can someone tell me how i can detect pictures of architecture or sculpture?
I think hough-transforming is a good approach. But i'm new in CV and maybe there a better methods to detect pattern. I heard about haarcascade. can i take this for architecture,too?
For example i want to detect those kind of pictures:
Image Hosted by ImageShack.us http://img842.imageshack.us/img842/4748/resizeimg0931.jpg
If you want an algorithm to detect them, then detecting an object from an image need a description of that object which can be understood by a machine or computer. For a sculpture or architecture, how can you have such uniform definition since they vary a lot in every sense? For example both your input images vary a lot. How can we differentiate between a house and an architecture? A lot of problems will rise in your question. Even with Hough Transforming, how you are supposed to differentiate a big house and a big architecture?
Check out this SOF : Image Processing: Algorithm Improvement for 'Coca-Cola Can' Recognition
He wants to detect coca-cola cans, and not coca-cola bottles. But if you look into it clearly, you will understand can and bottles are almost alike and it will be difficult to differentiate between them. You can find a lot of its difficulties in subsequent answers. Major problem is that, in some cases, it will be difficult for humans as well to differentiate them.
In your second image, even if you train some cascades for second image, there is a change it will detect live lions if they are present in your image, since a sculpture lion and an original lion seems almost same for a machine.
Haar cascades may not be much effective since you have to train for a lot of these kinds of images.
If you have some sample images and want to check if those things are there in your image, may be you can use SURF features etc. But you may need some sample images first to compare. For a demo of SURF, check out this SOF : OpenCV 2.4.1 - computing SURF descriptors in Python
Another option is template matching. But it is slow, and it is not scale and orientation invariant. And you need some template images for this
I think I have seen some papers relating this topic ( but i don't remember now). May be googling will get you them. I will update the answer if I get it.
Related
I'm working on a project for visually impaired people that converts the visual world to audio.
We prefer to create a prototype that doesn't need an internet connection. So we chose to work with OpenCV. After reading (a lot of) tutorials and documentation we were able to train OpenCV in recognizing specific objects.
For example: we trained OpenCV to recognize a certain chair and a door. That works fine.
But, we also tried to train OpenCV on a "generic" level. It should be possible to recognize (almost) all chairs. We did that by training OpenCV with a lot of positive and negative images as explained here: http://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html
The actual result wasn't what we expected -he could not recognize any chair-. I know, there are a lot of different parameters to take into account (maybe we did something wrong with that) and we experimented a lot. But our time (and unfortunately our knowledge of opencv) is limited.
We are looking for some advice on how to train opencv to recognize generic objects.
Where do we start?
Is opencv even suited to do that?
Thank you for your time!
Open CV is the library to use. But object recognition is tricky. Often when people say they are doing "object recognition" they are not, they are processing one image, or at best a series of related images, to separate into object and background.
To recognise a "chair" - everything from an armchair to a dining chair to a throne - would be almost impossible. I'd want at least stereo images to give a chance to detect flat surfaces. I don't doubt that with a lot of work you can get quite a good result, maybe just recognising dining -style chairs, but it's skilled work, it's not just a case of feeding a few parameters to a hierarchical classifier.
I want to train a new haar-cascade for glasses as I'm not satisfied with the results I'm getting from the cascade that is included in OpenCV.
My main problem is that I'm not sure where to get eyeglasses images. I can manually search and download, but that's not practical for the amount of images I really need. I'm specifically looking for images of people wearing eyeglasses.
As this forum contain many experienced computer vision experts, I hope someone here can guide as to how to obtain images for training.
I'll also be happy to hear other approaches for detecting eyeglasses (on people).
Thanks in advance,
Gil
If you simply want images, it looks like #herhuyongtao pointed you to a good place. Then you can follow opencv's tutorial on training.
Another option is to see what others have trained:
There's a trained data set found here that might be of use, which states simply that it is "better". I'm assuming that it's supposed to be better than opencv.
I didn't immediately see any other places for trained or labeled data.
I would like to use computer vision to do the following:
A camera is mounted outside a building, capturing a videostream of the street below. The camera is installed approximately 5-6 meters above the street.
Whenever a person wearing a certain kind of hat(white, round) is captured by the camera, an event should be triggered.
Which algorithm should I look into to implement this kind of behavior ?
Is this best achieved through training the algorithm with sample data or is there another way to tell it to look for this type of hat ?
Also, how do I use multiple frames of video to increase the quality of detection ?
Edit: Added a picture of the hat
Before we do everything in comments I will start an answer here.
The first link you posted describes a simple color-based detection. You can try that, but it will fail if there are other pixel clusters of similar color in the image. Your idea of combining it with tracking is good: Identify clusters, build trajectories over several images, and only accept plausible trajectories as a hit. For robust tracking you may want to look into Kalman filtering. A problem you will most likely encounter is that a "white" hat will hardly be "white" in the images your camera delivers.
