函数代码(computing the anglar glint)

基于分形理论的改进的一种图像分割算法,用matlab语言实现(Based on Fractal Theory An improved image segmentation algorithm)