Description: 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 simultaneously. Contours are treated in the intervening contour framework, while
texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we
introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure
of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of
normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results
on a wide range of images are shown. Platform: |
Size: 159744 |
Author:aan |
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Description: 使用核PcA来识别图片，图片为200张测试图片，200张训练图片，包含在在压缩文件中。-To identify the use of nuclear PcA picture, pictures, for 200 test images, 200 training images, is included in the compressed file. Platform: |
Size: 3163136 |
Author:戴步成 |
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Description: 在各种图形中，通过提取点的凹凸部分来鉴别曲线的特征，本节代码利用曲率关系分别提取凹点的个数，判断曲线的弯曲-In a variety of graphics, through the extraction point to identify curves convex-concave part of the characteristics of the code used in this section were extracted from concave curvature of the relationship between the number of points to determine the bending curve Platform: |
Size: 759808 |
Author:赵庆祥 |
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Description: ASM是由Cootes和泰勒推出的多分辨率方法的一个例子。
基本思想：
在ASM模型训练，训练从手工绘制的图像轮廓。发现的ASM模型在训练使用主成分分析（PCA），使该模型自动识别数据的主要变化是，如果可能的轮廓/好的对象的轮廓。还包含了ASM模型的协方差矩阵描述行垂直纹理口岸时，在正确的位置。
-Description This is an example of the basic Active Shape Model (ASM) as introduced by Cootes and Taylor, with multi-resolution approach.
Basic idea:
The ASM model is trained from manually drawn contours in training images. The ASM model finds the main variations in the training data using Principal Component Analysis (PCA), which enables the model to automatically recognize if a contour is a possible/good object contour. Also the ASM models contains covariance matrices describing the texture of the lines perpendicular to the control points when in the correct positions.
After creating the ASM model, an initial contour is deformed by finding the best texture match for the control points. This is an iterative process, in which the movement of the control points is limited by what the ASM model recognizes from the training data as a "normal" object contour.
Literature:
- Ginneken B. et al. "Active Shape Model Segmentation with Optimal Features", IEEE Transactions on Medical I Platform: |
Size: 10190848 |
Author:谢冉 |
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Description: snake 模型的MATLAB算法。可用于图形轮廓识别等图像处理方面的领域。-active contour model developed by others.It can be used in line detection and motion tracking.If someone wants to use it,please get in tough with the developer. Platform: |
Size: 203776 |
Author: |
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Description: 图像识别，包括人脸识别和虹膜识别，在数字图像中虹膜位置的有效定位是虹膜识别的关键问题。用一种基于主动轮廓线模型的方法定位虹膜的位置，先用灰度投影法检测出瞳孔内的一点作为瞳孔的伪圆心，该圆心只要能落在瞳孔内部即可。-Image recognition, including face recognition and iris recognition, iris position in the digital image of the iris recognition and effective positioning is the key issue. Using a model based on active contour method of positioning the iris position, the first gray projection method used to detect a point within the pupil as a pupil of the pseudo-center of a circle, the center of a circle can be as long as it falls within the pupil. Platform: |
Size: 958464 |
Author:张海 |
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Description: 这是一个基于活动轮廓模型的快速全局最小算法的matlab实现程序，对学习现代图像处理有一定帮助。-This is an active contour model based on the rapid global minimum algorithm matlab implementation process of learning in modern image processing have some help. Platform: |
Size: 962560 |
Author:殷代强 |
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Description: 国外课件：Active shape model 和 active contour model(snake)的matlab 原码，包括PDF格式的程序使用说明-Foreign courseware: Active shape model and active contour model (snake) in the matlab source, including the PDF format of the program instructions Platform: |
Size: 671744 |
Author:Dark Rider |
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Description: Gradient information serves several purposes. It can relate
the structure of objects in an image, identify features of interest
for recognition/classification directly or provide the basis of further
processing for various computer vision tasks. For example, "edges," noted
by regions of high gradient magnitude, are central to the task of
identifying defects in circuitry. A sample "edge-detected" image using
the Image Processing Toolbox for MATLAB is shown where
locations marked by white are those points that are indicative of high
gradient magnitude, which can also be described as regions of high
pixel contrast.-Gradient information serves several purposes. It can relate
the structure of objects in an image, identify features of interest
for recognition/classification directly or provide the basis of further
processing for various computer vision tasks. For example, "edges," noted
by regions of high gradient magnitude, are central to the task of
identifying defects in circuitry. A sample "edge-detected" image using
the Image Processing Toolbox for MATLAB is shown where
locations marked by white are those points that are indicative of high
gradient magnitude, which can also be described as regions of high
pixel contrast. Platform: |
Size: 6144 |
Author:aisso831 |
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Description: gvf snake 邊緣檢測範例(包含範例圖)-1. Introduction and Installation Instructions
----------------------------------------------
Welcome to the Gradient Vector Flow (GVF) Active Contour Toolbox. The
Matlab*.m files in this software distribution implement our GVF snake,
the original (Kass, Witkin, and Terzopoulos) snake, the balloon snake,
and the distance snake. These programs were designed, written and tested
by Chenyang Xu and Jerry Prince.