The second link you refer to - boosted Classifiers Based on Haar-like Features - is for detection of more complex objects. It probably won't help you find white blobs. Invest your time and energy in learning about tracking.
I'm happy to repeat myself here: "Solving a computer vision problem" is not something like "sorting an array". OpenCV is not the C++ Standard Library. You can use an std::map without knowing anything about a red-black tree. But (IMHO) you can't use Vision APIs without knowing a good deal of the math and theory. Working solutions Computer Vision are typically heavily tuned towards the specific problem scenario. Sorry if that sounds pedantic, but it explains why your question got beaten.
I am working on a computer vision application and I am stuck at a conceptual roadblock. I need to recognize a set of logos in a video, and so far I have been using feature matching methods like SIFT (and ASIFT by Yu and Morel), SURF, FERNS -- basically everything in the "Common Interfaces of Generic Descriptor Matchers" section of the OpenCV documentation. But recently I have been researching methods used in OCR/Random Trees classifier (I was playing with this dataaset: http://archive.ics.uci.edu/ml/datasets/Letter+Recognition) and thinking that this might be a better way to go about finding the logos. The problem is that I can't find a reliable way to automatically segment an arbitrary image.
My questions:
Should I bother looking into methods other than descriptor/keypoint, or is this the
best way to recognize a typical logo (stylized, few colors, sharp edges)?
How can I segment an arbitary image (or a video frame, in my case) so that I can properly
match against a sample database?
It would seem that HaarCascades work in a similar way (databases of samples), but I
can't figure out how the processes are related. Is there segmentation going on there?
Sorry of these questions are too broad. I'm trying to wrap my head around this stuff with little help. Thanks!
It seems like segmentation is not what you want. I think it has to do more with object detection and recognition. You want to detect the presence of a certain set of logos, in a certain set of images. This doesn't seem related to segmentation which is about labeling surfaces or areas of a common color, texture, shape, etc., although examining segmentation based methods may be useful.
I would definitely encourage you to look at problem and examine all possible methods that can be applied, not only the fashionable ones (such as SIFT, GLOH, SURF, etc). I would recommend you look at older, simpler methods like simple template matching, chamfering, etc.
Haar cascades became popular after a 2000 paper by Viola and Jones used for face detection (similar to what you see in modern point and click cameras). It does sound a bit similar to the problem you are interested in. You should perhaps also examine this part of the problem, but try not to focus too much on the learning part.
I have made a videochat, but as usual, a lot of men like to ehm, abuse the service (I leave it up to you to figure the nature of such abuse), which is not something I endorse in any way, nor do most of my users. No, I have not stolen chatroulette.com :-) Frankly, I am half-embarassed to bring this up here, but my question is technical and rather specific:
I want to filter/deny users based on their video content when this content is of offending character, like user flashing his junk on camera. What kind of image comparison algorithm would suit my needs?
I have spent a week or so reading some scientific papers and have become aware of multiple theories and their implementations, such as SIFT, SURF and some of the wavelet based approaches. Each of these has drawbacks and advantages of course. But since the nature of my image comparison is highly specific - to deny service if a certain body part is encountered on video in a range of positions - I am wondering which of the methods will suit me best?
Currently, I lean towards something along the following (Wavelet-based plus something I assume to be some proprietary innovations):
http://grail.cs.washington.edu/projects/query/
With the above, I can simply draw the offending body part, and expect offending content to be considered a match based on a threshold. Then again, I am unsure whether the method is invariable to transformations and if it is, to what kind - the paper isn't really specific on that.
Alternatively, I am thinking that a SURF implementation could do, but I am afraid that it could give me false positives. Can such implementation be trained to recognize/give weight to specific feature?
I am aware that there exist numerous questions on SURF and SIFT here, but most of them are generic in that they usually explain how to "compare" two images. My comparison is feature specific, not generic. I need a method that does not just compare two similar images, but one which can give me a rank/index/weight for a feature (however the method lets me describe it, be it an image itself or something else) being present in an image.
Looks like you need not feature detection, but object recognition, i.e. Viola-Jones method.
Take a look at facedetect.cpp example shipped with OpenCV (also there are several ready-to-use haarcascades: face detector, body detector...). It also uses image features, called Haar Wavelets. You might be interested to use color information, take a look at CamShift algorithm (also available in OpenCV).
This is more about computer vision. You have to recognize objects in your image/video sequence, whatever... for that, you can use a lot of different algorithms (most of them work in the spectral domain, that's why you will have to use a transformation).
In order to be accurate, you will also need a knowledge base or, at least, some descriptors that will define the object.
Try OpenCV, it has some algorithms already implemented (and basic descriptors included).
There are applications/algorithms out there that you can "train" (like neural networks) and are able to identify objects based on the training. Most of them (at least, the good ones) are not very popular and can only be found in research groups specialized in computer vision, object recognition, AI, etc.
Good luck!