I am doing a project on hand sign recognition on a static image. Can I use just Haar training to accomplish this?
As what I've understood, it is somewhat similar to the concept of neural networks.
Using Haar training maybe help to detect the hand, but not for recognize.
The people use many approaches, so I cannot give a unique. You could make some research using Google Scholar and use the keywords "hand sign", "recognition" and "detection".
Some tips: you need to segment the hand and use some template matching or other method to recognize the format. There is also a project for hand gestures here.
Related
Here is my problem:
I must match two images. One image from the project folder and this folder have over 20.000 images. The other one is from a camera.
What I have done?
I can compare images with basic OpenCV example codes that I found in the documentation. OpenCV Doc I can also compare and find an image by using the hash of my image data set. It is so fast and it is only suitable for 2 exact images. One for query the other one is the target. But they are the same exact image.
So, I need something as reliable as feature matching and as fast as hash methods. But I can't use machine learning or anything on that level. It should be basic. Plus, I'm new to these stuff. So, my term project is on risk.
Example scenario:
If I ever take a picture of an image in my image data set from my computer's screen. This would change many features of the original image. In the case of defining what's in that image, a human won't struggle much but a comparison algorithm will struggle. Such a case leaves lot's of basic comparison algorithm out of the game. But, a machine-learning algorithm could solve the problem but it's forbidden to use in my project.
Needs:
It must be fast.
It must be accurate.
It must be easy to understand.
Any help is okay. A piece of code, maybe an article or a tutorial. Even an advice or a topic title might be really helpful to me.
Once saw this camera model identification challenge on kaggle. This notebook discusses about noise pattern changes with changing devices. May be you should look in to this and other notebooks in that challenge. Thanks!
I know, the object recognition feature is currently not supported by Google's ARCore.
My simple goal: detect cups and show some coffee inside. (Best would be display it live on the phone)
Is there really no way to detect objects?
Do you know any additional computations approaches, which can recognize some objects via ARCore?
Train a CNN. Instead of training image + annotation, use the point cloud + annotation. Is this approach viable?
Any approach, to record the a video + point cloud and compute them on a backend?
Is Snapchat using ARCore?
Are they detecting the face and pose to put the virtual makeup on the mesh?
How is the mesh computed?
I don't expect answers to every question, just ideas.
Maybe, someone knows simular projects, interesting links or something to think about.
Thanks in advance.
I am looking for a way to compare a user submitted audio recording against a reference recording for comparison in order to give someone a grade or percentage for language learning.
I realize that this is a very un-scientific way of doing things and is more than a gimmick than anything.
My first thoughts are some sort of audio fingerprinting, or waveform comparison.
Any ideas where I should be looking?
This is by no means a trivial problem to solve, though there is an abundance of research on the topic. Presently the most successful forms of machine learning in the speech recognition domain apply Hidden Markov Model techniques.
You may also want to take a look at existing implementations of HMM algorithms. One such library in its early stages is ghmm.
Perhaps even better and more readily applicable to your problem is HTK.
In addition to chomp's great answer, one important keyword you probably need to look up is Dynamic Time Warping (DTW). This is the wikipedia article: http://en.wikipedia.org/wiki/Dynamic_time_warping
I am trying to build a collection of Visual similarity between images, being size, angle, color, rotation invariant for Content Based Image Retrieval.Quite agnostic about the platform but .NET, Java or Python are preferred. But if others are available, pls. suggest away.
I am quite familiar with OpenCV Match template and Match shapes. I have also looked at AForge.NET.
p.s. Something similar to http://www.imgseek.net/home would be ideal:
Content Based Image Retrieval is a field of heavy research. Unfortunately it is not my field of research, therefore I am unable to give you an authoritative suggestion on a viable algorithm. But I can give you the website where I would start looking for an answer:
CVPapers - Computer Vision Resource (check out the Computer Vision Paper Indexes)
There is a lot of research going on about gesture recognition. I figured I would narrow this down to the topic of hand gesture recognition (i.e. stationary hand positions, up to as complex and dynamic as sign language recognition).
Considering the image processing techniques available in real-time, such as blob detection, edge detection, point of interest tracking, etc. Coupled with Hidden Markov Models and other comparison AI, what techniques/algorithms would you use to do real-time motion tracking and gesture recognition?
I remember previous colleagues of mine have worked on a similar problem. They have published a conference paper on this topic: Framework for a portable gesture interface. Hope this helps.
I think for being accurate you have to combine all these techniques.
I did such a thing with wii-mote, it recognizes the hand move but not gesture.
We used hidden markov model and it was successful in real time.
Maybe it is not a real answer but you cant know without trying.
well i think you can use active shape models or active appearence models i remember seeing some papers about it