Description: 利用改进的脉冲神经网络完成灰度图像的分割-The use of impulsive neural networks to improve the completion of gray-scale image segmentation Platform: |
Size: 1024 |
Author:岳碧波 |
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Description: 一种新的基于snake模型的水平集图像分割方法：用matlab编程实现，可进行灰度图像的分割，里面有演示实例，便于学习。-A new snake model based on the level set image segmentation methods: using matlab programming implementation, can be gray-scale image segmentation, there are demonstration examples for study. Platform: |
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Author:tudan |
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Description: OTSU Gray-level image segmentation using Otsu s method.
Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes
by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection
Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern.
9:62-66 1979). Thresholds are computed to maximize a separability
criterion of the resultant classes in gray levels.
OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the
corresponding Iseg is therefore a binary image. The pixel values for
Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ...
[Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability
criterion within the range [0 1]. Zero is obtained only with images
having less than n gray level, whereas one (optimal value) is obtained
only with n-valued images.- OTSU Gray-level image segmentation using Otsu s method.
Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes
by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection
Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern.
9:62-66 1979). Thresholds are computed to maximize a separability
criterion of the resultant classes in gray levels.
OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the
corresponding Iseg is therefore a binary image. The pixel values for
Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ...
[Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability
criterion within the range [0 1]. Zero is obtained only with images
having less than n gray level, whereas one (optimal value) is obtained
only with n-valued images. Platform: |
Size: 2048 |
Author:ltrko9kd |
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Description: 阈值法实现灰度图像分割，效果不错，可行，可以借鉴-Threshold method to achieve gray-scale image segmentation, the effect of a good, viable, can learn from Platform: |
Size: 1024 |
Author:yuzhun |
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Description: 摘 要:针对茄子图像的灰度和颜色特点,利用MATLAB中丰富的图像处理函数,分别进行了色差分割和色调分割。在
色调分割中,采用了自动选取阈值的Otsu法。在去除残留噪音的处理中,采用标注的方法对二值图像的各连通区域进行面积
统计。保留最大面积的区域,从而使分割效果大大改善。利用多参数来衡量分割效果,使评价做到最大程度的客观、合理。-Based on the gray- level and color characteristics of eggp lant image, hue and color- difference segmentation were
conducted by using the rich image p rocessing functions embedded inMATLAB. The auto threshold- adap tivemethod ofOtsu operation
was specially used for hue segmentation. And upon the elimination of the residue noise, labelingmethod for statistical calculation was
introduced for the connected regions of the binary image. Themaximum areaswere p reserved to imp rove segmentation effects. In addi2
tion, multi- indiceswere app lied to assess the effect of segmentation, in order to get impersonal and reasonable assessment Platform: |
Size: 565248 |
Author:renyh |
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Description: 基于阈值分割的运动车辆检测跟踪，在视频中按灰度阈值不同进行图像分割，并标记结果。-Threshold-based segmentation to track the movement of vehicles detected in the video by a different gray level threshold for image segmentation, and mark the results. Platform: |
Size: 111616 |
Author:哈田甜哈 |
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Description: In this project ,we propose a color based segmentation method that uses the c means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The key concept in this color based segmentation algorithm with k means means to convert a given gray level MR image in to a color space image and then separate the position of tumor objects from other items of an MR image by using c means clustering
And histogram clustering .Experiments demonstrates that the method can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region.
-In this project ,we propose a color based segmentation method that uses the c means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The key concept in this color based segmentation algorithm with k means means to convert a given gray level MR image in to a color space image and then separate the position of tumor objects from other items of an MR image by using c means clustering
And histogram clustering .Experiments demonstrates that the method can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region.
Platform: |
Size: 2048 |
Author:pramod |
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Description: 使 用 说 明
本光碟中包含五个文件夹。
（1）视频剪辑：可供教学演示，其中，"two_cells"是采用改进的变分水平集方法，实现GAC模型图像分割的演化过程；"denoissing"是利用P_M方程，对图像平滑去噪的演化过程 "curve_linear_heat_flow"是利用FT实现的闭合曲线的线性热流演化过程。
（2）二值图像：其中的图像可供形态学图像处理实验用，也可通过提取对象的边界，供曲线演化实验使用。
（3）灰度图象和彩色图像：其中的图像，可以作为图像分割，平滑滤波，反差增强，彩色增强以及图像放大等实验的素材。
（4) MATLAB程序：其中包含10余个MATLAB程序或（函数）的源代码。程序中所作的注释，与书中关于对应算法的描述是一致的。
本光碟中的所有内容，仅供教学和研究参考。-This cd-rom contains five folder.
