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[Special Effects] Imagesegementation DL : 0: To carry on small comparison for he different threshold value under the image segmentation result, have to use many windows,namely for take a look at a different threshold value of slice segmentation ’s result at same window , the window be divided into many small window.But conduct a lone medical science slice observation, withdraw\"interested area\", need bilever value , even multi-threshold value, can carry on a single piece at fixed position to browse and segment into slices by selected threshold value .Multi-windows bilever threshold value methods can not only withdraw\"interested area\" of the medical science slice, but also take a look at a different threshold value of slice segmentation ’s result at same window-To carry on small comparison for he differe nt threshold value under the image segmentatio n result, many have to use windows, namely for take a look at a different threshold v alue of slice segmentation 's win at same result dow. the window be divided into many small window.Bu not conduct a lone medical science slice observat ion, withdraw "interested area," need bilever value, even multi-threshold value, can carry on a single piece at fixed position to b rowse and segment into slices by selected thres hold value. Multi-windows bilever threshold v alue methods can not only withdraw "interested area "of the medical science slice. but also take a look at a different threshold val ue of slice segmentation 's result at same windo w
Date : 2008-10-13 Size : 1.46kb User : lizhengrong
[Special Effects] 1_embed DL : 0: DCT水印设I 为载体数据,W 为水印信号, K 为密钥,那么嵌入水印后的载体Iw 可描述为[124 ] : Iw = F( I ,W , K) . (1) 式中, F 表示水印的嵌入策略。-I as a carrier of data, W for the watermark signal, K key, Then the watermark embedded vector can be described as Iw [124] : F = Iw (I, W, K). (1) -, F said watermark embedding strategy.
Date : 2008-10-13 Size : 1.6kb User : 江力
[Special Effects] 1_recover DL : 0: 设I 为载体数据,W 为水印信号, K 为密钥,那么嵌入水印后的载体Iw 可描述为[124 ] : Iw = F( I ,W , K) . (1) 式中, F 表示水印的嵌入策略。-established as a carrier of data, W for the watermark signal, K key, Then the watermark embedded vector can be described as Iw [124] : F = Iw (I, W, K). (1) -, F said watermark embedding strategy.
Date : 2008-10-13 Size : 1.13kb User : 江力
[Special Effects] 2_embed DL : 0: 设I 为载体数据,W 为水印信号, K 为密钥,那么嵌入水印后的载体Iw 可描述为[124 ] : Iw = F( I ,W , K) . (1) 式中, F 表示水印的嵌入策略。-established as a carrier of data, W for the watermark signal, K key, Then the watermark embedded vector can be described as Iw [124] : F = Iw (I, W, K). (1) -, F said watermark embedding strategy.
Date : 2008-10-13 Size : 1.6kb User : 江力
[Special Effects] 3_embed_recover DL : 0: 设I 为载体数据,W 为水印信号, K 为密钥,那么嵌入水印后的载体Iw 可描述为[124 ] : Iw = F( I ,W , K) . (1) 式中, F 表示水印的嵌入策略。-established as a carrier of data, W for the watermark signal, K key, Then the watermark embedded vector can be described as Iw [124] : F = Iw (I, W, K). (1) -, F said watermark embedding strategy.
