搜索资源列表
Image-Segmentation
- 一个基于图形的高效图像分割算法，包括论文及相应的源代码。-This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develo
segmentation
- This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited
Drawpad
- 基于MFC编写的小型绘图程序，可绘制直线，椭圆，矩形，支持颜色选择，可以保存到本地（按文本格式存储），将绘制图形视为一个对象，利用链表进行存储和读取，可以选择并执行删除（基于链表操作），希望对学习MFC和链表的朋友有用-MFC-based drawing program, can draw a straight line, oval, rectangular, color selection support, you can save to a local file and open the s
paper-and-matlab-code
- 新发表论文Graph cuts based active contour model with selective local or global segmentation 附源码，基于图割的主动轮廓模型，用于有选择的局部分割或全局分割，欢迎下载！-Published Graph cuts based active the contour the model with selective local or global a segmentation attached source, the ac
huidufenge
- 在图像识别技术的实现过程中，图像分割是一个重要的预处理环节，图像分割效果，直接影响着后续的分类、目标识别、图像分析、图像理解等过程的结果。针对着不同的图像特点，目前已经提出了错综复杂的图像分割算法。其中基于图论的图像分割算法是近几年研究的热点，这类算法着眼于全局，更注重局部数据的处理，比一般方法可以获得更佳的效果，并且图论理论有着比较完备的数学理论基础，将其用于图像处理有着较好的应用前景。-In the implementation process of image recognition te
NGC_ACM
- 图割（GC)和主动轮廓模型（ACM)相结合的一种图像分割方法，既可以进行局部图像的分割，也可进行全局图像的分割，简单实用。-A combination of graph cuts (GC) and the active contour model (ACM) image segmentation method, either local image segmentation, global image segmentation, simple and practical.
segment
- We de¯ ne a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an e±cient segmentation algorithm based on this predicate, and show that although this algo
Edge-Detection
- 与以往关注图像局部特征和局部连续性的方法不同，本文中的方法能够提取关于图像的全局印象。为此，我们将图像分割问题转化为图划分问题并提出了划分中的一种全局判别准则——Ncut (Normalized Cut)-Concerned with the past image local characteristics and continuity of local methods, the methods herein can extract images on the overall impressio
code_registration_release_v1.0
- 非刚性表面检测和配准。一种新的非刚性基于局部熵图匹配方法。-a novel high-order affinity graph is constructed to model the consistency of local topology. A hierarchical clustering approach is then used to locate the nonrigid surfaces
LineMatchingSourceCode
- 图像匹配是图像处理的基本问题，同时也一个难点，本代码是基于直线描述子的场景匹配。-Line segment matching plays an important role in image processing and computer vision, while it remains a challenging task for images under various transformations. In this work, we present a line matching alg
LEM-Algorithm
- LEM（拉普拉斯特征映射）算法，拉普拉斯特征映射是基于局部邻域，保持局部结构的流形学习方法。LEM通过一个无向加权图刻画流形上数据点间的近邻关系，图的顶点为原始数据点，图的边对应点之间的近邻关系，边的权值对应近邻点之间的相似程度（也可以是某种距离），LEM在低维嵌入空间中尽量保持图中数据点之间的近邻关系，然后求取嵌入坐标。通俗的说，LEM认为在高维数据空间离得近的点在低维嵌入空间也应该离得近-LEM (Laplace feature mapping) algorithm, Laplace fea
fgm_2012_05_12
- 分解图匹配（Factorized Graph Matching）源码-Graph matching (GM) is a fundamental problem in computer science, and it plays a central role to solve correspondence problems in computer vision. GM problems that incorporate pairwise constraints can be formulate
siftTest
- SIFT（Scale-invariant feature transform）是一种检测局部特征的算法，该算法通过求一幅图中的特征点（interest points,or corner points）及其有关scale 和 orientation 的描述子得到特征并进行图像特征点匹配，获得了良好效果(It is an algorithm for detecting local features. The algorithm obtains good results by finding feat