Description: 文回顾与评述了三维GIS的相关发展情况，探讨了三维GIS的有关基本问题，如数据获取、大数据量存贮与处理、三维空间分析，同时指出科学计算可视化、数据库系统管理、数字影象处理等技术的成熟和二维GIS长期发展提供的理论实践经验等为三维GIS的发展提供了良好的基础。最后为三维GIS实际系统的开发提出了几个值得注意的要点。-In this paper,the review and comment on the development of 3 dimension GIS are first made.Then the basic aspects about 3D GIS,such as data acquisition,storing and processing of mass data,analysis of 3D space,are discussed.Meanwhile, it points out that the techniques such as scientific computation visualization,database system management and digital image processing,and theories and experiences provided by development of 2D GIS offer a better platform form the development of 3D GIS.Finaally,some key points in developing practical system of 3D GIS is proposed. Platform: |
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Author:王哲 |
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Description: 图像插补，可以使用二维图像达到三维观看的效果，绝对好的很好的-Graphic interpolation could get 3D effect form two-dimension image.It is a good thing absolutly! Platform: |
Size: 9139200 |
Author:孔晓东 |
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Description: 第1章 分形
1.1 分形的定义
1.2 分维
第2章 迭代函数系统
2.1 压缩映射
2.2 迭代函数系统(IFS)
2.3 拼贴定理
第3章 一般图像的分形压缩方法
3.1 图像的表示方法与图像的均方误差
3.2 分形压缩的简化方法
3.3 压缩实例
3.4 问题与改进方向 -Chapter 1 Fractal 1.1 Fractal Fractal Dimension 1.2 definitions in Chapter 2 Iterated Function System 2.1 Mappings 2.2 Iterated Function System (IFS) 2.3 collage theorem in Chapter 3 of the general fractal image compression method 3.1 Image Representation and image of the mean square error 3.2 Fractal Compression 3.3 Compression simplified method of example 3.4 with improvements in the direction of Platform: |
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Author:leonwwj |
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Description: Min/Max filter，最大最小滤波器，说明看英文，只是国外的人写的代码，
-Description The filter computes the minima and/or maxima of an array over sliding window with a given size.
Multidimensional array is fully supported: running filter in 1D, 2D filter for image processing applications (erosion/dilatation), 3D and more.
This package has been implemented with a special care on the running speed: the MEX engine uses an algorithms that requires no more than three (3) comparisons per element and per dimension in all configurations.
All numerical and logical class arrays are supported.
Contributor (beside author) is Vaclav Potesil
Acknowledgements
This submission has inspired the following:
Free-knot spline approximation
MATLAB release MATLAB 7.8 (R2009a)
Other requirements MEX correctly setup NO image processing is required Download a tiff file for
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Author:谢冉 |
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Description: A set (e.g. an image) is called "fractal" if it displays self-similarity: it can be split into parts, each of which is (at least approximately) a reduced-size copy of the whole.
A possible characterisation of a fractal set is provided by the "box-counting" method: The number N of boxes of size R needed to cover a fractal set follows a power-law, N = N0 * R^(-DF), with DF<=D (D is the dimension of the space, usually D=1, 2, 3).
DF is known as the Minkowski-Bouligand dimension, or Kolmogorov capacity, or Kolmogorov dimension, or simply box-counting dimension.
