My question is regarding neural networks mimicking noise specifically. The idea is to have a clean image and a neural network ( type does not matter ) mimicking noise. Like Perlin noise or noise on camera that comes from photo blitz or salt and pepper noise on a black and white image etc. What I would like to know if someone tried something like this and is there research published regarding something like that. The noise is specifically for images and 2D matrices.
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At the moment, I'm learning about artificial neural networks for colorizing black and white images.
After reading enough literature and considering many examples, I had one, maybe stupid, question: how can a convolutional neural network, like U-net, colorize images? From many articles, I realized that this architecture is able to extract features from an image by applying a convolution operation and subsequently combine them. But where do the a and b color channels come from (we are talking about the Lab color space, of course)? The input is the L-channel of the image, which is a black and white image, the pixels of which contain the value of brightness (intensity), but how are the a and b channels obtained at the output? I would be grateful if someone could explain this to me mathematically and in simple terms.
I am completing a project in college about robotic perception and the mathematics behind it. I am currently looking into computer vision as a means of robotic perception and image/video feed analysis. I have stumbled upon noise reduction filters such as the Median, Bilateral and Gaussian filter after some experimenting with the OpenCV library but wanted to know if applying these filters made it easier for the images to be analysed. For example, if I were to perform Houghlines on an image to find lines, would it be useful to reduce noise in an image beforehand? Are there any applications of noise reduction for computer vision? I cannot find anything online and imagine that it may have some uses but am not sure.
I am talking about ordinary photographs that a robot may take with a camera to then use for object recognition. For example, if a robot were to determine between two types of animals in an image based on certain mathematically specified criteria, would it be helpful to reduce the noise in an image so that the image and the animals are easier to detect? I understand that this also blurs the image and edges can be lost/reduced. If noise filters are not helpful in this type of scenario, are there any you can think of that it may be useful for in terms of enabling a robot to intepret an image more accurately.
I apologise if this is not clear, this area of knowledge is something I am unfamiliar with and is something I am pushing myself to understand to include in my project.
I am looking for algorithms/publications on face detection. There are plenty in the web. But my scenario is somewhat specialized. I want to detect faces accurately in images taken by wearable devices (e.g. narrative clips), so there will be motion blur, and image quality will not be that good. I want to detect faces that are within 15 feet of the camera accurately. Next goal is to estimate the pose, primarily to find out if the person is looking toward the camera ( or better looking at the camera owner).
Any suggestion?
My go to for this would either be a deep-learning framework using convolutional layers for pixel classification, or K-means/ K-Nearest Neighbour algorithm.
This does depend on your data, however. From your post I am assuming that your data isn't labelled? meaning you are unable to feed in the 'truth' to the algorithm for classification.
you could perhaps use a CNN (convolutional neural network) for pixel classification (image segmentation) which should identify the location of a person. given this, perhaps you could run a 'local' CNN i a region close to the face identified to classify the region the body is located in as a certain pose.
This would probably be my first take on the problem but would depend on the exact structure of your data, and the structure of your labels (if you have any).
I have to say it does sound like a fun project!
I found OpenCV's Haar Cascades for Face Detection pretty accurate and robust for motion blur and "live" face recognition.
I'm saying that because I used them for implementing an Eye-Tracker in C++ with a laptop webcam (whose resolution was not excellent and motion blur was naturally always present).
They work in multiresolution and are therefore able to detect faces of any size, but you can easily tune them for your distance of interest.
They might not be your final optimal solution, but since they are already implemented and come with the OpenCV package, they could constitute a good starting point.
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
I am doing research in the field of computer vision, and am working on a problem related to finding visually similar images to a query image. For example, finding t-shirts of similar colour with similar patterns (Striped/ Checkered), or shoes of similar colour and shape, and so on.
I have explored hand-crafted image features such as Color Histograms, Texture features, Shape features (Histogram of Oriented Gradients), SIFT and so on. I have also read up literature about Deep Neural Networks (Convolutional Neural Networks), which have been trained on massive amounts of data and are currently state of the art in Image Classification.
I was wondering if the same features (extracted from the CNN's) can also be used for my project - finding fine-grained similarities between images. From what I understand, the CNNs have learnt good representative features that can help classify images - for example, be it a red shirt or a blue shirt or an orange shirt, it is able to identify that the image is a shirt. However it doesn't understand that an orange shirt looks more similar to a red shirt than a blue shirt does, and hence it is not able to capture these similarities.
Please correct me if I am wrong. I would like to know if there are any Deep Neural Networks that capture these similarities, and have proven to be superior to the hand-crafted features. Thanks in advance.
For your task, a CNN is definitely worth a try!
Many researchers used networks which are pretrained for Image Classification and obtained state-of-the-art results on fine-grained classification. For example, trying to classify birds species or cars.
Now, your task is not classification, but it is related. You can think about similarity as some geometric distance between features, which are basically vectors. Thus, you may carry out some experiments computing the distance between the feature vectors for all your training images (the reference) and the feature vector extracted from the query image.
CNNs features extracted from the first layers of the net should be more related to color or other graphical traits, rather than more "semantical" ones.
Alternatively, there is some work on learning directly a similarity metric through CNN, see here for example.
A little bit out-dated, but it can still be useful for other people. Yes, CNNs can be used for image similarity and I used before. As Flavio pointed out, for a simple start, you can use a pre-trained CNN of your choice such as Alexnet,GoogleNet etc.. and then use it as feature extractor. You can compare the features based on the distance, similar pictures will have a smaller distance between their feature vectors.