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Description: 对图像序列进行高斯背景分析，去除背景和噪声，参见文章“Automatic Temporal Segmentation for Content-Based Video Coding”。-right image sequences Gaussian background analysis, and remove background noise, see the article "Automatic Segmentation for Temporal Con tent-Based Video Coding. "
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Size: 1024 |
Author: 张志勇 |
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Description: 数字图像邻域平均法去噪的实现(包括有Ｍ文件和图像)-neighborhood Digital Image Denoising the average realized (including M documents and images)
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Size: 160768 |
Author: zql |
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Description: MATLAB PROGRAMS FOR "VIBRATION SIMULATION USING MATLAB AND ANSYS"
All the M-files which are listed in the book are available on this site. The
ANSYS-related files and some non-listed utility M-files are available for download
from the author s website, www.hatchcon.con.
-MATLAB PROGRAMS FOR "TS SIMULATIO N AND USING MATLAB ANSYS "All the M-files which a 're listed in the book are available on this site. The ANSYS-related files and some non-listed ut ility M-files are available for download from t he author's website, www.hatchcon.con.
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Size: 67584 |
Author: 向长城 |
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Description: This toolbox provides some functions for manipulating planar, closed splines to implement image or video segmentation by means of deformable (or active) contours. Contour topology is managed in a way that should allow changes similar to what can be observed with level sets (merging and splitting but no hole creation). Several objects can be segmented simultaneously in several frames. -This toolbox provides some functions for m anipulating planar. closed spline to implement image or video segm entation by means of deformable (or active) con tours. Contour topology is managed in a way that should allow changes similar to what can be obse rved with level sets (merging and splitting but no hole creation). Several objects can be segme nted simultaneously in several frames.
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Size: 162816 |
Author: zhuboy |
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Description: matlab con robot scara
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Size: 6207488 |
Author: jorgehlopez |
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Description: En este diagrama se encuentran varias curvas de rugosidad relativa constante para determinar el factor de friccion de fanning en funcion del numero de reynolds utilizando la ecuacion de colebrook resuelta por el metodo del punto fijo.
la funcion fanning2 calcula el factor de friccion con tan solo ingresar la rugosidad relativa y el numero de reynols.
Para generar el diagrama se debe terner la funcion fanning2.-En este diagrama se encuentran varias curvas de rugosidad relativa constante para determinar el factor de friccion de fanning en funcion del numero de reynolds utilizando la ecuacion de colebrook resuelta por el metodo del punto fijo.
la funcion fanning2 calcula el factor de friccion con tan solo ingresar la rugosidad relativa y el numero de reynols.
Para generar el diagrama se debe terner la funcion fanning2.
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Size: 43008 |
Author: Nafiou |
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Description: programacion de lcd en matlab con pic16f8-programacion de lcd en matlab con pic16f877
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Size: 15360 |
Author: richi |
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Description: Programa de misioneros y canibales con búsqueda en profundidad
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Size: 8192 |
Author: Alberto Rivera |
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Description: 基于多帧图像插值（Interpolation）技术的方法是SR恢复技术当中最直观
的方法。这类方法首先估计各帧图像之间的相对运动信息，获得HR图像在非均
匀间距采样点上的象素值，接着通过非均匀插值得到HR栅格上的象素值，最后
采用图像恢复技术来去除模糊和降低噪声（运动估计!非均匀插值!去模糊和
噪声）。-In this paper, we propose a novel method for solv-
ing single-image super-resolution problems. Given a
low-resolution image as input, we recover its high-
resolution counterpart using a set of training exam-
ples. While this formulation resembles other learning-
based methods for super-resolution, our method has
been inspired by recent manifold learning methods, par-
ticularly locally linear embedding (LLE). Speci?cally,
small image patches in the low- and high-resolution
images form manifolds with similar local geometry in