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Author:piri_small |
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Description: 1.GeometricContext文件是完成图片中几何方向目标分类。
参考文献《Automatic Photo Pop-up》Hoiem 2005
2 GrabCut文件是完成图像中目标交互式分割
参考文献《“GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts》
C. Rother 2004
3 HOG文件是自己编写的根据HOG特征检测行人的matlab代码
4 虹膜识别程序是下载的一个通用的虹膜识别程序，可以运行
5 GML_AdaBoost_Matlab_Toolbox是一个很好用的adaboost matlab工具箱
6 libsvm-mat-2.91-1 是用C编写的改进的SVM程序，代码质量很高，提供了matlab接口
7 SIFT_Matlab 是编写的利用sift特征进行的宽基线匹配，代码质量高
8 FLDfisher 是利用fisher 线性降维方法进行人脸识别-1.GeometricContext file is complete the picture in the geometric direction of target classification. References " Automatic Photo Pop-up" Hoiem 2005 2 GrabCut the target file is an interactive segmentation of image reference " " GrabCut " - Interactive Foreground Extraction using Iterated Graph Cuts" C. Rother 2004 3 HOG documents prepared under their own HOG Characteristics of pedestrian detection matlab code 4 iris recognition process is to download a general iris recognition program, you can run 5 GML_AdaBoost_Matlab_Toolbox is a good use of adaboost matlab toolbox 6 libsvm-mat-2.91-1 is written in C to improve the SVM procedures, code of high quality, provides a matlab interface to 7 SIFT_Matlab is prepared for the use of sift features a wide baseline matching, the code is the use of high quality 8 FLDfisher fisher linear dimension reduction method for face recognition Platform: |
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Author:张数 |
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Description: 一种新的三维映射几何建模方法, 即通过弧面映射变换, 先获取目标图与原图的像
素一一映射关系, 再采用四点线性插值方法, 实现了对原图像的桶形几何失真校正-This paper put s forward a kind of new 3-dimension mapping g eometr y mo deling
metho d: first w e obtain the o ne by one mapping relat io ns between the o bject picture and its original
one, and then use the 4-point linear interpolat ion method so that w e realize the barrel g eomet ry
disto rt io n adjustment of the orig inal picture. Platform: |
Size: 344064 |
Author:刘浏 |
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Description: 四棱锥由5个面构成一个封闭的立体图,其中4个共顶点的侧面是三角形,底面是个四边形。如果我们要绘制一个3D的四棱锥只需要绘制这5个面即可，绘制的方法和前一篇文章OpenGL_Qt学习笔记之_03(平面图形的着色和旋转)的相同。只不过这里的顶点坐标是3维的，所以图像深度那一维不一定为0。因此我们可以事先计算好四棱锥各个顶点的坐标，这对学过立体几何的人来说应该是小case了。然后绘制每个面就可以。
注意，在opengl中绘制每个面时，所有面给出的顶点的顺序都要按照逆时针或者顺时针(我这里采用的是逆时针)，这样才能保证所绘制出来的图像时正确的。
现在我们在paintGL中开始绘制四棱锥，如果按照NeHe的教程，它只是绘制了个金字塔，并没有底面，只有4个侧面，这里，我采用它的方法，代码如下: -A closed perspective view of four-sided pyramid is constituted by 5 surface, wherein the side of vertex 4 is triangular, the bottom surface is a quadrangle. If we want to draw a 3D pyramid only need to draw the five faces can, the drawn and former article OpenGL_Qt study notes _03 (planar graph coloring and rotation). But here vertex coordinates of a three-dimensional, so the image depth that dimension is not necessarily 0. So we can be calculated in advance the good four-sided pyramid coordinates of each vertex should learn solid geometry, is a small case. Then you can draw each face. Opengl in draw each surface, all surface given vertex order must be in accordance with the counterclockwise or clockwise (I used counterclockwise), so as to ensure the drawn image. Now we start drawing in paintGL four pyramid NeHe tutorial, it just draws a pyramid and the bottom surface of only four sides, here, I adopt its methods, the code is as follows: Platform: |
Size: 309248 |
Author:wuwei |
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Description: 研究中我么采用分段的方法对数字音频信号嵌入水印。为便于研
究，以灰度图像为数字水印原始信号经二值化处理后得到水印再嵌入
音频信号的离散变换域中，从而得到嵌入水印的音频信号，其实现过
程原理框图如图1 所示。在水印嵌入的过程主要实现四个阶段的任
务：(1)数据准备与预处理，主要是准备好水印原始图片和待嵌入水印
的音频信号 (2)音频信号分段处理，用于嵌入水印的音频数据部分等
分成(m1*m2)个音频数据段 (3)水印图像降维处理，在此过程灰度图像
水印转化为二值图象, 要将其嵌入到一维的数字音频信号中, 必须将
二维的图像转化成一维的序列 (4)将水印嵌入到数字音频信号中,从而
最终获得带数字水印的音频信号。-The study I Mody, segmented digital audio signal embedded watermark. Discrete transform domain to facilitate the study of the grayscale image for the digital watermark the original signal is obtained after the binarization processing watermark embedded in the audio signal, thereby obtaining the watermark embedded in the audio signal, and its implementation process schematic block diagram shown in Figure 1. Mainly in the watermark embedding process to achieve the task of four phases: (1) The data preparation and pretreatment, is ready to watermark the original picture and to be embedded in the audio signal of the watermark (2) audio signal segment processing for embedding watermark The audio data portion is divided into (m1* m2) audio data segment (3) The watermark image dimension reduction processing, binary image is converted to grayscale image watermark in this process, and to be embedded into a digital audio signal of a one-dimensional , two-dimensional images must be converted in Platform: |
