otarun.blogspot.com
Pixel of Knowledge: August 2009
http://otarun.blogspot.com/2009_08_01_archive.html
On pics, physics, and more! Thursday, August 6, 2009. Activity 12: Color Image Segmentation. Segmentation by thresholding will not always work especially when the area of interest has the same grayscale value as the background. In such cases, segmentation can be done using the color of the image of interest. In this activity, we're going to demonstrate parametric and non-parametric color segmentation. Normalize Chromaticity Coordinates (NCC). Can be obtained from r. Essentially, what this PDF does is to ...
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.
otarun.blogspot.com
Pixel of Knowledge: lessons from drinking sessions..part 1.
http://otarun.blogspot.com/2010/10/lessons-from-drinking-sessionspart-1.html
On pics, physics, and more! Sunday, October 3, 2010. Lessons from drinking sessions.part 1. Nakausap ko si Fidel Rillo habang ako ay nakipag-inuman sa bahay ng aming kaibigan (tawagin natin Mambo) bilang despidida nya papuntang Germany. Nais kong ibahagi ang mga bagay na napagusapan namin. Tinanong ako ng "What is God? Isang sagot ang binigay sa akin ng tatay ng aking kaibigan. "it is the search for truth". It is the search for truth.science and God. Subscribe to: Post Comments (Atom).
ap186-msison.blogspot.com
miguel AP 186 blog: Activity 16 Neural Networks
http://ap186-msison.blogspot.com/2009/09/activity-16-neural-networks.html
Miguel AP 186 blog. Wednesday, September 9, 2009. Activity 16 Neural Networks. Do you remember when I said machines are stupid? Table 1. Classification result and percent accuracy at different learning rates. Figure 1. Classification accuracy versus Neural Network learning rate. Red line highlights range with 100% accuracy. Maricor Soriano, A16 – Neural Networks, AP186 2008. Cole’s AP186 Blog. Subscribe to: Post Comments (Atom). There was an error in this gadget. Activity 17 Photometric Stereo.
ap186-msison.blogspot.com
miguel AP 186 blog: Activity 15 Probabilistic Classification
http://ap186-msison.blogspot.com/2009/09/activity-15-probabilistic.html
Miguel AP 186 blog. Wednesday, September 9, 2009. Activity 15 Probabilistic Classification. LDA calculates the probability, fij, of an object to belong in a class according to the equation. In this equation wi is the feature vector of object i and mj is the mean feature vector of class j. The value pj is the probability or ratio of the number of elements in class j over the total population of the classes. Table 1. Resulting probability of each object per class and classification results. Activity 15 Pro...
ap186-msison.blogspot.com
miguel AP 186 blog: August 2009
http://ap186-msison.blogspot.com/2009_08_01_archive.html
Miguel AP 186 blog. Thursday, August 6, 2009. Activity 12 Color Image Segmentation. Figure 1. Normalize Chromaticity Coordinates. The normalized chromaticity coordinates (NCC), shown in figure 1, can be thought of as a way of expressing 3 dimensional RGB information to a simpler 2 Dimensional r-g color space. The basis for this transform is that r g b=1 (done by normalizing RGB), hence the third value is redundant and it is enough to express color by only two values, in this case r and g. Overall, by loo...
malese-ous.blogspot.com
Malese-ous: ACTIVITY 17 - Stereometry
http://malese-ous.blogspot.com/2009/09/activity-17-stereometry.html
Monday, September 14, 2009. ACTIVITY 17 - Stereometry. The easiest case to work with would be a point source of light. The intensity would depend as 1/r 2, where r is the distance from the point source. The mathematical derivations will not be. Shown here. Just note that we need information on the different positions of the light source relative to the object and the following images. After applying some matrix operations, we obtain the following reconstruction. Figure 2. Reconstruction.