Date : 2008-10-13 Size : 1.66kb User : 江力
[Special Effects] improved_gaborfilter DL : 0: Bi-dimensional Gabor filter with DC component compensation This version of the 2D Gabor filter is basically a bi-dimensional Gaussian function centered at origin (0,0) with variance S modulated by a complex sinusoid with polar frequency (F,W) and phase P described by the following equation: G(x,y,S,F,W,P)=k*Gaussian(x,y,S)*(Sinusoid(x,y,F,W,P)-DC(F,S,P)), where: Gaussian(x,y,S)=exp(-pi*S^2*(x^2+y^2)) Sinusoid(x,y,F,W,P)=exp(j*(2*pi*F*(x*cos(W)+y*sin(W))+P))) DC(F,S,P)=exp(-pi*(F/S)^2+j*P) File Id: 13776 Average rating: 0.0 Size: 1 KB # of reviews: 0 Submitted: 2007-01-26 Downloads: 274 Subscribers: 0 Keywords: gabor filter Stiven Schwanz Dias -Bi-dimensional Gabor filter with DC compo .. compensation This version of the 2D Gabor f ilter is basically a bi-dimensional Gaussian f unction centered at origin (0, 0) with variance S modulated by a complex sinuso id with polar frequency (F, W) and phase P described by the following equati on : G (x, y, S, F, W, P) = k * Gaussian (x, y, S) * (Sinusoid (x, y, F, W, P) - DC (F, S, P)), where : Gaussian (x, y, S) = exp (-pi * S * 2 ^ (x ^ 2 y ^ 2)) Sinusoid (x, y, F, W, P) = exp (j * (2 * pi * F * (x * cos (W) y * sin (W)) P))) D C (F, S, P) = exp (-pi * (F / S) ^ 2 * P j) File Id : 13776 Average rating : 0.0 Size : # 1 KB of reviews : 0 Submitted : 2007-01-26 Downloads : 274 Subscribers : 0 Keywords : gabor filter Stiven Schwanz Dias
Date : 2008-10-13 Size : 1.27kb User : 石峰
[Special Effects] toolbox_signal DL : 0: This toolbox implements the algorithm in a fairly general way in a C file that can be called from Matlab. It allows to perform the traditional NL-means for denoising (for both B&W and color images) but also to use an arbitrary set of patches to perform the denoising. -This toolbox implements the algorithm in a fairly general way in a C file that can be called f rom Matlab. It allows to perform the traditiona l NL-means for denoising (for both B
Date : 2008-10-13 Size : 1.34mb User : 张文国
[Special Effects] MorphologicalThin DL : 0: 本代码实现了形态学细化。特点：中间过程可以存储在输入中，因此节省存储空间；并且附有注解，便于理解；附有参考文献，有算法描述。This function implements the morphology thinning. Input pSrc is a float matrix in the range of [0, 1]. It supports in-place operation.Ref: Z. Guo and R. W. Hall, \"Parallel Thinning with Two-Subiteration Algorithm\"
Date : 2008-10-13 Size : 1.11mb User : liuchao
[Special Effects] Imagesegementation DL : 0: To carry on small comparison for he different threshold value under the image segmentation result, have to use many windows,namely for take a look at a different threshold value of slice segmentation ’s result at same window , the window be divided into many small window.But conduct a lone medical science slice observation, withdraw"interested area", need bilever value , even multi-threshold value, can carry on a single piece at fixed position to browse and segment into slices by selected threshold value .Multi-windows bilever threshold value methods can not only withdraw"interested area" of the medical science slice, but also take a look at a different threshold value of slice segmentation ’s result at same window-To carry on small comparison for he differe nt threshold value under the image segmentatio n result, many have to use windows, namely for take a look at a different threshold v alue of slice segmentation 's win at same result dow. the window be divided into many small window.Bu not conduct a lone medical science slice observat ion, withdraw "interested area," need bilever value, even multi-threshold value, can carry on a single piece at fixed position to b rowse and segment into slices by selected thres hold value. Multi-windows bilever threshold v alue methods can not only withdraw "interested area "of the medical science slice. but also take a look at a different threshold val ue of slice segmentation 's result at same windo w
Date : 2025-12-29 Size : 1kb User :
[Special Effects] 1_embed DL : 0: DCT水印设I 为载体数据,W 为水印信号, K 为密钥,那么嵌入水印后的载体Iw 可描述为[124 ] : Iw = F( I ,W , K) . (1) 式中, F 表示水印的嵌入策略。-I as a carrier of data, W for the watermark signal, K key, Then the watermark embedded vector can be described as Iw [124] : F = Iw (I, W, K). (1)-, F said watermark embedding strategy.
