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I'm trying to develop a system, which recognizes various objects present in an image based on their primitive features like texture, shape & color.
The first stage of this process is to extract out individual objects from an image and later on doing image processing on each one by one.
However, segmentation algorithm I've studied so far are not even near perfect or so called Ideal Image segmentation algorithm.
Segmentation accuracy will decide how much better the system responds to given query.
Segmentation should be fast as well as accurate.
Can any one suggest me any segmentation algorithm developed or implemented so far, which won't be too complicated to implement but will be fair enough to complete my project..
Any Help is appreicated..
A very late answer, but might help someone searching for this in google, since this question popped up as the first result for "best segmentation algorithm".
Fully convolutional networks seem to do exactly the task you're asking for. Check the paper in arXiv, and an implementation in MatConvNet.
The following image illustrates a segmentation example from these CNNs (the paper I linked actually proposes 3 different architectures, FCN-8s being the best).
Unfortunately, the best algorithm type for facial recognition uses wavelet reconstruction. This is not easy, and almost all current algorithms in use are proprietary.
This is a late response, so maybe it's not useful to you but one suggestion would be to use the watershed algorithm.
beforehand, you can use a generic drawing(black and white) of a face, generate a FFT of the drawing---call it *FFT_Face*.
Now segment your image of a persons face using the watershed algorithm. Call the segmented image *Water_face*.
now find the center of mass for each contour/segment.
generate an FFT of *Water_Face*, and correlate it with the *FFT_Face image*. The brightest pixel in resulting image should be the center of the face. Now you can compute the distances between this point and the centers of segments generated earlier. The first few distances should be enough to distinguish one person from another.
I'm sure there are several improvements to the process, but the general idea should get you there.
Doing a Google search turned up this paper: http://www.cse.iitb.ac.in/~sharat/papers/prim.pdf
It seems that getting it any better is a hard problem, so I think you might have to settle for what's there.
you can try the watershed segmentation algorithm
also you can calculate the accuracy of the segmentation algorithm by the qualitative measures
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I am trying to implement a Convolutional Neural Network (CNN) model to classify hand gestures. Dataset is not readily available and hence I need to prepare it.
How should i prepare the dataset? Should the images I capture contain objects other than the hand or only the hand? Which will give me an accurate model that will work accurately despite of background and other objects in the frame?
Good Dataset for your problem:
You should consider involving different backgrounds and objects in background.
Following links might help you:
https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
https://www.quora.com/Computer-Vision-What-is-the-best-way-to-collect-Train-and-Test-data-images-for-object-recognition-job
here is an example:
http://cims.nyu.edu/~tompson/NYU_Hand_Pose_Dataset.htm
it containing other images would just mean you have to implement something in your pipeline to isolate the hand. i would recommend having only the hand in the images so you can just start modelling on the images right away.
a lot of cnn architectures in this area using multi-resolution CNNs. so in your data preparation just make multiple resolutions and feed to a multi input CNN. you can make this using Keras functional API. low res images are fine for differentiating between certain very different poses and the higher res can focus on small differences.
obviously, standard data augmentation is not that suitable for hand pose. stuff like mirroring or changing the angle could make your data unsuitable for the given label. so be a bit more conservative with your data augmentation if you don't have that much.
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I am interested in knowing the importance of data augmentation(rotation at various angles, flipping the images) while providing a dataset to a Machine Learning problem.
Whether it is really needed? Or the CNN networks using will handle that as well no matter how different the data are transformed?
So I took a classification task with 2 classes to conclude some results
Arrow shapes
Circle shapes
The idea is to train the shapes with only one orientation(I have taken arrows pointing right) and check the model with a different orientation(I have taken arrows pointing downwards) which is not at all given during the training stage.
Some of the samples used in Training
Some of the samples used in Testing
This is the entire dataset I am using in for creating a tensorflow model.
https://bitbucket.org/akhileshmalviya/samples/src/bab50b85d826?at=master
I am wondering with the results I got,
(i) Except a few downward arrows all others are getting predicted correctly as arrow. Does it mean data augmentation is not at all needed?
(ii) Or is this the right use case I have taken to understand the importance of data augmentation?
Kindly share your thoughts, Any help could be really appreciated!
