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DSIFT_EF

于 2020-11-02 发布
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代码说明：

说明：  用于处理亮度不一样或者曝光时间长短不一样的图像，通过图像融合增强图像效果。(Used to process images with different brightness or different exposure time, and enhance the image effect through image fusion.)

文件列表：

DSIFT_EF\DENSE-SIFT.pdf, 7092378 , 2020-06-06
DSIFT_EF\dense.jpg, 150781 , 2020-06-04
DSIFT_EF\dense1.jpg, 106521 , 2020-05-30
DSIFT_EF\DenseSIFT.m, 3748 , 2014-10-08
DSIFT_EF\Dsift1\2-15--50.png, 1021733 , 2020-10-20
DSIFT_EF\Dsift1\2-15-35.png, 1175090 , 2020-10-20
DSIFT_EF\Dsift1\2-15-75.png, 795987 , 2020-10-20
DSIFT_EF\Dsift1\2-15-r-25.jpg, 151777 , 2020-10-20
DSIFT_EF\Dsift1\re1.jpg, 75302 , 2020-10-20
DSIFT_EF\DSIFTNormalization.m, 1422 , 2014-10-08
DSIFT_EF\DSIFT_fusion.m, 2688 , 2015-07-16
DSIFT_EF\exp-DSIFT_EF\exp10\10000.jpg, 703870 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp10\30000.jpg, 606233 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp10\400.jpg, 398646 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp10\6000.jpg, 629868 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp10\result10-DSIFT_EF.jpg, 667396 , 2020-10-17
DSIFT_EF\exp-DSIFT_EF\exp11\10000.jpg, 703870 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp11\20000.jpg, 679703 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp11\400.jpg, 398646 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp11\6000.jpg, 629868 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp11\result11-DSIFT_EF.jpg, 665324 , 2020-10-17
DSIFT_EF\exp-DSIFT_EF\exp12\10000.jpg, 703870 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp12\40000.jpg, 504592 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp12\500.jpg, 478214 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp12\6000.jpg, 629868 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp12\result12-DSIFT_EF.jpg, 678771 , 2020-10-19
DSIFT_EF\exp-DSIFT_EF\exp13\10000.jpg, 703870 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp13\30000.jpg, 606233 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp13\500.jpg, 478214 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp13\6000.jpg, 629868 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp13\result13-DSIFT_EF.jpg, 664439 , 2020-10-17
DSIFT_EF\exp-DSIFT_EF\exp14\10000.jpg, 703870 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp14\20000.jpg, 679703 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp14\500.jpg, 478214 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp14\6000.jpg, 629868 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp14\result14-DSIFT_EF.jpg, 663274 , 2020-10-17
DSIFT_EF\exp-DSIFT_EF\exp15\1000.jpg, 574287 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp15\10000.jpg, 703870 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp15\2000.jpg, 566842 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp15\6000.jpg, 629868 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp15\result15-DSIFT_EF.jpg, 600974 , 2020-10-17
DSIFT_EF\exp-DSIFT_EF\exp4\10000.jpg, 817264 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp4\2000.jpg, 697661 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp4\500.jpg, 502663 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp4\5000.jpg, 733092 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp4\result4-DSIFT_EF.jpg, 723480 , 2020-10-17
DSIFT_EF\exp-DSIFT_EF\exp5\2000.jpg, 697661 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp5\20000.jpg, 831787 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp5\300.jpg, 421860 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp5\5000.jpg, 733092 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp5\result5-DSIFT_EF.jpg, 751411 , 2020-10-17
DSIFT_EF\exp-DSIFT_EF\exp6\2000.jpg, 697661 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp6\300.jpg, 421860 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp6\30000.jpg, 653095 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp6\5000.jpg, 733092 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp6\result6-DSIFT_EF.jpg, 715878 , 2020-10-17
DSIFT_EF\exp-DSIFT_EF\exp7\1000.jpg, 593227 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp7\10000.jpg, 817264 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp7\2000.jpg, 697661 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp7\5000.jpg, 733092 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp7\result7-DSIFT_EF.jpg, 720634 , 2020-10-17
DSIFT_EF\exp-DSIFT_EF\exp8\1000.jpg, 593227 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp8\2000.jpg, 697661 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp8\20000.jpg, 831787 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp8\5000.jpg, 733092 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp8\result8-DSIFT_EF.jpg, 749140 , 2020-10-17
DSIFT_EF\exp-DSIFT_EF\exp9\10000.jpg, 703870 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp9\400.jpg, 398646 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp9\40000.jpg, 504592 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp9\6000.jpg, 629868 , 2020-10-16
DSIFT_EF\exp-DSIFT_EF\exp9\result9-DSIFT_EF.jpg, 664977 , 2020-10-17
DSIFT_EF\images\flash\flash.jpg, 18048 , 2014-07-12
DSIFT_EF\images\flash\no_flash.jpg, 17815 , 2014-07-12
DSIFT_EF\images\ForrestSequence\1_Scene7_10.tif, 2130592 , 2009-07-09
DSIFT_EF\images\ForrestSequence\2_Scene7_09.tif, 2131344 , 2009-07-09
DSIFT_EF\images\ForrestSequence\3_Scene7_05.tif, 2131932 , 2009-07-09
DSIFT_EF\images\ForrestSequence\4_Scene7_13.tif, 2132444 , 2009-07-09
DSIFT_EF\images\garage\garage1.jpg, 78876 , 2013-10-04
DSIFT_EF\images\garage\garage2.jpg, 94708 , 2013-10-04
DSIFT_EF\images\garage\garage3.jpg, 98137 , 2013-10-04
DSIFT_EF\images\garage\garage4.jpg, 91849 , 2013-10-04
DSIFT_EF\images\garage\garage5.jpg, 55957 , 2013-10-04
DSIFT_EF\images\garage\garage6.jpg, 44825 , 2013-10-04
DSIFT_EF\images\multitwo\1.jpg, 859473 , 2020-05-30
DSIFT_EF\images\multitwo\2.jpg, 370538 , 2020-05-30
DSIFT_EF\images\multitwo\3.jpg, 747529 , 2020-05-30
DSIFT_EF\images\multitwo\4.jpg, 447400 , 2020-05-30
DSIFT_EF\images\new\12000.jpg, 613269 , 2020-06-09
DSIFT_EF\images\new\2000.jpg, 523340 , 2020-06-08
DSIFT_EF\images\new\20000.jpg, 627509 , 2020-06-09
DSIFT_EF\images\new\500.jpg, 440167 , 2020-06-08
DSIFT_EF\images\new\8000.jpg, 582137 , 2020-06-08
DSIFT_EF\images\new2\10000.jpg, 565376 , 2020-06-08
DSIFT_EF\images\new2\20000.jpg, 614251 , 2020-06-09
DSIFT_EF\images\new2\3000.jpg, 517154 , 2020-06-09
DSIFT_EF\images\new2\500.jpg, 388774 , 2020-06-09
DSIFT_EF\images\text\130.bmp, 3981366 , 2020-05-26
DSIFT_EF\images\text\20.bmp, 3981366 , 2020-05-26
DSIFT_EF\images\text\250.bmp, 3981366 , 2020-05-26
DSIFT_EF\images\text\75.bmp, 3981366 , 2020-05-26

