I'm trying to detect shapes written on a whiteboard with a black/blue/red/green marker. The shapes can be circles, rectangles or triangles. The image can be found at the bottom of this post.
I'm using OpenCV as the framework for the image recognition.
My first task is to research and list the different strategies that could be used for the detection. So far I have found the following:
1) Grayscale, Blur, Canny Edge, Contour detection, and then some logic to determine if the contours detected are shapes?
2) Haar training with different features for shapes
3) SVM classification
4) Grayscale, Blur, Canny Edge, Hough transformation and some sort of color segmentation?
Are there any other strategies that I have missed? Any newer articles or tested approaches? How would you do it?
One of the test pictures: https://drive.google.com/file/d/0B6Fm7aj1SzBlZWJFZm04czlmWWc/view?usp=sharing
UPDATE:
The first strategy seems to work the best, but is far from perfect. Issues arise when boxes are not closed, or when the whiteboard has a lot of noise. Haar training does not seems very effective because of the simple shapes to detect without many specific features. I have not tried CNN yet, but it seems most appropriate to image classification, and not so much to detect shapes in a larger image (but I'm not sure)
I think that the first option should work. You can use fourier descriptors in order to classify the segmented shapes.
http://www.isy.liu.se/cvl/edu/TSBB08/lectures/DBgrkX1.pdf
Also, maybe you can find something useful here:
http://www.pyimagesearch.com/2016/02/08/opencv-shape-detection/
If you want to try a more challenging but modern approach, consider deep learning approach (I would start with CNN). There are many implementations available on the internet. Although it is probably an overkill for this specific project, it might help you in the future...
Related
I've been processing some image frames in videos and I discovered that sometimes one or two frames of the video will have artifacts or noise like the images below:
The artifacts look like abrasions of paint with noisy colors that covers only a small region (less than 100x100 in a 1000x2000 frame) of the image. I wonder if there are ways to detect the noisy frames? I've tried to use the difference of frames with SSIM, NMSE or PSNR but found limited effectiveness. Saliency map (left) or sobel/scharr filtering (right) providing more obvious view but regular borders are also included and I'm not sure how to form a classifier.
Scharr saliency map:
Since they are only a few frames in videos it's not quite necessary to denoising and I can just remove the frames one detected. The main problem here is that it's difficult to distinguish those frames in playing videos.
Can anybody offer some help here?
Detailing the comment as an answer with a few more details:
The Scharr and saliency map looks good.
Thresholding will result in a binary image which can be cleaned up with morphological filters (erode to enhance artefacts, dilate to 'erase' gradient contours).
Finding contours will result in lists of points which can be further processed/filtered using contour features.
If the gradients are always bigger than the artefacts, contour features, such as the bounding box dimensions and aspect ratio should help segment artefact contours from gradient contours (if any: hopefully dilation would've cleaned up the thresholded/binary image).
Another idea could be looking into oriented gradients:
either computer the oriented gradients (see visualisations): with the right cell size you might strike a balance where the artefacts have a high magnitude while gradient edges don't
you could try a full histogram of oriented gradients (HoG) classifier setup (using an SVM trained on histograms (as features))
The above options do rely on hand crafted features/making assumptions about the size of artefacts.
ML could be an interesting route too, hopefully it can generalise well enough.
Depending how many example images you have available, you could test a basic prototype using Teachable Machine (which behind the scenes would apply KNN to a transfer learning layer on top of MobileNet (or similar net)) fairly fast.
(Note: I've posted OpenCV Python links, but there are libraries that can help (e.g. scikit-image, scikit-learn, kornia, etc. in Python, cvv in c++, BoofCV in java, etc. (and there might be toolboxes for Matlab/Octave with similar features))
I have the following task: recognize a set of simple hand-drawn shapes on a sheet of paper from a still image (not a video stream), so they might not be exactly identically pixelwise.
Those shapes are basically symbols for doors, windows, etc. in a floor plan (see attached image), so they might be slightly scaled or rotated (90° steps).
There are about 5 different ones.
So far I came across SIFT (and its OpenCV-variants SURF and ORB) as well as a cascaded classifier to recognize haar-like features.
For SIFT there seem to be too little key points in such a shape whereas I did not manage to get the haar-trained cascaded classifier to work. Also, a cascaded classifier seems a bit heavy for recognizing such simple shapes, no?
Does anyone of you have any good hints or alternative approaches? Or maybe you even have a snippet of code lying around which I can use?
I think histograms of gradients (HOG) should work great for such elements.
I am working on a project that aims to build a program which automatically gives a relatively accurate detection of pupil region in eye pictures. I am currently using simplecv in Python, given that Python is easier to experiment with. Since I just started, the eye pictures I am working with are fairly standardized. However, the size of iris and pupil as well as the color of iris can vary. And the position of the eye can shift a little among pictures. Here's a picture from wikipedia that is similar to the pictures I am using:
"MyStrangeIris.JPG" by Epicstessie is licensed under CC BY-SA 3.0
I have tried simple thresholding. Since different eyes have different iris colors, a fixed thresholding would not work on all pictures.
