This is my template image:
https://drive.google.com/open?id=151q9lliGc5ySFe96rY6YM4wbVjvbr9uv
This is my test image where in which i am trying to find template image
https://drive.google.com/open?id=1ncop46vbTVTjcwp51GrcQfGX4w1WrzDI
I am able to find good match when the test image is pretty much straight and not angled something like this
https://drive.google.com/open?id=1SY68YXxIpDNyK5UfgRdjomI5bkKKqLWE
but whenever it is angled or slant as in my first test image, i am failing to identify good match points
I am using SIFT for extracting keypoints and descriptors and FLANN based search for matching the keypoints, I am pretty much doing everything according to this https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_matcher/py_matcher.html
what can be done to improve the accuracy of finding a match?
Ordinary feature detectors such as SIFT have limited angular invariance up to 30 degrees. In your case you need much more, so you can use ASIFT algorithm that is able to match features extracted from images which angular difference is much higher. Note that the computational requirements of this method are very high so you need really powerful hardware if you want to use it in real time. Unfortunately it is not included in OpenCV but there are plenty of implementations around the web, for instance on the mentioned website.
Related
The problem statement:
Given two images such as the two images of Brad Pitt below, figure out if the image contains the same person or no. The difficulty is that we have only one reference image for each person and what to figure out if any other incoming image contains the same person or no.
Some research:
There are a few different methods of solving this task, these are
Using color histograms
Keypoint oriented methods
Using deep convolutional neural networks or other ML techniques
The histogram methods involve calculating histograms based on color and defining some sort of metric between them and then deciding upon a threshold. One that I have tried is the Earth Mover's Distance. However this method is lacking in accuracy.
The best approach therefore should be some sort of mix between 2nd and 3rd methods, and some preprocessing.
For preprocessing obvious steps to perform are:
Run a face detection such as Viola-Jones and separate the regions containing faces
Convert the said faces to grayscale
Run eye,mouth,nose detection algorithms perhaps using haar_cascades of opencv
Align the face images according found landmarks
All of this is done using opencv.
Extracting features such as SIFT and MSER generate accuracy of between 73-76%. After some additional research I've come across this paper using fisherfaces. And the fact that opencv has now the ability to create fisherface detectors and train them is great and works fantastically, achieving the accuracy promised by the paper on the Yale datasets.
The complication of the problem is that in my case I don't have a database with several images of the same person, to train the detector on. All I have is a single image corresponding to a single person, and given another image I want to understand whether this is the same person or no.
So what I am interested in knowing is`
Has anyone tried anything of the sort? What are some papers/methods/libraries that I should look into?
Do you have any suggestions on how to tackle problem?
Since you have only one image, you can give this method using DLib a try. I have used 3-4 images per person and it is giving good results.
Detect face (sample_face)
Get face descriptor (128 D vector) using dlib compute_face_descriptor (Check link)
Get the new picture in which you want to recognise the face
detect face and compute the descriptor(lets call test_face).
Compute euclidean distance between test_face descriptor and all sample_faces descriptor
assign the test_face with class(person name) with least euclidean distance.
Give this a whirl, you can play with face aligning if you start getting good results.
This is one of the hot topic for computer visin area. To handle as you have written there are many kind of solutions are available.
But i suggest to look OpenFace which has very high accuracy. There is a implementation of that project at Github.
Thanks
You need to understand that machine learning doesn't work that way, there are intensive training carried out before your model can give some good results.
with the single image of a person you just cannot predict that its the same person, cause you need to train your model over different images of the person under different light intensities, angles and many other varying scenarios.
Still i would like to try this link :
http://hanzratech.in/2015/02/03/face-recognition-using-opencv.html
you may find some match for the image atleast.
So what I am interested in knowing is` Has anyone tried anything of
the sort?
Yes. This is 2017 and facial recognition has been researched for decades.
What are some papers/methods/libraries that I should look into?
Anything google throws at you searching "single image/sample face recognition"
Do you have any suggestions on how to tackle problem?
See above
Extracting features such as SIFT and MSER generate accuracy of between 73-76%.
I doubt humans, who's facial recognition is unmatched perform much better with only 1 image as reference. I mean I couldn't tell for sure if that's Brad Pitt or if one is just a look-alike and I have seen him on houndreds of pictures and hours of movies...
Problem: I have a photo of an object (a manufactured part like the attached photo below), using my Andoird phone camera I want to verify if the object in camera preview matches to the template or not. (in other words, is it the same part as the template or not)
I can make the user to move the camera in order to have similar view of the template in camera preview as the template however there will be different noise level and/or lighting and maybe different background.
Question: What do you recommend me to use for solving this problem? I was thinking of Canny edge extraction and then matching the camera frames towards the canny edge extract from template? is this a good idea? if yes would you please tell me how can I implement this? any resources? samples? (I can do the Canny edge extraction but couldn't find a way to do the matching)
if Not a good idea then what do you recommend?
Things I have tried:
Feature Extract and Matching: I used few different extractor and matcher implementations from OpenCV and my app is working and drawing the detected feature points and matches, etc. however being a beginner with image processing I cannot make sense of the result and also how to know what is a match. any idea, help, good resources?
Template Matching: I used OpenCV template matching however the performance was horrible and I decided that this cannot be the solution.
I tried object recognition with my phone on your test image and the results were positive.
Detector used :ORB(Binary Detector).
