Physical to AR-Spatiotemporal Object Representation using CNN Video Segmentation

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Hi Everyone, We are very happy to announce the 4th Computer vision Israel community Meetup!

This time, Friend of our community, Eddie Smolyansky, Visualead's Head of Research, will present Visualead work on video object segmentation using convolutional neural networks.

Topics will include:

- What is semantic segmentation in images?
- How can it be extended to *any* object segmentation in video?
- A discussion on some of their experiments (as well as failure cases)
- Present other leading methods for video object segmentation: this is a hot field with new state of the art results almost monthly.

More formally:

The task of category independent foreground segmentation in images is challenging for a machine learning system, because it needs to learn the general concept of an object, even for object categories that it hasn't seen during training. In the case of foreground segmentation in videos, the problem is compounded by the fact that the object as well as the background change appearance throughout the video. We propose a method for learning the general concept of object appearance in videos, based on deep neural networks. Apart from learning the object appearance for each frame, our system learns the temporal changes between frames in a video, which represent the object motion, and thus leverages the temporal information available in videos. By learning a category-independent object segmentation, we are able to perform unsupervised video object segmentation. In addition, in the case of semi-supervised video segmentation (where one frame from the video is annotated) we further train our system torecognize a specific object which appears in the video. In both scenarios, our system compares favorably against the state of the art.Furthermore, we demonstrate a novel use case for video object segmentation, by implementing a mobile application where a user captures a video of an object, and our system is able tosegment the object and display it in an AR setting