Size: 1024 |
Author:吴吟骋 |
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Description: BM3D图像降噪快速算法，BM3D图像降噪算法基于3维块匹配方式，利用图像的冗余性进行降噪，效果甚好，但是计算量相当大，此算法在一定程度上对BM3D进行了优化处理，提高其运算速度-BM3D fast algorithm of image denoising, BM3D algorithm for image denoising based on block matching method based on the three dimension, using the redundancy of image noise reduction, effect is very good, but the amount of calculation is quite large, this algorithm in a certain extent of BM3D optimized for processing. To improve the operation speed Platform: |
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Author:宋涛 |
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Description: 1.mean shift 的迭代过程只做了一次（为了简单）
2.对图像中的某一点进行平移时，只考虑了距离它小于2*hr的点，而不是图像中所有的 点。
3.实验过程中只用到了gauss核，虽然提供了method的选择，但在进行值域加权时只用 到gauss核，如果需要请自己改正。
4.meanshiftseg中，对分成一类的像素，取的是它们的平均值作为属于该区域的像素的 值。
5. 为了简单彩色图只做了第一维。其它类似的。-The iterative process 1.mean shift only once (for simplicity) 2. image pan when considering only one point less than the distance between its 2* hr point, instead of the image in all points. 3. The experiment uses only a gauss core, while providing a method of choice, but only used during the gauss range nuclear weighted, if necessary, please correct yourself. 4.meanshiftseg, the class of a pixel is divided into, and their average value was taken as the value of the area belonging to the pixels. 5. For simplicity only the first color image dimension. Other similar. Platform: |
Size: 4096 |
Author:王栋 |
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Description: 在这个项目中，我们的目标是建立一个识别和大小231x231图像呈现对象分类系统。我们得到了一组训练图像，每四个标签之一：1飞机；汽车2；3马，否则。我们提供了两个特点：一是方向梯度直方图（HOG），其尺寸为5408；另一个是overfeat ImageNet美国有线电视新闻网的特点，其尺寸37000。关于测试图像，我们只给出了每个图像的功能，没有标签，结果判断由平地机。我们的目标是提供二进制和多个预测。平衡错误率（BER）是我们的性能评估。为了解决这个问题，我们首先减少PCA的问题的维数，处理不平衡数据集，通过向上采样或下采样，去除异常值，通过无监督学习，如k-均值和EM算法。其次，我们使用ML方法，如二进制和多项式logistic回归，二进制和多项式SVM和神经网络。多项式SVM的证明有最好的结果。最后，我们在100分中得了92分。-In this project, our goal was to build a system that recognizes and classifies the object present in an image of size 231x231. We were given a set of training images each with one of four labels: 1 for airplanes 2 for cars 3 for horses 4 otherwise. We were provided with two sets of features: one is Histogram of Oriented Gradients (HOG), which has dimension of 5408 the other one is OverFEAT ImageNet CNN Features, which has dimension of 37,000. Concerning the test images, we were only given the features of each image without label, and the results to be judged by the grader. Our goal was to provide binary and multiple predictions. The Balanced Error Rate (BER) was our performance uator. To solve the problem, we firstly reduced the problem’s dimensionality by PCA, dealt with imbalanced datasets through up-sampling or down-sampling, and removed outliers through unsupervised learning such as K-Means and EM Algorithm. Secondly, we applied ML methods such as Binary and Multinomial Logist Platform: |
Size: 322560 |
Author:杨雪 |
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Description: 1、读入图片，根据PGN格式的line 2 确定矩阵的大小为 28*28=784，根据line4 获取.
2、读入图片，根据PGN格式的line 2 确定矩阵的大小为 28*28=784，根据line4 获取。
3、计算平均矩阵。
4、对平均值矩阵进行SVD：
5、平均矩阵进行SVD后的前20个singular vector的输出结果。
6. 将训练集的每一张图片当成一行，形成一个矩阵，然后对矩阵进行PCA分解。
7. 这个矩阵对测试集的每张图片进行降 维，得到的图像。(1, read the picture, according to the line 2 of PGN format, the size of the matrix is 28*28=784, obtained according to the line4.
2, read the picture, according to the line 2 of PGN format, the size of the matrix is 28*28=784, obtained according to the line4.
3. Calculate the average matrix.
4. The average value matrix is SVD:
5. The average matrix carries out the output of the first 20 singular vector after SVD.
6. take each picture of the training set as a line, form a matrix, and then decompose the matrix by PCA.
7. this matrix reduces the dimension of each picture of the test set and gets the image.) Platform: |
Size: 6418432 |
Author:彦雁 |
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Search - 3 dimension image