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Sunday, October 24, 2010. Determination of grain size. Wala nang grades at stress this time. Plate 1. Unknown Sample. Mean Lineal Intercept Method (Heyn's Method). Image processing using Scilab. Mean Lineal Intercept Method (Heyn's Method). Image processing using Scilab. Saturday, October 3, 2009. A18 – Noise models and basic image restoration. In this activity this is what I did:. 1 Add noise to the original image:. 2 Filter the noisy images. Assuming a rectangular sub-image with of size. All the filter...

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Sunday, October 24, 2010. Determination of grain size. Wala nang grades at stress this time. Plate 1. Unknown Sample. Mean Lineal Intercept Method (Heyn's Method). Image processing using Scilab. Mean Lineal Intercept Method (Heyn's Method). Image processing using Scilab. Saturday, October 3, 2009. A18 – Noise models and basic image restoration. In this activity this is what I did:. 1 Add noise to the original image:. 2 Filter the noisy images. Assuming a rectangular sub-image with of size. All the filter...

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carmilumban_ ap186 | carmilumban-ap186.blogspot.com Reviews

https://carmilumban-ap186.blogspot.com

Sunday, October 24, 2010. Determination of grain size. Wala nang grades at stress this time. Plate 1. Unknown Sample. Mean Lineal Intercept Method (Heyn's Method). Image processing using Scilab. Mean Lineal Intercept Method (Heyn's Method). Image processing using Scilab. Saturday, October 3, 2009. A18 – Noise models and basic image restoration. In this activity this is what I did:. 1 Add noise to the original image:. 2 Filter the noisy images. Assuming a rectangular sub-image with of size. All the filter...

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carmilumban-ap186.blogspot.com

1

carmilumban_ ap186: October 2010

http://www.carmilumban-ap186.blogspot.com/2010_10_01_archive.html

Sunday, October 24, 2010. Determination of grain size. Wala nang grades at stress this time. Plate 1. Unknown Sample. Mean Lineal Intercept Method (Heyn's Method). Image processing using Scilab. Mean Lineal Intercept Method (Heyn's Method). Image processing using Scilab. Subscribe to: Posts (Atom). View my complete profile. Determination of grain size. Ethereal template. Powered by Blogger.

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carmilumban_ ap186: A19 – Restoration of blurred and noisy image

http://www.carmilumban-ap186.blogspot.com/2009/10/a19-restoration-of-blurred-image.html

Saturday, October 3, 2009. A19 – Restoration of blurred and noisy image. Objective: Be able to restore the. Original image from a degraded image using Wiener filter. Or the product of FT of f(image) and h (spatial degradation function) that is G(. Where F(u,v) is the Fourier transform of the original image and H(u,v) is the trasfer function given by. Where a and b is the total distance for which the image has been displaced in the x and y direction. 2Add noise to this blurred image. Here we notice that a...

3

carmilumban_ ap186: June 2009

http://www.carmilumban-ap186.blogspot.com/2009_06_01_archive.html

Monday, June 29, 2009. Activity 4 Enhancement by Histogram Manipulation. The histogram of a grayscale image is equal to the graylevel probability. Distribution function (PDF) if normalized by the total number of pixels. As such,. The grayscale PDF of an image can be modified in the same way one can modify. The PDF of random numbers. Histogram manipulation is one way of improving the quality of an image,. Enhancing certain image features, or mimicking the response of different imaging. The CDF. Then.

4

carmilumban_ ap186: October 2009

http://www.carmilumban-ap186.blogspot.com/2009_10_01_archive.html

Saturday, October 3, 2009. A18 – Noise models and basic image restoration. In this activity this is what I did:. 1 Add noise to the original image:. I applied the following noises using genradn and imnoise in Scilab and obtained their corresponding PDF p(z):. 2 Filter the noisy images. I made and then applied the following filters on each type of noise in (1). Assuming a rectangular sub-image with of size. N=m=3( for nearest neighbor perimeter). 3 Determined which type of filter suited for each type noise.

5

carmilumban_ ap186: Determination of grain size

http://www.carmilumban-ap186.blogspot.com/2010/10/determination-of-grain-size.html

Sunday, October 24, 2010. Determination of grain size. Wala nang grades at stress this time. Plate 1. Unknown Sample. Mean Lineal Intercept Method (Heyn's Method). Image processing using Scilab. Mean Lineal Intercept Method (Heyn's Method). Image processing using Scilab. Subscribe to: Post Comments (Atom). View my complete profile. Determination of grain size. Ethereal template. Powered by Blogger.

