1. By changing the spacing threshold for lines, our coordinated particle system can provide different effects while the behavior of particles still stays the same. Here are some results that we tested (note that some of the images below need to be zoomed in to observe):
Take an input image, we use one particle group release particles inside of foreground objects, while another group take care of the background. The two particle groups will not interrupt with each other, and each particle only performs coordinated movement with members inside a same group.
Implementing some of the ideas discussed in my previous post (http://gigl.scs.carleton.ca/node/545), I created a version of the leaky integration cumulative range geodesic filter which provides great flexibility for adaptive mask termination. The filter program uses the following algorithm for adaptive mask termination:
Dr. Mould has previously examined procedures for controlling filtering mask expansion when the mask size is not constant throughout the image. The results are contained in his article on cumulative range geodesic filtering, available at http://gigl.scs.carleton.ca/node/481
In summary, Dr. Mould found that the following method applied the appropriate amount of smoothing depending on the degree to which a pixel was an outlier or part of an outlier region in the image:
Continuing the discussion of outlier removal from my previous post (http://gigl.scs.carleton.ca/node/543), I am demonstrating a case where the poor noise removal characteristics of the leaky integration filter is an advantage.
In this post, leaky integration refers to a calculation similar to integration, except that the integrand is multiplied by an increasing exponential function. The exponential function has a value of 1 at the upper boundary of integration.
To produce the results shown in this post, I created a version of the cumulative range geodesic filter which uses leaky integration as follows: The distance to each pixel in a filtering mask is equal to the incremental distance between the current pixel and the previous pixel along the path from the source pixel, added to the leaky sum of the incremental distances between the other pixels along the path.
In my last few posts, I explored global image recolouration, where I attempted to find some measurement of how "interesting" a pixel's original colour is and assign "uninteresting" pixels a new colour. I intended to insert features of a new image (a solid colour, in most cases), in a manner which did not eliminate the essential features of the original image.
In this post, I am presenting results from local image recolouration. Local recolouration involves blending the source image with a new image only in a specific region.
Recolouring an image by averaging the image with a solid colour, as shown in my last two posts (http://gigl.scs.carleton.ca/node/538 and http://gigl.scs.carleton.ca/node/539), is a simple way to add contrast and visual interest to an image, provided that the average is not uniformly weighted across the image. I experimented with recolouration as kind of an end in itself, but also with the intention of predicting the results of a more general process, image retexturing.
The "residual" from filtering, as used in this post, is the subtraction of the filtered image from the original image. Larger magnitudes in the residual indicate areas of higher-frequency colour variation or outliers in the source image.