To install this software, simply unzip the file into an empty directory,
making sure to ask your unzipper program to restore the directory
structure of the zipped files. In this documentation, we will assume
that you have unzipped the files into C:\GVF.
2. Documentation
----------------
We have included two research papers that describe GVF. You can find
these in C:\GVF\papers.
Brief descriptions of the software modules can be found in this file
below. For more detail, you should run Matlab, add the path
C:\GVF\snake, and type "help function-name". F Platform: |
Size: 1561600 |
Author:Jason Wang |
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Description: 本演示实现了主动轮廓模型由Kass等人提出。
要运行它的图形用户界面
1。基于MATLAB型引导提示。
2。关于“到现有的图形用户界面，点击”
3。选择在与本文件相同的目录snk.fig文件
4。按一下上方的绿色箭头启动图形界面
一旦GUI已经启动，您可以通过使用蛇
1。点击“新形象”，并加载输入图像。提供的样品图像。
2。平滑参数设置“西格玛”或保留其默认值并点击“过滤器”。这将平滑图像。
3。只要你点击“过滤器”，头发会出现交叉，用他们和你左击鼠标，您可以选择在图像上的初始轮廓的位置。会出现一个红色圆圈到处您单击并在多数情况下，你应该单击对象周围的一切你想要的部分。最后一点，必须挑选使用右键单击，以阻止更多的积分为MATLAB的要求。
4。设置各种蛇参数（在能源方面的相对权重的蛇目标函数）或离开它们的默认值，然后按一下他们“迭代”按钮。蛇会出现和移动，因为它收敛到它的低能量状态。-This demo implements the Active Contour Models as proposed by Kass et al.
To run it with GUI
1. Type guide on the matlab prompt.
2. Click on "Go to Existing GUI"
3. Select the snk.fig file in the same directory as this file
4. Click the green arrow at the top to launch the GUI
Once the GUI has been launched, you can use snakes by
1. Click on "New Image" and load an input image. Samples image are provided.
2. Set the smoothing parameter "sigma" or leave it at its default value and click "Filter". This will smooth the image.
3. As soon as you click "Filter", cross hairs would appear and using them and left click of you mouse you can pick initial contour location on the image. A red circle would appear everywhere you click and in most cases you should click all the way around the object you want to segment. The last point must be picked using a right-click in order to stop matlab for asking for more points.
4. Set the various snake paramete Platform: |
Size: 333824 |
Author:zhou |
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Description: 静态背景下的高精度视频目标是实时检测与轮廓提取，matlab源代码实现。
-High-precision static background video object detection and contour extraction in real time, matlab source code. Platform: |
Size: 43008 |
Author:李英华 |
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Description: 这是“Implicit Active Contours Driven by Local Binary Fitting Energy”（简称LBF模型）的MATLAB源代码。LBF模型是非常重要局部区域活动轮廓模型，它被广泛使用于各个领域，如MRI大脑图像分割，血管图像分割，图像偏差场纠正。-This is the "Implicit Active Contours Driven by Local Binary Fitting Energy" (referred to as the LBF model) of the MATLAB source code. LBF model is very important to local area active contour model, which is widely used in various fields, such as MRI brain image segmentation, vessel segmentation, image bias field correction. Platform: |
Size: 52224 |
Author:erdongchen1985 |
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Description: 这是“An efficientlocalChan–Vesemodelforimagesegmentation”（简称LCV模型）的MATLAB源代码。LCV模型是非常重要局部区域活动轮廓模型，它被广泛使用于各个领域，如MRI大脑图像分割，血管图像分割，图像偏差场纠正。-This is "An efficientlocalChan-Vesemodelforimagesegmentation" (referred to as the LCV model) of the MATLAB source code. LCV model is very important to local area active contour model, which is widely used in various fields, such as MRI brain image segmentation, vessel segmentation, image bias field correction. Platform: |
Size: 45056 |
Author:erdongchen1985 |
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Description: 缺陷识别m文件，对缺陷图像进行中值，小波去噪，利用数学形态学方法提取二进制图像轮廓，并计算裂纹等缺陷的面积和周长-M file defect identification, the value of the defect in the image, wavelet denoising, using mathematical morphology to extract the binary image contour, and calculates the cracks and other defects in the area and perimeter Platform: |
Size: 1024 |
Author:zwx |
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Description: 利用MAT—LAB对实验数据进行了基于表面轮廓法的三维图像重构，观察不同的平滑领域参数以及轮廓提取阈值对三维重构效果的影响。-In this paper，taking advantage
of MATLAB we reconstruct experiment data into three dimension image in the way of feature-based method，
and observe the effect of different parameters，such as the size of smooth convolution kernel and the threshold of ex—
traction contour，on the quality of three dimension reconstruction Platform: |
Size: 935936 |
Author:姜南 |
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