Data augmentation is a data-depended process.
In general, you need it when your training data is complex and you have a few samples.
A neural network can easily learn to extract simple patterns like arcs or straight lines and these patterns are enough to classify your data.
In your case data augmentation can barely help, the features the network will learn to extract are easy and highly different from each other.
When you, instead, have to deal with complex structures (cats, dogs, airplanes, ...) you can't rely on simple features like edges, arcs, etc..
Instead, you have to show to your network that the instances you're trying to classify got an high variance and that the features extracted can be combined in a lot of different ways for the same subject.
Think about a cat: it can be of any color, the picture can be taken in different light conditions, its whole body can be in any position, the picture could be taken with a certain orientation...
To correctly classify instances so different, the network must learn to extract robust features that could be learned only after seeing a lot of different inputs.
In your case, instead, simple features can completely discriminate your input, thus any sort of data augmentation could help by just a little bit.
The task you are solving can be easily solved without any NN and even without machine learning.
Just because the problem is so simple it does not really matter whether you do a data augmentation or not. The need for data augmentation is task specific and depends on many things:
how easy is to augment the data with preserving the ability to correctly mark the class. For image, sounds which we used to see/hear it is not a problem (we know that adding small noise to the sound does not change the meaning, rotating the lizard is still a lizard). For other things augmenting without preserving the class/value is hard (for example in Go, randomly adding a stone can change the value of the position dramatically)
does the augmented data is drawn from the same distribution you care about. Adding random stones to Go does not work, but rotating flipping the board works and preserves distribution. But for example in a racing king game (variant of chess) it will not help. You can't flip the position (left <-> right), the evaluation stays the same, but it will never happen in real game and therefore drawn from different distribution and useless
how much data do you have and how expressive is your model. The more parameters you model have, the bigger the chance of overfitting and the more is your need for data. If you train a linear regression in n dims, you will have n + 1 params. You do not really need to augment this. Also if you already have 10bln data points, the augmentation is probably will not be helpful.
how expensive the augmentation procedure. For rotating/scaling the image it is very cheap, but for other augmentation it can be computationally expensive
something else that I forgot.
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I am doing a project which is hole detection in road. I am using a laser to emit beam on the road and using a camera to take a image of the road. the image may be like this
Now i want to process this image and give a result that is it straight or not. if it curve then how big the curve is.
I dont understand how to do this. i have search a lot but cant find a appropriate result .Can any one help me for that?
This is rather complicated and your question is very broad, but lets have a try:
Perhaps you have to identify the dots in the pixel image. There are several options to do this, but I'd smoothen the image by a blur filter and then find the most red pixels (which are believed to be the centers of the dots). Store these coordinates in a vector array (array of x times y).
I'd use a spline interpolation between the dots. This way one can simply get the local derivation of a curve touching each point.
If the maximum of the first derivation is small, the dots are in a line. If you believe, the dots belong to a single curve, the second derivation is your curvature.
For 1. you may also rely on some libraries specialized in image processing (this is the image processing part of your challenge). One such a library is opencv.
For 2. I'd use some math toolkit, either octave or a math library for a native language.
There are several different ways of measuring the straightness of a line. Since your question is rather vague, it's impossible to say what will work best for you.
But here's my suggestion:
Use linear regression to calculate the best-fit straight line through your points, then calculate the mean-squared distance of each point from this line (straighter lines will give smaller results).
You may need to read this paper, it is so interesting one to solve your problem
As #urzeit suggested, you should first find the points as accurately as possible. There's really no way to give good advice on that without seeing real pictures, except maybe: try to make the task as easy as possible for yourself. For example, if you can set the camera to a very short shutter time (microseconds, if possible) and concentrate the laser energy in the same time, the "background" will contribute less energy to the image brightness, and the laser spots will simply be bright spots on a dark background.
Measuring the linearity should be straightforward, though: "Linearity" is just a different word for "linear correlation". So you can simply calculate the correlation between X and Y values. As the pictures on linked wikipedia page show, correlation=1 means all points are on a line.
If you want the actual line, you can simply use Total Least Squares.
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I was given this question on a job interview and think I really messed up. I was wondering how others would go about it so I could learn from this experience.
