yxiongdropbox

4 ms·

1.
by yxiongdropbox 10y ago
The learning algorithm we used is not a neural network that got trained in end-to-end fashion. Instead, it is a local prediction model that takes an input image patch and produces a patch of the same dimension with probability for each pixe
2.
by yxiongdropbox 10y ago
Hi everyone, this is Ying Xiong from Dropbox, and I'm the author of the blog post. Feel free to let me know if you have any question, comments or suggestions. Hope you enjoy this post, and keep tuned as we have other posts to be publis
3.
by yxiongdropbox 10y ago
Very good question. As stated in the blog post (one line above that figure), we actually used a polar parametrization r=x·sinθ+y·cosθ than the slope-intercept version y=mx+b. If we were to use y=mx+b, then the hough transform image would lo
4.
by yxiongdropbox 10y ago
Yep, we turned RGB into LUV space before extracting edges, which helps a lot on contrast and keeps essential edge information that could've been lost if converted to grayscale. Agree with that 3D deformation is a difficult open problem
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by yxiongdropbox 10y ago
Indeed, this is a deceptively really hard problem that I think nobody perfectly solved yet. The main problem with the deep learning route is it being resource demanding (both computation and memory expensive). Hopefully these problems will
6.
by yxiongdropbox 10y ago
Glad you liked the post! Yeah, Hough transform is definitely a time-tested algorithm that embodies both elegance and efficacy. I truly love that. On the technical side, we wrote the detection library in C++, so that it can be easily ported
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by yxiongdropbox 10y ago
Yep, we do the entire document detection and other following steps (to be described in coming posts) on the mobile device.