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    直方图调整并显示对比，直方图调整后都需要对比一下调整前与调整后的区别，并显示。此程序适合初学者学习。(Histogram and adjust the display contrast)
    2013-08-21 08:25:38下载
    积分：1
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    2013-04-24 15:26:42下载
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    基于OpenCV实现的图像切割程序，通过程序中参数设置分块的数量和大小(The project is uesd to break an image into block)
    2013-12-01 22:31:29下载
    积分：1
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    C#的图像处理，用于验证码的识别功能，可以识别简单的有背景干扰的验证码(C# image processing, is used to identify functional verification code, you can recognize simple background interference verification code)
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    2017-04-19 10:06:08下载
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    一些使用支持矢量机进行图像分割的算法，以及相关的文献资料，打包下载的好东西哦(Some use support vector machine algorithm for image segmentation, and related literature, package download a good thing)
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    积分：1
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    内容包括高斯混合概率假设密度滤波器仿真程序，GM-PHD滤波器提供了一种PHD滤波器工程化的方法，在无数据关联的情况下进行目标跟踪。(This algorithm is extended to accommodate mildly nonlinear target dynamics using approximation strategies from the extended and unscented Kalman filters.)
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    积分：1
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    遥感图像的色彩变换 能从IHS转换到RGB 并能从RGB转换到HIS 效果比较好(Remote sensing images from the IHS color transform to the RGB and the conversion from RGB to HIS conversion effect is better)
    2009-03-13 21:06:43下载
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  • two
    ：植物种类识别方法主要是根据叶片低维特征进行自动化鉴定。然而，低维特征不能全面描述叶片信息，识别准确率低，本文提 出一种基于多特征降维的植物叶片识别方法。首先通过数字图像处理技术对植物叶片彩色样本图像进行预处理，获得去除颜色、虫洞、 叶柄和背景的叶片二值图像、灰度图像和纹理图像。然后对二值图像提取几何特征和结构特征，对灰度图像提取 Hu不变矩特征、灰 度共生矩阵特征、局部二值模式特征和 Gabor 特征，对纹理图像提取分形维数，共得到 2183 维特征参数。再采用主成分分析与线性 评判分析相结合的方法对叶片多特征进行特征降维，将叶片高维特征数据降到低维空间。使用降维后的训练样本特征数据对支持向量 机分类器进行训练(plant species identification method is mainly based on blade automatic identification of low dimensional characteristics.However, can not fully describe blade low-dimensional feature information, identification accuracy is low, in this paper A kind of plant leaves recognition method based on multiple feature dimension reduction.First by digital image processing technology to the plant leaf color sample image preprocessing, obtain background color removal, wormhole, petioles, and the blades of a binary image, gray image and texture image.Then the binary image to extract the geometric characteristics and characteristics of structure and characteristics of gray image extraction Hu moment invariants, gray co-occurrence matrix feature, local binary pattern features and Gabor, to extract the fractal dimension of texture image, get 2183 d characteristic parameters.By principal component analysis and linear uation analysis method of combining the characteristics of blade more feature dimensi)
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