(1) video clip: for teaching demonstration, among them, "two_cells" is modified variational level set method, realize the GAC model of image segmentation evolution process "Denoissing" is to use P_M equation, the noise of the image smooth evolution process "Curve_linear_heat_flow" is the use of the realization of the FT closed curve of the evolution process of the linear heat flux.
(2) binary images: one of the image for morphology image processing experiments with, but through the extraction of the object boundaries, the curve evolution for use.
(3) gray image and color images: one of the image, image segmentation, can be used as a smooth filtering, contrast enhancement, color and enhance image magnification of the material.
(4) MATLAB: including more than 10 MATLAB program or (function) of source code. The comments in the program, and the book about the description of the corresponding algorithm is consistent.
The entire content of the disc, o Platform: |
Size: 7410688 |
Author:何最红 |
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Description: 用matlab实验水平集，运用到灰度图像上面可以得到的很好的分割效果，下载后可以直接运行得到结果-Matlab experiment level set, apply to the gray image, it can get very good segmentation effect can be run directly after download the result Platform: |
Size: 22528 |
Author:yang shun |
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Description: 《图像处理的偏微分方程方法》随书光盘完整版
本光碟中包含五个文件夹。
（1）视频剪辑：可供教学演示，其中， two_cells 是采用改进的变分水平集方法，实现GAC模型图像分割的演化过程； denoissing 是利用P_M方程，对图像平滑去噪的演化过程 curve_linear_heat_flow 是利用FT实现的闭合曲线的线性热流演化过程。
（2）二值图像：其中的图像可供形态学图像处理实验用，也可通过提取对象的边界，供曲线演化实验使用。
（3）灰度图象和彩色图像：其中的图像，可以作为图像分割，平滑滤波，反差增强，彩色增强以及图像放大等实验的素材。
（4) MATLAB程序：其中包含10余个MATLAB程序或（函数）的源代码。程序中所作的注释，与书中关于对应算法的描述是一致的。
本光碟中的所有内容，仅供教学和研究参考。-" Image Processing Based on PDE" CD with the book contains the full version of the disc five folders. (1) Video clip: for teaching demonstration, which, " two_cells" is improved variational level set methods to achieve the evolution of GAC model image segmentation " denoissing" is the use of P_M equation, the evolution of image smoothing denoising process " curve_linear_heat_flow" is the use of a closed curve FT achieve a linear heat evolution. (2) binary image: one of the images for morphological image processing experiments, it can also be obtained by extracting the object boundary curve evolution for experimental use. (3) gray-scale images and color images: images which can be used as image segmentation, filtering, contrast enhancement, color enhancement and image amplification test material. (4) MATLAB Program: which includes more than 10 or MATLAB program (function) of the source code. Comments made in the program, and a description of the boo Platform: |
Size: 7414784 |
Author:marshal |
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Description: 算法步骤：
（1） 计算图像直方图，灰度级为Ns，图像的平均灰度值为Tc；
（2） 分别计算第一个灰度级和其余灰度级的信息熵并求和，前两个灰度级和其余灰度级的信息熵并求和，以此类推计算Ns个和；
（3） 找到H在Tc附近最大值的位置Tenf即为阈值；
（4） 图像中大于阈值的像素置1，其余置0而实现二值分割。
算法的核心：信息熵越大越有不确定性，分割出的图片越好。(Algorithm steps:
(1) the histogram of the image is calculated, the gray level is Ns, and the average gray value of the image is Tc.
(2) calculate the information entropy of the first gray level and the rest of grayscale, and sum up the information entropy of the first two grayscale and the rest gray level, and calculate Ns sum by analogy.
(3) to find the location of the maximum value of H near Tc, Tenf is the threshold;
(4) 1 pixels larger than the threshold in the image, and the rest 0 to achieve the two value segmentation.
The core of the algorithm: information entropy is more uncertainty, the segmented image.) Platform: |
Size: 71680 |
Author:leo小野兽 |
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Search - Gray-level matlab image segmentation