You have one image from a surveillance video located at an airport which includes line of people waiting for check-in. You have to assess if the line is big/crowded and therefore additional clerks are necessary. You can assume anything that may help your answer. What would you do?
I told them I would try to
segment the area containing people from the rest by edge detection
use assumptions on body contour such as relative height/width to denoise unwanted edges
use color knowledges; but then they asked how to do that and I didn't know
You failed to mention one of the things that makes it easy to identify people standing in a queue — the fact that they aren't going anywhere (at least, not very quickly). I'd do it something like this (Warning: contains lousy Blender graphics):
You said I could assume anything, so I'll assume that the airport's floor is a nice uniform green colour. Let's take a snapshot of the queue every 10 seconds:
We can use a colour range filter to identify the areas of floor that are empty in each image:
Then by calculating the maximum pixel values in each of these images, we can eliminate people who are just milling around and not part of the queue. Calculating the queue length from this image should be very easy:
There are several ways of improving on this. For example, green might not be a good choice of colour in Dublin airport on St Patrick's day. Chequered tiles would be a little more difficult to segregate from foreground objects, but the results would be more reliable. Using an infrared camera to detect heat patterns is another alternative.
But the general approach should be fairly robust. There's absolutely no need to try and identify the outlines of individual people — this is really very difficult when people are standing close together.
I would just use a person detector, for example OpenCV's HOG people detection:
http://docs.opencv.org/modules/gpu/doc/object_detection.html
or latent svm with the person model:
http://docs.opencv.org/modules/objdetect/doc/latent_svm.html
I would count the number of people in the queue...
I would estimate the color of the empty floor, and go to a normalized color space (like { R/(R+G+B), G/(R+G+B) } ). Also do this for the image you want to check, and compare these two.
My assumption: where the difference is larger than a threshold T it is due to a person.
When this is happening for too much space it is crowded and you need more clerks for check-in.
This processing will be way more robust than trying to recognize and count individual persons, and will work with quite row resolution / low amount of pixels per person.
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HOG is popular in human detection. Can it be used for detecting objects like cup in the image for example.
I am sorry for not asking programming question, but I mean to get the idea if i can use hog to extract object features.
According to my research I have dont for few days I feel yes but I am not sure.
Yes, HOG (Histogram of Oriented Gradients) can be used to detect any kind of objects, as to a computer, an image is a bunch of pixels and you may extract features regardless of their contents. Another question, though, is its effectiveness in doing so.
HOG, SIFT, and other such feature extractors are methods used to extract relevant information from an image to describe it in a more meaningful way. When you want to detect an object or person in an image with thousands (and maybe millions) of pixels, it is inefficient to simply feed a vector with millions of numbers to a machine learning algorithm as
It will take a large amount of time to complete
There will be a lot of noisy information (background, blur, lightning and rotation changes) which we do not wish to regard as important
The HOG algorithm, specifically, creates histograms of edge orientations from certain patches in images. A patch may come from an object, a person, meaningless background, or anything else, and is merely a way to describe an area using edge information. As mentioned previously, this information can then be used to feed a machine learning algorithm such as the classical support vector machines to train a classifier able to distinguish one type of object from another.
The reason HOG has had so much success with pedestrian detection is because a person can greatly vary in color, clothing, and other factors, but the general edges of a pedestrian remain relatively constant, especially around the leg area. This does not mean that it cannot be used to detect other types of objects, but its success can vary depending on your particular application. The HOG paper shows in detail how these descriptors can be used for classification.
It is worthwhile to note that for several applications, the results obtained by HOG can be greatly improved using a pyramidal scheme. This works as follows: Instead of extracting a single HOG vector from an image, you can successively divide the image (or patch) into several sub-images, extracting from each of these smaller divisions an individual HOG vector. The process can then be repeated. In the end, you can obtain a final descriptor by concatenating all of the HOG vectors into a single vector, as shown in the following image.
This has the advantage that in larger scales the HOG features provide more global information, while in smaller scales (that is, in smaller subdivisions) they provide more fine-grained detail. The disadvantage is that the final descriptor vector grows larger, thus taking more time to extract and to train using a given classifier.
In short: Yes, you can use them.