Date : 2025-12-29 Size : 1kb User : 江力
[Special Effects] 1_recover DL : 0: 设I 为载体数据,W 为水印信号, K 为密钥,那么嵌入水印后的载体Iw 可描述为[124 ] : Iw = F( I ,W , K) . (1) 式中, F 表示水印的嵌入策略。-established as a carrier of data, W for the watermark signal, K key, Then the watermark embedded vector can be described as Iw [124] : F = Iw (I, W, K). (1)-, F said watermark embedding strategy.
Date : 2025-12-29 Size : 1kb User : 江力
[Special Effects] 2_embed DL : 0: 设I 为载体数据,W 为水印信号, K 为密钥,那么嵌入水印后的载体Iw 可描述为[124 ] : Iw = F( I ,W , K) . (1) 式中, F 表示水印的嵌入策略。-established as a carrier of data, W for the watermark signal, K key, Then the watermark embedded vector can be described as Iw [124] : F = Iw (I, W, K). (1)-, F said watermark embedding strategy.
Date : 2025-12-29 Size : 1kb User : 江力
[Special Effects] 3_embed_recover DL : 0: 设I 为载体数据,W 为水印信号, K 为密钥,那么嵌入水印后的载体Iw 可描述为[124 ] : Iw = F( I ,W , K) . (1) 式中, F 表示水印的嵌入策略。-established as a carrier of data, W for the watermark signal, K key, Then the watermark embedded vector can be described as Iw [124] : F = Iw (I, W, K). (1)-, F said watermark embedding strategy.
Date : 2025-12-29 Size : 1kb User : 江力
[Special Effects] improved_gaborfilter DL : 0: Bi-dimensional Gabor filter with DC component compensation This version of the 2D Gabor filter is basically a bi-dimensional Gaussian function centered at origin (0,0) with variance S modulated by a complex sinusoid with polar frequency (F,W) and phase P described by the following equation: G(x,y,S,F,W,P)=k*Gaussian(x,y,S)*(Sinusoid(x,y,F,W,P)-DC(F,S,P)), where: Gaussian(x,y,S)=exp(-pi*S^2*(x^2+y^2)) Sinusoid(x,y,F,W,P)=exp(j*(2*pi*F*(x*cos(W)+y*sin(W))+P))) DC(F,S,P)=exp(-pi*(F/S)^2+j*P) File Id: 13776 Average rating: 0.0 Size: 1 KB # of reviews: 0 Submitted: 2007-01-26 Downloads: 274 Subscribers: 0 Keywords: gabor filter Stiven Schwanz Dias -Bi-dimensional Gabor filter with DC compo .. compensation This version of the 2D Gabor f ilter is basically a bi-dimensional Gaussian f unction centered at origin (0, 0) with variance S modulated by a complex sinuso id with polar frequency (F, W) and phase P described by the following equati on : G (x, y, S, F, W, P) = k* Gaussian (x, y, S)* (Sinusoid (x, y, F, W, P)- DC (F, S, P)), where : Gaussian (x, y, S) = exp (-pi* S* 2 ^ (x ^ 2 y ^ 2)) Sinusoid (x, y, F, W, P) = exp (j* (2* pi* F* (x* cos (W) y* sin (W)) P))) D C (F, S, P) = exp (-pi* (F/S) ^ 2* P j) File Id : 13776 Average rating : 0.0 Size :# 1 KB of reviews : 0 Submitted : 2007-01-26 Downloads : 274 Subscribers : 0 Keywords : gabor filter Stiven Schwanz Dias
Date : 2025-12-29 Size : 1kb User : 石峰
[Special Effects] toolbox_signal DL : 0: This toolbox implements the algorithm in a fairly general way in a C file that can be called from Matlab. It allows to perform the traditional NL-means for denoising (for both B&W and color images) but also to use an arbitrary set of patches to perform the denoising. -This toolbox implements the algorithm in a fairly general way in a C file that can be called f rom Matlab. It allows to perform the traditiona l NL-means for denoising (for both B
Date : 2025-12-29 Size : 1.34mb User : 张文国
[Special Effects] PCA DL : 0: 主成分分析方法（PCA），PCA算法的理论依据是K-L变换，通过一定的性能目标来寻找线性变换W，实现对高维数据的降维。-Principal component analysis (PCA), PCA algorithm is based on the theory of KL transform, through a certain performance targets to find the linear transformation W, the realization of high-dimensional data, dimensionality reduction.
