This post describes our work and research on the greenScreen. Also check out my website — www.

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A few months ago, after taking the great Fast. Considering the above, we were exploring ideas which:. Our early thoughts were to take on some medical project, since this field is very close to our hearts, and we felt and still feel that there is an enormous number of low hanging fruits for deep learning in the medical field.

However, we realized that we are going to stumble upon issues with data collection and perhaps legality and regulation, which was a contradiction with our will to keep it simple.

Our second choice was a background removal product. However, fully automated background removal is quite a challenging task, and as far as we know, there is still no product that has satisfactory results with it, although some do try.

What background will we remove? This turned out to be an important question, since the more specific a model is in terms of objects, angle, etc. When starting our work, we thought big: a general background remover that will automatically identify the foreground and background in every type of image.

But after training our first model, we understood that it will be better to focus our efforts in a specific set of images. Therefore, we decided to focus on selfies and human portraits. With these assumptions in mind, we embarked on a journey of research, implementation and hours of training to create a one click easy to use background removal service.

Semantic Segmentation Overview - Train a Semantic Segmentation Network Using Deep Learning.

The main part of our work was training the model, but we couldn't underestimate the importance of proper deployment. Good segmentation models are still not compact as the classification model e. If you want to read more details about the deployment process es of our product, your are welcomed to check out our posts on server side and client side.

When examining deep learning and computer vision tasks which resemble ours, it is easy to see that our best option is the semantic segmentation task. Semantic segmentation is a well known computer vision task, one of the top three, along with classification and object detection.

The segmentation is actually a classification task, in the sense of classifying every pixel to a class. So how does the segmentation work? To better understand, we will have to examine some of the early works in this field. The earliest idea was to adopt some of the early classification networks such as VGG and Alexnet.

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When examining VGG early layers, it may be noticed that there are high activation around the item to classify. Deeper layers have even stronger activation, however they are coarse in their nature since the repetitive pooling action. Early results for semantic segmentation emerged along with the classification algorithms.

In this postyou can see some rough segmentation results that come from using the VGG :. To smooth the prediction, the researchers used a naive bilienar up-sampling layer. In the FCN paper, the researchers improved the idea above. Adding some skip connections between the layers allowed the prediction to encode finer details from the original image.

Further training improved the results even more.

background removal with deep learning

The FCN unlocked the concept of segmentation, and researchers tried different architectures for this task. The main idea stays similar: using known architectures, up-sampling, and using skip connections are still prominent at the newer models. You can read about advances in this field in a few good posts: herehere and here.

You can also see that most architectures keep the encoder- decoder architecture. After doing some research, we settled on three models, which were available to us: the FCN, Unet and Tiramisu — very deep encoder-decoder architecture.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.

If nothing happens, download the GitHub extension for Visual Studio and try again. This project represents the final project for The INF machine learning course at polytechnique Montreal. In this project we tackle on the problem of background removal through image matting. It consists of predicting the foreground of an image or a video frame. Indeed, object seen on images are not always present at full opacity. Think for instance of a tinted glass box.

Ideal image segmentation would give a mask telling which pixel belongs to the box and which to the rest of the image. The following are some of the results of our model.

The download script uses the chromediver which is available by installing the chrome web browser. It can also be installed stand alone. Checkout the following link for more info chromedriver. This project uses a custom dataset generated by a script. The script crawl the web to retreive foreground and background images with specific filters.

Note that the output of the script was manually filtered. Refer to the Experiment section in the project article for more details about the dataset generation. This will create a new folder in data in the scripts directory in wich the dataset is stored. The training script will save a checkpoint in the log directory after each batches. In order to try our model, we included a snapshot of our trained model in the log directory.

That can be used as follow:. Skip to content.This post describes our work and research on the greenScreen. Also check out my website — www. A few months ago, after taking the great Fast. Considering the above, we were exploring ideas which:.

Our early thoughts were to take on some medical project, since this field is very close to our hearts, and we felt and still feel that there is an enormous number of low hanging fruits for deep learning in the medical field. However, we realized that we are going to stumble upon issues with data collection and perhaps legality and regulation, which was a contradiction with our will to keep it simple.