two distinct feature spaces. As in LLE, local geometry
is characterized by how a feature vector correspond-
ing to a patch can be reconstructed by its neighbors
in the feature space. Besides using the training image
pairs to estimate the high-resolution embedding, we
also enforce local compatibility and smoothness con-
straints between patches in the target high-resolution
image through overlapping. Experiments show that our
method is very ?exible
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Size: 27595776 |
Author: qianyeyu |
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Description: analisis huella digital, con algortimos realizados en la gui de matlab
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Size: 5825536 |
Author: caroline |
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Description: predictor linela para prosesamiento digital de señ ales en matlab con el algoritmo de durbin
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Size: 1024 |
Author: locky_05 |
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Description: matlab求解热传导方程的几个例子，有注释，适合初学者-matlab solving a few examples of the heat conduction equation, with notes, suitable for beginners
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Size: 4096 |
Author: caihong |
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Description: robot pid matlab con menu incluido
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Size: 8192 |
Author: focel |
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Description: Tutorial de Matlab - cuenta con un indice de Integrales,limites,derivadas,resumen de comandos
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Size: 1395712 |
Author: fernando |
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Description: Realizzazione di un interfaccia grafica in matlab
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Size: 1885184 |
Author: yung86 |
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Description: Prueba1 FletcherReeves
Hernández Santiago José
Maestría en Ciencias de la Computación
Septiembre / 2011
1. Comenzar con un punto arbitrario
2. Calcular Gradiente de Fi
3. Si el Gradiente Fi es igual a 0(converge), termina
4. Si el Gradiente Fi es !=0 continuar
5. Encontrar dirección de búsqueda
Si= -GradienteFi= - Gradiente F(Xi)
6. Determinar la Longitud Optima del incremento lamda(i) en dirección Si
X(i+1)=X(i)+lamda(i)*S(i)= X(i)-lamda(i)*Gradiente F(Xi)
7. Hacer i=2
8. Obtener Gradiente Fi
9. Calcular Si= -GradFi + ( [abs(GradFi)^2]/[abs(GradF(i-1))^2] )*S(i-1)
10. Determinar la Longitud Optima del incremento lamda(i) en dirección Si
X(i+1)=X(i)+lamda(i)*S(i)= X(i)-lamda(i)*Gradiente F(Xi)
7. Verificar Optimalidad de X(i+1)
Si es optimo, detener
Si no es optimo hacer i=i+1 e ir al paso 8- Prueba1 FletcherReeves
Hernández Santiago José
Maestría en Ciencias de la Computación
Septiembre / 2011
1. Comenzar con un punto arbitrario
2. Calcular Gradiente de Fi
3. Si el Gradiente Fi es igual a 0(converge), termina
4. Si el Gradiente Fi es !=0 continuar
5. Encontrar dirección de búsqueda
Si= -GradienteFi= - Gradiente F(Xi)
6. Determinar la Longitud Optima del incremento lamda(i) en dirección Si
X(i+1)=X(i)+lamda(i)*S(i)= X(i)-lamda(i)*Gradiente F(Xi)
7. Hacer i=2
8. Obtener Gradiente Fi
9. Calcular Si= -GradFi + ( [abs(GradFi)^2]/[abs(GradF(i-1))^2] )*S(i-1)
10. Determinar la Longitud Optima del incremento lamda(i) en dirección Si
X(i+1)=X(i)+lamda(i)*S(i)= X(i)-lamda(i)*Gradiente F(Xi)
7. Verificar Optimalidad de X(i+1)
Si es optimo, detener
Si no es optimo hacer i=i+1 e ir al paso 8
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Size: 6144 |
Author: JHSantiago |
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Description: campos de direcciones hechios en matlab con codigo abierto
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Size: 1024 |
Author: danny |
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Description: control neuronal, archivos de simulacion y pruebas de resultados graficos con tutorial de redes neuronales
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Size: 1196032 |
Author: roimar90
|
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Description: calcula el histograma de una funcion en matlab. con una imagen para poder probarlo
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Size: 110592 |
Author: Patri
|
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Description: ecualizador en matlab con 10 bandas lo que permite ecualizar ademas de un sintetizador
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Size: 3876864 |
Author: Cjguaman |
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