This is an Elegant and timely attempt about protecting web from exclusive behaviors of some just-profit-seeking companies !!!
I have recently received some emails from some newbies which asked me how to become an expert in Computer Vision(CV). As a matter of fact, I don’t know myself as a CV expert; hence, maybe I can’t show you the door (remember the Morpheus in the Matrix film :) .But as a self-taught software developer who does love this major, I have some proposals for you. Here it comes:
First : Start by the procedure that Robin Hewitt suggests: Getting started in computer vision. She is an independent software developer who is a prominent expert in relevant stuff. Do whatever she proposes !!!!
Second : Become familiar with Matlab and specially C/C++. I can argue more than one hour that you need both C and C++. I do love C , and I can extend CV algorithms with it; but in the industry you do need C++. In the real world applications , you need software engineering approaches beside of algorithms and therefore, you MUST develop softwares in OOP mode. You HAVE to learn it. It’s Compulsory !!
Third : Learn to develop softwares with Python. I don’t want to start to describe it. Just google it….. You will be a better hacker when you use it to develop raw algorithms.
Forth : Start to study Math every day. It’s like an exercise for your brain :) I’m not kidding !!
Fifth : “ Concentrate and NEVER give up!" Take this as a fundamental rule….
Cheers
UPDATE: You can use the python binding for OpenCV too .
Just " from opencv import *" and have a lot of FUN :)
At last, I came back from that hell :). What a bad place was over there…. There are lots of work I must get them done !!!
Bad luck... I'm in a working mission in a very bad natural condition. I had to go to a territory last week which has no water at all ! I'm near to the YAZD city, an ancient city that is near the hottest area in the planet Earth (Loot desert, or Kavir loot in persian)!!!!
def fibonacci(n) :
if n==0 or n==1 :
return 1
else :
return fibonacci(n-1) + fibonacci(n-2)
Since I'm implementing a project in "Face Detection", I have to use advanced methods for "Feature Extraction". I have designed a MLP network for training, but I used raw pictures as an input layer… As maybe you know, it's better idea to decrease the input dimension by using Feature Extraction/Selection approaches.
There is some approaches like :"non Linear PCA" and "Kernel Methods".
I want to use Kernel Methods. I think it's more applicable and most of recent papers that I have read were about this major! However , It's maybe because of my guide attitude (Dr.Araabi) who have focused on it more :) I don't know. But because my learning procedure is "Learning by Doing" I will implement both of them. If you have any prior experience, please email me and let me know…
Cheers :)
