Image processing is a very specific research area in
computer science, while it is also a very popular topic due to its magic
application in daily life. You might not be an expert on complex image
processing algorithms, while you might be very familiar with iPhone’s panorama
App, which can help to take an amazing panorama photo when you go around by
holding your iPhone. Isight camera on iPhone will take several photos during the
process while image processing algorithms will seamlessly put these photos
together automatically and smartly.
The first eight eigenfaces abstracted from a principal component analysis (PCA) of the Ekman and Friesen (1976) faces [1]
How to automatically identify the “principal content” is the
critical problem if we want machines to
help us perform lots of image processing jobs. The basic algorithm to perform
this task is Principle Component Analysis (PCA) [2], which was invented in 1901
by Karl Pearson and has been applied in statistics for a long time. I don’t want to discuss about the detailed
mathematical content of these algorithms, while I would like to
talk a little about the basic idea. Considering the way that used to describe a
point in 2-dimension graph, yes, a pair of x and y axes will be enough to fulfill the job and uniquely describe the
point without any redundant information. For a 3-dimensional object such as a
line, we will naturally use a combination of x, y and z axes to describe. The reason for choosing these two or three mutually
perpendicular axes is that they have the best ability to describe the object in
the given space. In other word, these perpendicular
axes are the “principal component” which can be used to
describe the object in the
most economically way. PCA is such
an algorithm to identify these “principal component” inside a picture. In order
to perform this algorithm on an image, we need to digitalize an image to be
represented by a matrix first. PCA is to perform a kind of matrix decomposition
named SVD (Singular value decomposition) [3] and then determine which set of
“axes” (Eigen vector) are the most
important to keep in order to describe the photo in a most economically
way.
Reference
[1] The Perception of Facial Expressions,
http://www.scourge.fr/mathdesc/documents/facerecog/PerceptionFacialExpression.htm
http://www.scourge.fr/mathdesc/documents/facerecog/PerceptionFacialExpression.htm
[2] Principal component
analysis, http://en.wikipedia.org/wiki/Principal_component_analysis
I find image processing to be an interesting topic. One of the examples that you give is the iPhone’s camera feature of creating a panoramic view, which is a good example of image processing, since it shows that the iPhone can correctly interpret motions and use multiple frames to create one large image. With that said, some of this post’s grammar is stilted, and I was confused at times. I am still not sure what you mean by “sea level” or “principal component”. While I am curious how these algorithms for identifying faces work, I am not sure if I was able to interpret everything that you said in your post.
ReplyDeleteHi Jingmei, well done on your post. You did a thorough research on this topic and it is interesting. Unlike us, computers absolutely "see" images differently. I like how you give the analogy of the sunrise at the sea level, it definitely helps readers to understand the processing algorithm better.
ReplyDeleteThe eigenfaces are clear examples of how images are processed with the PCA method, which is an important technique used in many image processing application. Overall, this is a good post. Well-done
Hey Jingmei,
ReplyDeleteVery thorough post. Again I'm very interested in the topic you're writing about, and the clarity and conciseness of your descriptions how principal content acquisition is accomplished made it possible to walk away with more knowledge. I appreciate how you started with simple analogies and proceeded to provide the technical aspects. Great post!
Hello Jingmei,
ReplyDeleteAs someone who has done work with image processing in relation to artificial intelligence, I found your post to be very interesting. You gave a really great explanation of how Principal Component Analysis works and how it can be used effectively. I also really liked how you used an image that highlighted where the principal components are. I really enjoyed this post, so thanks for sharing!