Descriptor used :ORB.
Matching Technique : Brute-force matching .
Image Size 640x480.
I was able to detect around 500 feature points (number of keypoints is around sufficient but it might produce false matches when you have more images with similar looking objects.you need to refine your matching to avoid false matches).
Result of object recognition on two different scales.
Regarding you finding difficulties in understanding object recognition. What exactly did you not understand(Specific topic).
I recommend you to go thru the these two books
Learning OpenCV by By Adrian Kaehler, Gary Bradski
OpenCV 2 Computer Vision Application Programming Cookbook by by Robert Laganière(chapter 8 & 9).
Cheers!
from what I understand canny edge detection might not be an optimal solution. according to me after some basic pre-processing of the test image find its sift features and compare it with the sift features of the template. sift being really versatile should work here too.
you can also try opensurf feature they are faster than sift but i havent had an opportunity to work alot with them to be able to comment on its accuracy
what is the best approach to identify a pattern (could be a text,signature, logo. NOT faces,objects,people,etc) in an image, given that all images are taken from the same angle, which means the pattern to identify will be ALWAYS visible at the same angle, but not position / size/ quality / brightness, etc.
Assuming I have the logo, I would like to run a test on 1000 images, from different sizes & quality and get those images that have this pattern embedded or at least a high probability to have this pattern embedded.
Thanks,
Perhaps you can show a couple of images but it seems like template matching (perhaps with a distance transform) seems like an ideal candidate to your problem.
Perl? I'd have suggested using OpenCV with python or C since you're on the Linux platform.
You could check out SURF and SIFT (explains how to do this with OpenCV and C++ with code attached) which can do decent template matching (logos, etc.).
Text detection is a different kettle of fish, I'd suggest Robust Text Detection in Natural Images with Edge-enhanced maximally stable extremal regions paper which is the latest I've seen that does robust text detection from natural scenes without becoming overly intricate.
Training a neural network with the expected patterns seems to be the best way all-round, though the training process will take a long time. Actual identification is almost real-time though.
Here's a discussion on MSER implementation in two libraries: a) OpenCV, b) VLfeat
Have you checked AForgenet.com ? It has great libs for blob processing. Its in .NET
For my project, supposed to segment closest hand region from camera, I initially try openCV's stereovision example. However, disparity map looks very bad and its useless for me.
Is there any other method which is better than openCV implementation and have some output(image-video). Because, my time is limited, I must choose one better algorithm and implement this.
Thank you.
OpenCV implements a number of stereo block matching algorithms some of them pretty cutting edge.
Disparity maps always look bad except in very simple circumstances - the first step is to try and improve the source images, the lighting and the background. I
If it was easy then everybody would eb doing it and there would be no market for expensive 3D laser scanners.
Try the different block matching algorithms provided by OpenCV. The little bit of experimentation I've done so far seems to indicate that cv::StereoSGBM gives better disparity maps than cv::StereoBM, but is slower.
The performance of the block matching algorithms will depend on what parameters they are initialized with. Have a look at the stereo examples again here, notice line 195-222 where the algorithms are initialized.
I also suggest you use some basic GUI (OpenCV:s highgui for example) to manipulate these parameters real-time when finetuning the algorithm.
In brief, what are the available options for implementing the Tracking of a particular Image(A photo/graphic/logo) in webcam feed using OpenCv?In particular i am trying to collate opinion about the following:
Would HaarTraining be overkill(considering that it is not 3d objects but simply Images to be tracked) or is it the only way out?
Have tried Template Matching, Color-based detection but these don't offer reliable tracking under varying illumination/Scale/Orientation at all.
Would SIFT,SURF feature matching work as reliably in video as with static image
comparison?
Am a relative beginner to OpenCV , as is evident by my previous queries on SO (very helpful replies). Any cues or links to what could be good resources for beginning NFT implementation with OpenCV?
Can you talk a bit more about your requirements? Namely, what type of appearance variations do you expect/how much control you have over the environment. What type of constraints do you have in terms of speed/power/resource footprint?
Without those, I can only give some general assessment to the 3 paths you are talking about.
1.
Haar would work well and fast, particularly for instance recognition.
Note that Haar doesn't work all that well for 3D unless you train with a full spectrum of templates to cover various perspectives. The poster child application of Haar cascades is Viola Jones' face detection system which is largely geared towards frontal faces (can certainly be trained for many other things)
For a tutorial on doing Haar training using OpenCV, see here.
2.
Try NCC or better yet, Lucas Kanade tracking (cvCalcOpticalFlowPyrLK which is a pyramidal as in coarse-to-fine LK - a 4 level pyramid usually works well) for a template. Usually good upto 10% scale or 10 degrees rotation without template changes. Beyond that, you can have automatically evolving templates which can drift over time.
For a quick Optical Flow/tracking tutorial, see this.
3.
SIFT/SURF would indeed work very well. I'd suggest some additional geometric verification step to remove spurious matches.
I'd be a bit concerned about the amount of computational time involved. If there isn't significant illumination/scale/in-plane rotation, then SIFT is probably overkill. If you truly need it, check out Changchang Wu's excellent SIFTGPU implmentation. Note: 3rd party, not OpenCV.
It seems that none of the methods when applied alone could bring reliable results unless it is a hobby project. Probably some adaptive algorithm would be more or less acceptable. For example see a famous opensource project where they use machine learning.