AI News, Interactive Segmentation with Convolutional NeuralNetworks

Interactive Segmentation with Convolutional NeuralNetworks

Recently animated stickers have increased in popularity due to their massive use in messaging applications or memes.

Still, with existing tools, generating animated stickers is extremely challenging and time-consuming, making the task practically infeasible for non-experts.

Automated animated sticker generation is a challenging problem to solve because of the complex nature of videos: they are subject to motion blur, bad composition, and occlusion.

An object can be hard to segment due to its complex structure, small size (very little information) or large similarity between background and foreground.

Inspired by recent work in interactive object segmentation with deep neural networks, we built a model that takes the image, the current segmentation result, and the user corrections as input and outputs a binary mask of the object.

Based on our production data, we have found that typical users tend to draw with a variety of patterns such as clicks, strokes or highlighting the whole object.

Thus, we needed our algorithm to take into account a diversity of annotations and decided to include simulated strokes and clicks during the training phase to get the best results and give the user a great experience.

Image Segmentation with Watershed Algorithm¶

Below we will see an example on how to use the Distance Transform along with watershed to segment mutually touching objects.

So, now we know for sure that region near to center of objects are foreground and region much away from the object are background.

This way, we can make sure whatever region in background in result is really a background, since boundary region is removed.

These areas are normally around the boundaries of coins where foreground and background meet (Or even two different coins meet).

Erosion is just another method to extract sure foreground area, that’s all.) Now we know for sure which are region of coins, which are background and all.

So we create marker (it is an array of same size as that of original image, but with int32 datatype) and label the regions inside it.

The regions we know for sure (whether foreground or background) are labelled with any positive integers, but different integers, and the area we don’t know for sure are just left as zero.

It labels background of the image with 0, then other objects are labelled with integers starting from 1.

Object Tracking using OpenCV (C++/Python)

The definition sounds straight forward but in computer vision and machine learning, tracking is a very broad term that encompasses conceptually similar but technically different ideas.

For example, all the following different but related ideas are generally studied under Object Tracking If you have ever played with OpenCV face detection, you know that it works in real time and you can easily detect the face in every frame.

We define a bounding box containing the object for the first frame and initialize the tracker with the first frame and the bounding box.

Finally, we read frames from the video and just update the tracker in a loop to obtain a new bounding box for the current frame.

In tracking, our goal is to find an object in the current frame given we have tracked the object successfully in all ( or nearly all ) previous frames.

The motion model is just a fancy way of saying that you know the location and the velocity ( speed + direction of motion ) of the object in previous frames.

If you knew nothing else about the object, you could predict the new location based on the current motion model, and you would be pretty close to where the new location of the object is.

This appearance model can be used to search in a small neighborhood of the location predicted by the motion model to more accurately predict the location of the object.

If the object was very simple and did not change it’s appearance much, we could use a simple template as an appearance model and look for that template.

The classifier takes in an image patch as input and returns a score between 0 and 1 to indicate the probability that the image patch contains the object.

The score is 0 when it is absolutely sure the image patch is the background and 1 when it is absolutely sure the patch is the object.

An offline classifier may need thousands of examples to train a classifier, but an online classifier is typically trained using a very few examples at run time.

classifier is trained by feeding it positive ( object ) and negative ( background ) examples.

If you want to build a classifier for detecting cats, you train it with thousands of images containing cats and thousands of images that do not contain cats.

The initial bounding box supplied by the user ( or by another object detection algorithm ) is taken as the positive example for the object, and many image patches outside the bounding box are treated as the background.

This algorithm is a decade old and works ok, but I could not find a good reason to use it especially when other advanced trackers (MIL, KCF) based on similar principles are available.

The big difference is that instead of considering only the current location of the object as a positive example, it looks in a small neighborhood around the current location to generate several potential positive examples.

Minimizing this ForwardBackward error enables them to reliably detect tracking failures and select reliable trajectories in video sequences.

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