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Submission + - Breakthrough in Automatic Handwritten Character Recognition sans Deep Learning

subh_arya writes: Researchers from NYU, UToronto and MIT have come up with a technique that captures human learning abilities for a large class of simple visual concepts to recognize handwritten characters from World's Alphabet. Their computational model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. Unlike recent deep learning approaches that require thousands of examples to train an efficient model, their model can achieve human-level performance with only one example. Additionally, the authors present several “visual Turing tests” probing the model’s creative generalization abilities, which in many cases are indistinguishable from human behavior. A science magazine article is here.

Submission + - UCF Researchers Perform World's First Automated Mass-Crowd Count (ucf.edu)

subh_arya writes: Automatic crowd counting has been an extremely challenging computer vision problem. However, researchers from UCF, seem to have found a reasonably accurate solution using sophisticated probabilistic models. Although there has been several previous efforts in this direction, this is the first time the technology has been put to use on a realistic scenario where around 550,000 protesters participated for Catalunyan Independence. A freely available technical paper published in IEEE Trans. on Pattern Analysis and Machine Intelligence, 2015 is available here.

Comment Re:123D Catch? Autodesk already has an app doing t (Score 2) 48

I'm kind of surprised that Microsoft isn't using the acceleration and magnetic sensors in the phone to help determine the camera position. It's one of the features that phone cameras have that DSLR's don't.

Actually they do. Fig.2 in the paper, where the IMU output is used to refine the camera pose estimated by purely image based means.

Comment Re:this is old hat. (Score 3, Informative) 48

Its true that the concept of 3D reconstruction from dense stereo/structure from motion is not new. However, the computational pipeline integrated entirely into a mobile device without any expensive hardware or offloading computation on a cloud, differentiates this effort from the previous ones.

Submission + - MobileFusion - A tool to generate 3D models from ordinary smartphones

subh_arya writes: Researchers from Microsoft Research unveil the first technology to perform 3D surface reconstruction from ordinary smartphone cameras. Their computational framework creates a connected 3D surface model by continuously registering RGB input to an incrementally built 3D model. Although the reconstruction results look promising, Microsoft does not claim to release an app anytime sooner.

Comment Re: Note to Slashdot Editors (Score 1) 115

Most deep learning algorithms used for image classification tasks use a data augmentation step - wherein they alter the training image through scaling, translation, etc. According to the paper published here:http://arxiv.org/abs/1501.02876, they do additional transformations in the training images to make the learned model even more robust. So the risk of using up cpu cycles on the same data again and again is reduced.

Comment Nothing except occasional PPT for work (Score 1) 1880

I have been using LInux since 2000. However, I always keep one machine dual boot. This is mainly due to work as my PhD advisor needs me to create presentations in M$ powerpoint which he can edit on his Win7. PPTs made on Openoffice Impress (I am not saying the native format) have issues embedding videos. Also some tables just don't work properly. At home I have converted my wife to use Ubuntu. ;-)

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