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Our second choice was a background removal product. However, fully automated background removal is quite a challenging task, and as far as we know, there is still no product that has satisfactory results with it, although some do try. What background will we remove? This turned out to be an important question, since the more specific a model is in terms of objects, angle, etc. When starting our work, we thought big: a general background remover that will automatically identify the foreground and background in every type of image.

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But after training our first model, we understood that it will be better to focus our efforts in a specific set of images. Therefore, we decided to focus on selfies and human portraits. With these assumptions in mind, we embarked on a journey of research, implementation and hours of training to create a one click easy to use background removal service.

The main part of our work was training the model, but we couldn't underestimate the importance of proper deployment. Good segmentation models are still not compact as the classification model e. If you want to read more details about the deployment process es of our product, your are welcomed to check out our posts on server side and client side. When examining deep learning and computer vision tasks which resemble ours, it is easy to see that our best option is the semantic segmentation task.

Semantic segmentation is a well known computer vision task, one of the top three, along with classification and object detection. The segmentation is actually a classification task, in the sense of classifying every pixel to a class. So how does the segmentation work? To better understand, we will have to examine some of the early works in this field. The earliest idea was to adopt some of the early classification networks such as VGG and Alexnet.

When examining VGG early layers, it may be noticed that there are high activation around the item to classify. Deeper layers have even stronger activation, however they are coarse in their nature since the repetitive pooling action. Early results for semantic segmentation emerged along with the classification algorithms.

In this postyou can see some rough segmentation results that come from using the VGG :.I build a CNN model for background subtraction. The experiment is not finished yet, and I will include the result in next post. You can get a PDF version of this post from here. Before reading this post, you may want to review my work in week In the last reportI proposed a framework of vehicle counting based on deep learning figure 1. During weekI focused on the first part, i.

Figure 2. Then the author constructs a CNN model, followed by some post-processing methods, to get the final foreground mask. Instead of feeding the whole image to the CNN, the author extracts none-overlapping patches and processes them one by one, both during training and testing.

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It is not deep enough. The framework is quite similar to [1]. It consists of two stages: generating background image and feeding forward through CNN. In my model, I simplify this stage because a strong CNN model should compensate the imperfections of the background model.

In this stage, I first generate the background image. This is because ResNet only takes input that has 3 channels. Second, the 3-channel feature map runs through fully-convolutional ResNet for feature extracting.

Here comes a problem: the output of the convolutional operation has smaller size than the input. According to some state-of-art work on semantic segmentation [6,7]deconvolutional layers also called transposed convolutional layers prove to be useful in pixel-to-pixel estimation. So I add three deconvolutional layers to up-sample the feature map. I use part of CDnet [8] to train the model. I modify the dataset so that is fit the model better.

You can get the modified dataset from here. If you cannot access Google Drive for some reason, please contact me by emailing yanyiqinwpu gmail.

First, I run through the whole dataset to generate all the background images. These background images together with the origin frame images will be used to train the model. The ResNet model can be implemented based on TF-slim [9]. I train the network with mini batches of size 40 via Adam optimizer. The training runs for iterations, for the first steps, the learning rate is 1e-3; for step the learning rate is reduced to 1e-4; then the learning rate is reduced to 1e-5 and kept unchanged until the end of the training.

background removal with deep learning

For the lost function, I choose the classical binary cross-entropy loss. Boundaries of foreground objects and pixels that do not lie in the ROI are marked by gray value in the ground truth segmentations Figure 3.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Computer Science Stack Exchange is a question and answer site for students, researchers and practitioners of computer science. It only takes a minute to sign up. I'm trying to build a machine learning classifier that can detect which object is in an image. I have a labled training set of images and am currently in the image processing phase.

Each of these images has one of the objects. However, almost all images have these objects embedded in a very common background color amongst all images. The background red is more or less uninformative of what object is in the picture.

background removal with deep learning

In order to reduce the dimensionality of my problem, I'd really like to apply an algorithm that somehow extracts the object from the common background. My idea after that was then to resize the resulting images all to some common dimension. Then i was going to try to do some deep learning. Does anyone know any algorithms in computer processing that can do this? Try using clustering on the histogram of pixel color values. You might want to convert to HSV color spacebuild a histogram of the hues, and apply clustering on those values.