In addition, I tried simplecv's build-in sobel and canny edge detection, it's not working especially for eyes with darker iris. I also doubt that sobel or canny alone can solve the problem, given sometimes there are noises on the edge of the pupil (e.g., reflection of eyelash)
I have entry-level knowledge about image processing and machine learning. Right now, I am thinking about three possibilities:
Do a regression on the threshold value base on some variables
Make a specific mask only for edge detection for the pupil
classification on each pixel (this looks like lots of work to build the training set)
Am I on the right track? I would like to reach out to anyone with more experience on this type of problem. Any tips/suggestions are more than welcome. Thanks!
I think that for start you should put aside the machine learning. You have so much more to try in "regular" computer vision.
You need to try and describe a model for your problem. A good way to do this is to sit and think how you as a person detect iris. For example, i can think of:
It is near the center of image.
It is is Brown/green/blue circle, with distinct black center, surrounded by mostly white ellipse.
You have a skin color around the white ellipse.
It can't be too small or too large (depends on your images..)
After you build your model, try to find better ways to find these features. Hard to point on specific stuff, but you can start from: HSV color space, Correlation, Hough transform, Morphological operations..
Only after you feel you have exhausted all conventional tools, start thinking on features extraction and machine learning..
And BTW, because you are not the first person that try to detect iris, you can look at other projects for ideas.
I have written a small matlab code for image (link you have provided), function which i have used is hough transform for circle detection, which has also implemented in opencv, so porting will not create problem, i just want to know that i am on write way or not.
my result and code is as follows:
clc
clear all
close all
im = imresize(imread('irisdet.JPG'),0.5);
gray = rgb2gray(im);
Rmin = 50; Rmax = 100;
[centersDark, radiiDark] = imfindcircles(gray,[Rmin Rmax],'ObjectPolarity','dark');
figure,imshow(im,[])
viscircles(centersDark, radiiDark,'EdgeColor','b');
Input Image:
Result of Algorithm:
Thank You
Not sure about iris classification, but I've done written digit recognition from photos. I would recommend tuning up the contrast and saturation, then use a k-nearest neighbour algorithm to classify your images. Depending on your training set, you can get as high as 90% accuracy.
I think you are on the right track. Do image preprocessing to make classification easier, then train an algorithm of your choice. You would want to treat each image as one input vector though, instead of classifying each pixel!
I think you can try Active Shape Modelling or if you want a really feature rich modelling and do not care about the time it takes execute the algorithm you can try Active appearance modelling. You might want to look into these papers for better understanding:
Active Shape Models: Their Training and Application
Statistical Models of Appearance for Computer Vision - In Depth
For a project I've to detect a pattern and track it in space despite rotation, noise, etc.
It's highlighted with IR light and recorded with an IR camera:
Picture: https://i.stack.imgur.com/RJuVS.png
As on this picture it will be only very simple shape and we can choose which one we're gonna use.
I need direction on how to process a recognition of these shapes please.
What I do currently is thresholding and erosion to get a cleaner shape and then a contour detection and a polygon approximation.
What should I do then? I tried hu-moments but it wasn't good at all.
Could you please give me a global approach to recognize and track such pattern in space?
Can you choose which shape to project?
if so I would recomend using few concentric circles. Then using hough transform for circles you can easily find the center of the shape even when tracking is extremly hard (large movement/low frame rate).
If you must use rectangular shape then there is a good open source which does that. It is part of a project to read street signs and auto-translate them.
Here is a link: http://code.google.com/p/signfinder/
This source is not large and it would be easy to cut out the relevant part.
It uses "good features to track" of openCV in module CornerFinder.
Hope it helped
It is possible, you need following steps: thresholding image, some morphological enhancement,
blob extraction and normalization of blob size, blobs shape analysis, comparison of analysis results with pattern that you want to track.
There is many methods for blobs shape analysis. Simple methods: geometric dimensions, area, perimeter, circularity measurement; bit quads and others (for example, William K. Pratt "Digital Image Processing", chapter 18). Complex methods: spacial moments, template matching, neural networks and others.
In any event, it is very hard to answer exactly without knowledge of pattern shapes that you want to track )
hope it helped
I have used Haar classifier with OpenCV before succesfully. Unfortunately it seems to work only on square objects and fixed angles (i.e. faces). However I need to find "long" (rectangular) objects which have different angles (see sample input image).
Is there a way to train Haar classifier to find such objects? All I can find are tutorials for face recognition. Any other alternative approches?
Haar classifiers are known to work with rigid object only. You need a classifier for each of the view. For example, the side-face classifier in OpenCV doesn't work as good as front-face classifer(due to the reason being, side face has more variation in yaw-pitch-roll than front face).
There is no perfect way of answering your question.
However, in your case whatever you are trying to classify (microbes I suppose) are overlapping on each other. Its a complex issue. But, you can isolate the region where microbes occur (not isolate each microbe like a face).
You can refer fingerprint segmentation techniques that are known to enhance the ridges on a fingerprint (here in your case its microbe edges) from the background and isolate the image.
Check "ridgesegmentation.m" in the following page:
http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html