Yizhan Ao Yingqiao Gou
Color Segmentation using GMM color segmentation, bayer filter, image acquisition, color-space
Estimating p(Cl|x) directly is too difficult. Luckily, we have Bayes rule to rescue us! Bayes rule applied onto p(Cl|x) gives us the following: p(Cl|x)=p(x|Cl)p(Cl)∑li=1p(x|Ci)p(Ci) p(Cl|x) is the conditional probability of a color label given the color observation and is called the Posterior. p(x|Cl) is the conditional probability of color observation given the color label and is generally called the Likelihood.
In this case, one has to come up with a wierd looking fancy function to bound the color which is generally mathematically very difficult and computationally very expensive.
Stitching multiple images seemlessly to create a panorama
local classifiers, color confidence, shape confidence, local boundary deformation
To initialize the color model, we followed the process in Video Snap Cut and converted the input image to Lab color space.
To estimate the large amounts of motion in the object, we used detectSURFFeatures, which is rotational invariant. We tried to force matching to focus on the foreground by removing the background (setting pixels to NaN) but this often resulted in the algorithm not finding enough matching points.
To track small bits of motion, the transformation alone was not enough. We used the optical flow to account for these small changes.
We first convert the image to Lab color space. For each window, we calculated the probability a pixel is foreground or background using pdf of the old GMMs and the current image.
Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM)