with image processing libraries like opencv you can determine if there are faces recognized in an image or even check if those faces have a smile on it.
Would it be possible to somehow determine, if the person is looking directly into the camera? As it is hard even for the human eye to determine is someone is looking into the camera or to a close point, i think that this will be very tricky.
Can someone agree?
thanks
You can try using an eye detection program, I remember doing back a few years ago, and it wasn't that strong, so when we tilt our head slightly off the camera, or close our eyes, the eyes can't be detected.
Is it is not clear, what I really meant was our face must be facing straight at the camera with our eyes open before it can detect our eyes. You can try doing something similar with a bit of tweaks here and there.
Off the top of my head, split the image to different sections, for each ROI, there are different eye classifiers, for example, upper half of the image, u can train the a specific classifiers of how eyes look like when they look downwards, lower half of image, train classifiers of how eyes look like when they look upwards. and for the whole image, apply the normal eye detection in case the user move their head along while looking at the camera.
But of course, this will be based on extremely strong classifiers and ultra clear quality images, video due to when the eye is looking at. Making detection time, extremely slow even if my method is successful.
There maybe other ideas available too that u can explore. It's slightly tricky, but it not totally impossible. If openCV can't satisfy, openGL? so many libraries, etc available. I wish you best of luck!
Related
I am trying to build a solution where I could differentiate between a 3D textured surface with the height of around 200 micron and a regular text print.
The following image is a textured surface. The black color here is the base surface.
Regular text print will be the 2D print of the same 3D textured surface.
[EDIT]
Initial thought about solving this problem, could look like this:
General idea here would be, images shot at different angles of a 3D object would be less related to each other than the images shot for a 2D object in the similar condition.
One of the possible way to verify could be: 1. Take 2 images, with enough light around (flash of the camera). These images should be shot at as far angle from the object plane as possible. Say, one taken at camera making 45 degree at left side and other with the same angle on the right side.
Extract the ROI, perspective correct them.
Find GLCM of the composite of these 2 images. If the contrast of the GLCM is low, then it would be a 3D image, else a 2D.
Please pardon the language, open for edit suggestion.
General idea here would be, images shot at different angles of a 3D object would be less related to each other than the images shot for a 2D object in the similar condition.
One of the possible way to verify could be:
1. Take 2 images, with enough light around (flash of the camera). These images should be shot at as far angle from the object plane as possible. Say, one taken at camera making 45 degree at left side and other with the same angle on the right side.
Extract the ROI, perspective correct them.
Find GLCM of composite of these 2 images. If contrast of the GLCM is low, then it would be a 3D image, else a 2D.
Please pardon the language, open for edit suggestion.
If you can get another image which
different angle or
sharper angle or
different lighting condition
you may get result. However, using two image with different angle with calibrate camera can get stereo vision image which solve your problem easily.
This is a pretty complex problem and there is no plug-in-and-go solution for this. Using light (structured or laser) or shadow to detect a height of 0.2 mm will almost surely not work with an acceptable degree of confidence, no matter of how much "photos" you take. (This is just my personal intuition, in computer vision we verify if something works by actually testing).
GLCM is a nice feature to describe texture, but it is, as far as I know, used to verify if there is a pattern in the texture, so, I believe it would output a positive value for 2D print text if there is some kind of repeating pattern.
I would let the computer learn what is text, what is texture. Just extract a large amount of 3D and 2D data, and use a machine learning engine to learn which is what. If the feature space is rich enough, it may be able to find a way to differentiate one from another, in a way our human mind wouldn't be able to. The feature space should consist of edge and colour features.
If the system environment is stable and controlled, this approach will work specially well, since the training data will be so similar to the testing data.
For this problem, I'd start by computing colour and edge features (local image pixel sums over different edge and colour channels) and try a boosted classifier. Boosted classifiers aren't the state of the art when it comes to machine learning, but they are good at not overfitting (meaning you can just insert as much data as you want), and will most likely work in a stable environment.
Hope this helps,
Good luck.
I implemented an eye blink detection solution using the research from this article:
http://www.iu.hio.no/~frodes/unitech10/011-Krolak/
I use a Haar eye classifier to identify the two eye regions, then using template matching on both eyes to detect blink state change. I also require that the face and eye regions remain fairly still. It works pretty well, except I occasionally get false positive on photos if I slightly move them (particularly rotate/scale). Does anyone have any suggestions to eliminate such cases? I don't want to make the stillness too strict, because it makes the live case unusable.
I have implemented with some success two cascades, one that detect open eyes and one that detect closed eyes.
You can use the face detector and restrict the search on the eyes region, then apply the "open eye" cascade. The good thing with that approach is that you can add slightly different poses and angles for the eyes on the training set. Worked really well for me.
I have a very specific application in which I would like to try structure from motion to get a 3D representation. For now, all the software/code samples I have found for structure from motion are like this: "A fixed object that is photographed from all angle to create the 3D". This is not my case.
In my case, the camera is moving in the middle of a corridor and looking forward. Sometimes, the camera can look on other direction (Left, right, top, down). The camera will never go back or look back, it always move forward. Since the corridor is small, almost everything is visible (no hidden spot). The corridor can be very long sometimes.
I have tried this software and it doesn't work in my particular case (but it's fantastic with normal use). Does anybody can suggest me a library/software/tools/paper that could target my specific needs? Or did you ever needed to implement something like that? Any help is welcome!
Thanks!
What kind of corridors are you talking about and what kind of precision are you aiming for?
A priori, I don't see why your corridor would not be a fixed object photographed from different angles. The quality of your reconstruction might suffer if you only look forward and you can't get many different views of the scene, but standard methods should still work. Are you sure that the programs you used aren't failing because of your picture quality, arrangement or other reasons?
If you have to do the reconstruction yourself, I would start by
1) Calibrating your camera
2) Undistorting your images
3) Matching feature points in subsequent image pairs
4) Extracting a 3D point cloud for each image pair
You can then orient the point clouds with respect to one another, for example via ICP between two subsequent clouds. More sophisticated methods might not yield much difference if you don't have any closed loops in your dataset (as your camera is only moving forward).
OpenCV and the Point Cloud Library should be everything you need for these steps. Visualization might be more of a hassle, but the pretty pictures are what you pay for in commercial software after all.
Edit (2017/8): I haven't worked on this in the meantime, but I feel like this answer is missing some pieces. If I had to answer it today, I would definitely suggest looking into the keyword monocular SLAM, which has recently seen a lot of activity, not least because of drones with cameras. Notably, LSD-SLAM is open source and may not be as vulnerable to feature-deprived views, as it operates directly on the intensity. There even seem to be approaches combining inertial/odometry sensors with the image matching algorithms.
Good luck!
FvD is right in the sense that your corridor is a static object. Your scenario is the same and moving around and object and taking images from multiple views. Your views are just not arranged to provide a 360 degree view of the object.
I see you mentioned in your previous comment that the data is coming from a video? In that case, the problem could very well be the camera calibration. A camera calibration tells the SfM algorithm about the internal parameters of the camera (focal length, principal point, lens distortion etc.) In the absence of knowledge about these, the bundler in VSfM uses information from the EXIF data of the image. However, I don't think video stores any EXIF information (not a 100% sure). As a result, I think the entire algorithm is running with bad focal length information and cannot solve for the orientation.
Can you extract a few frames from the video and see if there is any EXIF information?
I've built an imaging system with a webcam and feature matching such that as I move the camera around; I can track the camera's motion. I am doing something similar to here, except with the webcam frames as the input.
It works really well for "good" images, but when taking images in really low light lots of noise appears (camera high gain), and that messes with the feature detection and matching. Basically, it doesn't detect any good features, and when it does, it cannot match them correctly between frames.
Does anyone know a good solution for this? What other methods are used for finding and matching features?
Here are two example images with very low features:
I think phase correlation is going to be your best bet here. It is designed to tell you the phase shift (i.e., translation) between two images. It is much more resilient (but not immune) to noise than feature detection because it operates in frequency space; whereas, feature detectors operate spatially. Another benefit is, it is very fast when compared with feature detection methods. I have an implementation available in the OpenCV trunk that is sub-pixel accurate located here.
However, your images are pretty much "featureless" with the exception of the crease in the middle, so even phase correlation may have some trouble with it. Think of it like trying to detect translation in a snow storm. If all you can see is white, you can't tell that you have translated at all, thus the term whiteout. In your case, the algorithm might suffer from "greenout" :)
Can you adjust the camera settings to work better in low-light conditions. Have you fully opened the iris? Can you live with lower framerates? Setting a longer exposure time will allow the camera to gather more light, thus giving you more features at the cost of adding motion blur. Or, if low-light is your default environment you probably want something designed for this like an IR camera, but those can be expensive. Other than that, a big lens and long exposures are your friend :)
Histogram equalization may be of interest in improving the image contrast. But, sometimes it can just enhance the noise. OpenCV has a global histogram equalization function called equalizeHist. For a more localized implementation, you'll want to look at Contrast Limited Adaptive Histogram Equalization or CLAHE for short. Here is a good article on it. This page has some nice examples, and some code.
I have a problem to detect object in images or video frames.
I have a task that is detect some people or something who enter into the sight of web camera, and then my system will be alarm.
Next step is recognize which kind of thing the object is, in this phase I know use Hough transform to detect line, circle, even rectangle. But when a people come into the sight of camera, people's profile is more complex than line, circle and rectangle. How can i recognize the object is people not a car.
I need help to know that.
thanks in advance
I suggest you look at the paper "Histograms of Oriented Gradients for Human Detection" by Dalal and Triggs. They used Histograms of Oriented Gradients to detect humans in images
I think one method is to use Bayesian analysis on your image and see how that matches with a database of known images. I believe some people run a wavelet transform to emphasize more noticeable features.