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ccesporlas-ap186.blogspot.com

CINDY 186: Activity 18 | Noise Models and Basic Image Restoration

http://ccesporlas-ap186.blogspot.com/2009/10/activity-18-noise-models-and-basic.html

Monday, October 5, 2009. Activity 18 Noise Models and Basic Image Restoration. Image restoration is a basic image processing application where an image that has been degraded or damaged is being recovered. To recover the image, the degradation must be known and thus can be used in restoring the image. Figure 1. PDFs of different noise types. Figure 2. Types of filtering used. Figure 3. Test image and its PDF. Figure 4 shows the noisy image for the added Gaussian noise with mean = 0.5 and standard dev...

ccesporlas-ap186.blogspot.com

CINDY 186: Activity 17 | Photometric Stereo

http://ccesporlas-ap186.blogspot.com/2009/09/activity-17-photometric-stereo.html

Wednesday, September 9, 2009. Activity 17 Photometric Stereo. Figure 1. Photometric Stereo. Equation 1. Intensity at point P(x,y). In this activity, we use four images of an object taken with different light source positions and use it to reconstruct the 3D structure of the illuminated area, as shown by Figure 2. Figure 2. Images of object at different light source positions. To calculate for the surface normal of the object we need to get the far away point source locations given by V. Buno, Luis III.

malese-ous.blogspot.com

Malese-ous: June 2009

http://malese-ous.blogspot.com/2009_06_01_archive.html

Monday, June 29, 2009. Activity 4 - Enhancement by Histogram Manipulation. Contrast is an important characteristic of images. It enables us to distinguish details. However, conditions may not be optimized to meet the desired contrast. It is often necessary to digitally process images to enhance its appearance. I rate this entry 10 for completing the activity and successfully backprojecting, which was the tricky part in my opinion. Xbasc();/ to clear image window. Old=trial(i);/ enhanced image. New=(old-d...

malese-ous.blogspot.com

Malese-ous: July 2012

http://malese-ous.blogspot.com/2012_07_01_archive.html

Wednesday, July 25, 2012. So I've been away for a really long time.This was an academic blog turned personal blog turned I dunno. As of today, I've decided to use this blog for posting game dev related topics because, hey, I'm a game developer now! Who would have thought? Definitely not the past me. Anyway, I hope I get to keep my promise this time. This only serves as an intro, so nothing game dev related here yet. So there! Subscribe to: Posts (Atom).

adrian-ap186.blogspot.com

Adventures of a Bookworm: June 2009

http://adrian-ap186.blogspot.com/2009_06_01_archive.html

Adventures of a Bookworm. Adrian's Applied Physics 186 Blog. Monday, June 29, 2009. Activity 4. Enhancement by Histogram Manipulation. This activity focused on the enhancement of a low resolution image using histogram manipulation. The activity was started by selecting and downloading a low contrast image from the internet. The image is shown below. The image was then resized and converted into grayscale using the im2gray. From the images enhanced using linear and nonlinear function, it can be observed t...

gilbert-ap186.blogspot.com

Liknayang Inaplay 186: September 2009

http://gilbert-ap186.blogspot.com/2009_09_01_archive.html

Gilbert's Applied Physics 186 Blog. Thursday, September 24, 2009. Activity 19 - Restoration of Blurred Image. This last activity is about demonstration of restoration of a corrupted grayscale image with a known degradation function which in this case is motion blur and additive noise using Weiner filtering. The following schematic diagram is a model of the image degradation and restoration process. From the diagram, f hat(x, y). Here, h(x, y). Represents the degradation function, f(x, y). The power spect...

gilbert-ap186.blogspot.com

Liknayang Inaplay 186: August 2009

http://gilbert-ap186.blogspot.com/2009_08_01_archive.html

Gilbert's Applied Physics 186 Blog. Thursday, August 27, 2009. Activity 15 - Probabilistic Classification. This activity is similar to the previous one except that another method is implemented here. Objects are grouped here using probabilistic classification. Instead of 3, only 2 classes are considered here which are all from Activity 14. Rice crackers and Superthin. Biscuits. As assembled previously, 10 samples represent each class. These are shown below. Is assigned to class i. That has maximum f i.

gilbert-ap186.blogspot.com

Liknayang Inaplay 186: July 2009

http://gilbert-ap186.blogspot.com/2009_07_01_archive.html

Gilbert's Applied Physics 186 Blog. Thursday, July 30, 2009. Activity 11 - Color Image Processing. After previous activities of processing grayscale and binary-type images, this time, color images are handled here specifically true color images which are taken by a digital color camera using its various white balancing (WB) settings. From the expressions above, rho. Corresponds to the spectral reflectance of a surface, S. Is the spectral sensitivity of a color camera in the RGB channels. Meanwhile, K.

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carmilumban_ ap186

Sunday, October 24, 2010. Determination of grain size. Wala nang grades at stress this time. Plate 1. Unknown Sample. Mean Lineal Intercept Method (Heyn's Method). Image processing using Scilab. Mean Lineal Intercept Method (Heyn's Method). Image processing using Scilab. Saturday, October 3, 2009. A18 – Noise models and basic image restoration. In this activity this is what I did:. 1 Add noise to the original image:. 2 Filter the noisy images. Assuming a rectangular sub-image with of size. All the filter...

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