Date : 2025-12-29 Size : 1kb User : 李伟
[Special Effects] ColorFeatures DL : 0: 一种彩色图像边缘检测的算法。实现J.van de Weijer的" Robust Photometric Invariant Features from the Color Tensor"-The quasi-invariant derivatives can be used to suppress undesired edges such as shadow, shading and specular edges. LITERATURE: J. van de Weijer, Th. Gevers, A.W.M Smeulders " Robust Photometric Invariant Features from the Color Tensor" IEEE Trans. Image Processing, vol. 15 (1), January 2006.
Date : 2025-12-29 Size : 379kb User : 张子墨
[Special Effects] kuaisutisheng DL : 0: 针对CT 医学图像和MR I 医学图像成像特点, 提出了基于快速整数提升小波变换的融合方法。在CT 和 MR I 两幅医学图像配准的前提下, 利用提升小波变换把图像分解成低频和高频子图像, 对于小波变换后的高频子图像, 选择区域标准差大的作为融合后的子图像对于低频子图像, 采用加权融合, 最后进行小波逆变换, 得到融合后的图像, 并对融合后图像用信息熵、平均梯度、相关系数的指标进行评价。实验结果表明, 基于快速整数提升小波变换融合中, 小波高低频系数采用不同的规则能够取得更好的融合效果, 其轮廓清晰。该算法能够提升融合后的医学图像信息量, 同时有效地保护图像的细节信息, 在执行时间和图像质量上均优于普通小波算法。-A imed at the characterist ics of CT andMR Imedical images, a new image fu sion al2 go rithm based on the fast in t lif t ing w avelet t ran sfo rm s is p ropo sed. Two o riginal images are decompo sed by the fast in t lif t ing w avelet t ran sfo rm s. The fu sion ru le based on the max imum standard deviat ion value variance is u sed to fu se the h igh f requency sub2image, w h ile a w eigh t2 ed average fu sed ru le is app lied by coeff icien t s of the low f requency. F inally, the fu sion image is recon st ructed fo r perfo rm ing inverse fast in t lif t ing w avelet t ran sfo rm s. The fu sion image is evaluated by en t ropy, average gradien t, and co rrelat ion coeff icien t s. Experimen tal resu lt show s that the fu sion image hasmo re info rmat ion than o riginal images and imp roves the quali2 ty of o riginal images. M eanw h ile, the fu sion image p ro tect s detail characterist ics of the image, thu s the real2t ime p rocess and image qualit ies are w ell than tha
Date : 2025-12-29 Size : 782kb User : 杨颜华
[Special Effects] asmpointtool DL : 0: asm人脸特征点标定辅助工具，核心为利用ASM算法检测人脸特征点，可以通过鼠标和键盘来选择和移动调整特征点，鼠标点击选取特征点，a w s d控制特征点移动。-asm facial feature point calibration aids, the core for the use of ASM algorithm detects facial feature points, via mouse and keyboard to select and move the adjusted characteristic point, the mouse click select the feature points, awsd control feature point moves.
Date : 2025-12-29 Size : 137kb User : 朱丽丽
[Special Effects] Marketing DL : 0: Pro sssc sse aw wwe ssa a w w w w r r r r r
Date : 2025-12-29 Size : 262kb User : El patrón del mal

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