In your examples, the majority class the cluster containing the majority of pixel values is the background. You could use heuristics, such as that the cluster that contains the majority of pixels along the boundary of the image is more likely to be the background. The watershed algorithm or other methods of image segmentation could be an alternative approach that might work well. Sign up to join this community. The best answers are voted up and rise to the top.

Home Questions Tags Users Unanswered. Removing common background in image processing phase for classification Ask Question. Asked 4 years, 5 months ago.

Background Removal with Deep Learning

Active 3 years, 10 months ago. Viewed 1k times. These are not the images i'm training on but they give the general idea: The background red is more or less uninformative of what object is in the picture. Are you familiar with the following?

For finding the background: en. For object recognition: en.

d566: Deep learning background removal

The objects i'm trying to classify are actually very similar to each other, its in the details that they differ. So i'm very afraid PCA will lose me this fine details. Its also for this reason that i chose to not go the SIFT route. I think because my images are very similair, sift will have trouble with matching. I really think for the kinda of detail I need, i should go some kind of neural net on the objects themselves.

Anyway, my advice would be to spend some quality time at the library studying each of those topics. I think you'll find there are many good techniques documented in standard textbooks or resources, which would be the first things to try. PCA would not have been one of the techniques I would have suggested for image segmentation or object recognition. If thats too slow, i think a histogram method might also work.

Active Oldest Votes. If your images are as clean as shown in the example, almost anything should work well. Could your answer be applied to removing a powerful coherent laser light of a known wavelength or color from an incoherent light background such as that viewed from a Boeing cockpit window on approachmetersfrom the airport?In theaters, Spider-Man swings over the streets of New York, gliding across impossible gaps, somersaulting through explosions. In the studio, however, Tom Holland rolls around in front of a giant green sheet.

But fortunately for many actors, a new A. With a green screen or other monotone set, the background can be made transparent by filling everything in the frame other than the subject with an uncommon color like bright green or blue. But without that easily identifiable color, how is a computer to know what to remove? Prior methods looked for things like the edges of foreground objects and the level of blur in average backgrounds.

The video below shows how even the green screen approach was often not forgiving enough for low-budget amateur productions. The innovation here, as in so many modern breakthroughs, was to shift the approach from classical algorithms to ones based on machine learning. In particular, deep learning neural networks were able to look at an enormous training dataset of almost 50, images, and then apply that knowledge to testing images.

The A. The result seems to be an A. Unlike prior approaches, it can also be used on images with real outdoor backgrounds and all sorts of lighting conditions on the foreground object. That could dramatically reduce the cost of special effects shots, especially outdoors, and quite simply revolutionize the visual authenticity of the burgeoning genre of green screen YouTubers.

Those three-fingered hands and half-disappearing jaws could soon be a thing of the past. Graham Templeton. Related Tags Movies A.

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Results for:.The task of separating such delicate segmentation is called matting, and defines a different challenge. I did an internship where one of my tasks was to automatically remove the background in X-ray images, I spent basically half a year studying image processing, segmentation, reading papers, skimming image processing books etc Somebody should've told me that earlier, would've probably saved me a month.

Perhaps I'm saying the obvious here, but why not train with a bunch of images which have been recorded in front of an actual green screen?

background removal with deep learning

That way, you can insert any random background and generate as many new training images as you like. The network is being trained for photographs, not CGI. I suspect the different cues will end up producing wildly different trained networks.

But the green screen idea is still an interesting and worthy proposal. I don't think that they have enough images taken in front of a green screen. Just changing the background has diminishing returns because the network may start to memorize the foreground images.

The network may rely on differences in lighting, etc, and fail to generalize. Animats on Feb 12, There are commercial services doing this by hand with cheap labor.

They're called "clipping path services". EmilStenstrom on Feb 13, Unless they decide to replace their people with deep learning. CyberDildonics on Feb 12, The state of the art in natural image matting already is confronting fine details as well as image segmentation and clustering.

Copying papers from years ago would give much better results than he shows here. To get more training data, you could have filmed using a green screen.


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