How to detect a paper in real time in android - android

I am currently using Android Scanner which detects the edges of a paper if it is found in a picture, how do I achieve the same thing in real time? Is there a similar library available?

Found this great application, exactly fits my needs, just posting it here if someone else out there is looking for the same thing.
https://github.com/ctodobom/OpenNoteScanner

Related

Facial expressions identification like snapchat

I am working on app that detect eye blink of the user. I have been searching the web for 2 days but still don't have clear vision about how this can be done.
As far as i have knew is that the system supports face detection which is detecting if there is a face in the picture and locating it.
But this works only with images and detect only faces which is not what i need. I need to open an camera activity and directly detect the face of the user and locate his eyes and other facial parts and wait till he blinks, like when you long click on the screen on snap chat.
I have seen a lot about open-cv but still not sure what it is or how to use it or if it seize my goals.
Note: snap chat has no API released for the technology used, and even it doesn't let anyone to talk to the engineers behind this technology.
I know that openCV has the ability to allow image processing on the device's camera feed (as opposed to only being able to process still images).
Here is an introductory tutorial on eye detection using openCV:
http://romanhosek.cz/android-eye-detection-and-tracking-with-opencv/
If you can't find eye-blink detection tutorials in a google search, I think you'll have to create the code for eye-blink detection on your own, but I think openCV will be a helpful tool in doing so. There are lots of beginner openCV tutorials to help you get started.

Is there any way to filter out specific wavelengths

Please forgive me if this question had been asked - I searched, but have been unable to find this, so again, please forgive if already asked.
What I'm wondering is if there is any way to filter out specific wavelengths of light programatically through a smartphone camera (ios or Android although ios is preferred). This is a newer area for me, and while I've never built any camera apps, I have built other apps for ios.
I know that I can apply a physical filter in front of the lens to achieve results similar to what I want, but I'm wondering if I can use code to dispense with wavelengths outside my specific focus, or at least enhance the wavelength I am interested in that would drown out the others. The wavelengths in question are from 600nm - 640nm which is a small set, but it's captured normally by the camera. So, is it possible to focus on that set of wavelengths and filter out all others using code and not a physical attachment to the camera?
Any help would be greatly appreciated! Thank you!
And, again, if I have posted in the wrong area or incorrectly, please let me know and I will fix it.
Thanks!

How do I detect the exact shape of a human face in Android?

I have a project to implement a Snapchat Lenses-like face recognition and distortion algorithm. So far I've tried Android's default face detection API, the play-services-vision face detection API, and OpenCV/JavaCV, but they seem to only detect the location of faces and features and not describe the exact shape of the faces.
Is there anything I missed from these libraries that will allow me to do total face recognition that does describe the exact shape of the faces?
P.S. Should I ask this in Superuser instead?
That is a problem with a solution still in progress in the area of Digital Image Processing (very good solutions available, but not the best one defined yet).
I can think of one simple solution, that might work. Once you have the features of the face (such as eyes, lips, etc), you can retrieve the color of the skin surrounding it (using a pre-defined window, considering the size of the eyes/lips block). Then, you use the retrieved color to feed a border/edge recognition algorithm.
All of this can be easily done with OpenCV, but I cannot assure the method's accuracy, since I have not tried this myself.
Also, maybe Canny's method could be useful for your application.
But I strongly suggest you to search for contemporary papers on this subject, as methods for face recognition are getting better and better, but it can lead to very complex and efficiency-expensive alternatives.
Take a look at the link below, and search for any paper that leads a method in doing what you want:
http://ieeexplore.ieee.org/Xplore/home.jsp
Use keywords such as "face border recognition", "border recognition", "face edge detection", or similar. It's highly probable someone has already done this and then you won't have to reinvent the wheel!

Getting Face From The Picture

Here is what i have done till now.
I have launched the camera from my application
I have taken a photo
I have used the face detection technique to paint a square green window in the face region
Now I am wondering, how will i extract the exact face using face detection?
I have gone through a number of questions over stack overflow related to this problem, unfortunately, the solutions are not accurate enough.
I mean, I have seen solutions like performing cropping based on circular window, but I somehow need to get the exact face extracted from the image. Is it possible? How? Whats the best possible solution available to do this? Any code snippets would be appreciated.

Android image processing and pattern detection

I'm trying to develop an android app that is capable of detecting led patterns on a screen in order to transmit data.
The screen that the phone will be looking at is a simple 5x19 array of red LEDs. I would like to be able to display numbers, most likely in binary and have the app detect the lit LEDs and determine the number being displayed and their pattern. This would probably require fiducial s similar to those used by QR codes.
Does anyone know what I will need to perform this type of image processing? Are there any good libraries?
Thanks, Thomas
OpenCV has been ported to Android. Check it out

Categories

Resources