Week 2: d3.js and line detection

This week’s meeting focused on talks given by Lisa and Jeeyoung.

Lisa talked first, discussing her latest project using Data-Driven Documents (d3).  The dataset of choice was the Iris dataset, that which any R enthusiast will be familiar with.  It contains five variables: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species.

For those unfamiliar with d3.js, it is a visualization library that provides functionality to bind data to objects.  The advantages of using d3.js is that it is extremely fast and flexible.

Here is a simple example of selecting a circle and updating it with new coordinates and size :

var circle = svg.selectAll(“circle”)
.attr(“cy”, 90)
.attr(“cx”, 30)
.attr(“r”, 40);

But what if we want to make it more interesting.  Consider a data set of size two (i.e [20, 35]).  D3.js makes it easy to add a new circle and visualize the data in such a way that the size and x-coordinate of each circle reflects each data point.

var circle = svg.selectAll(“circle”)
.data([20, 35])
.attr(“cy”, 90)
.attr(“cx”, function(d) {return d;})
.attr(“r”, function(d) {return d;});

Lisa’s project explored multiple visualization strategies including an easy-to-use interface to change the axis of a scatter plot.

Jeeyoung discussed edge detection and line detection.  In edge detection, a user applies an algorithm (that uses a threshold on the directional derivatives) to produce an image with the original’s edges.  One of the more common edge detection algorithms is Canny edge detection (see below: source Wikipedia).

From there, you can apply line detection.  It is easier to do this by mapping each point along the edges to a line in the parameter space (slope-intercept coordinate plane) and looking for intersections.  While the exact details will not be mentioned, the transformation to be used is the Hough Transformation.

Note you can implement both using Matlab commands : edge(.) and hough(.)

As usual come join us in the Stats Club Office every Monday at 4:30pm!

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