This series of tutorials are awesome!

(Warning: this post might contain nudity which is inappropriate for underage audience) 

Many of you probably have seen or worked with this image sometime in your experience working with image and signal processing.

It is a widely used images in the academic world and of course, has been cited or referenced countless times in all kinds of video/blog tutorials. But have you ever asked, who is this young lady in this picture and why everyone is using this picture, and even, why this image even came into the dull and male-dominated circle 🙂

1. Who is her

This lady is named Lena Söderberg who showed up in the playboy magazine back in the 1970s. That photo was taken almost 40 ~ 50 years ago!  It is actually not that hard to find some images out in the public internet of the raw magazine photo.

2. How and Why her?

In a nutshell, it was a gentlemen named Alex Sawchuck who randomly found a copy of magazine and cropped the shoulder above piece to be scanned and then used in a paper later on.

“The Original Sin

Alexander Sawchuk estimates that it was in June or July of 1973 when he, then an assistant professor of electrical engineering at the USC Signal and Image Processing Institute (SIPI), along with a graduate student and the SIPI lab manager, was hurriedly searching the lab for a good image to scan for a colleague’s conference paper. They had tired of their stock of usual test images, dull stuff dating back to television standards work in the early 1960s. They wanted something glossy to ensure good output dynamic range, and they wanted a human face. Just then, somebody happened to walk in with a recent issue of Playboy.

The engineers tore away the top third of the centerfold so they could wrap it around the drum of their Muirhead wirephoto scanner, which they had outfitted with analog-to-digital converters (one each for the red, green, and blue channels) and a Hewlett Packard 2100 minicomputer. The Muirhead had a fixed resolution of 100 lines per inch and the engineers wanted a 512 ✕ 512 image, so they limited the scan to the top 5.12 inches of the picture, effectively cropping it at the subject’s shoulders.” – CMU

There is also a very good presentation on slideshare where you can find the origins of all the interesting stories behind some of the popular or even famous images.

Anyway, next time when you saw that image, you might infuse your research with a big more imagination after knowing the story behind it.

References:

[1] Lena Story

[2] Professional Communication Society Newsletter

[3] Lenna Wikipedia

[4] Top Model in Computer Graphics

Today I was taking the deep learning course from Udacity, they use numpy very often in that class and here are some notes about a few handy numpy functions that I learned.

np.arange

arange is not a typo, it is simply a function very much like built-in range function but return a ndarray instead of list, that is probably why it is called a(rray)range. Here is the source code for arange in case you are interested.

In [33]: np.arange(-1, 1, 0.5)
Out[33]: array([-1. , -0.5,  0. ,  0.5])

In [51]: np.arange(12).reshape((3,4))

Out[51]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

np.vstack

v(ertically)stack all the passed ndarray/list into a new array.

In [37]: np.vstack([[1,2], [3,4], [5,6], [7,8]])

Out[37]: 
array([[1, 2],
       [3, 4],
       [5, 6],
       [7, 8]])

At the same time, you have other sibling utility functions like hstack, concatenate ..etc. that have a very similar usage.

In [38]: np.hstack([[1,2], [3,4], [5,6]])
Out[38]: array([1, 2, 3, 4, 5, 6])

np.sum

the sum is pretty straight-forward, summing up all the numbers, however, there is one pitfall that I fell over is did not pay attention to the argument ‘axis‘. 0 means column wise and 1 means row wise.

In [53]: x

Out[53]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

In [54]: x.sum(0)

Out[54]: array([12, 15, 18, 21])

In [55]: x.sum(1)

Out[55]: array([ 6, 22, 38])