Difference between revisions of "Publications/carlinet.15.itip.inc"

From LRDE

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== Illustrations ==
 
== Illustrations ==
 
=== Object detections in videos ===
 
=== Object detections in videos ===
In the scope of the ICDAR competition on Smartphone Document Capture and OCR ([https://sites.google.com/site/icdar15smartdoc/challenge-1 SmartDoc-
+
In the scope of the ICDAR competition on Smartphone Document Capture and OCR ([https://sites.google.com/site/icdar15smartdoc/challenge-1 SmartDoc-2015]), we aim at automatically detecting documents in video captured by smartphones. The dataset
2015]), we aim at automatically detecting documents in video captured by smartphones. The dataset
 
 
covers different document layout (textual and/or having graphical content) and realistic scene analysis
 
covers different document layout (textual and/or having graphical content) and realistic scene analysis
 
problems (change of illumination, motion blur, change of perspectives, partial occlusions...).
 
problems (change of illumination, motion blur, change of perspectives, partial occlusions...).

Revision as of 09:38, 4 May 2015

Materials

Multivariate Tree of Shapes Computation Binaries

You can download the x86_64 binaries to compute the Multivariate Tree of Shapes Here. This application outputs 16-bits image where each pixel stores the depth of the node it belongs to. To recover the MToS from this image, one just has to compute its max-tree. Note that the image is twice has big has the original one and has a border for topogical and algorithmic purposes. Thus, any pixel with coordinates (x,y) in the original image is now at coordinates (2*(x+1), 2*(y+1)) in the depth image. The application also outputs a 8bits grayscale version of the depth image that can be used to vizualise the shapes by thresholding this image.

Usage: ./compute_ctos-demo [options] input depth16.tiff depth8.png

Mumford-Shah Simplification with the MToS

You can download the x86_64 binaries to compute the Mumford-Shah simplification running on the MToS (as described in the paper) Here.

Usage: ./mumford_shah_on_tree_full input[rgb] α₀ α₁ λ output
α₀	Grain filter size before merging trees (0 to disable)
α₁	Grain filter size on the color ToS (0 to disable)
λ	Mumford-shah regularisation weight (e.g. 5000)

Illustrations

Object detections in videos

In the scope of the ICDAR competition on Smartphone Document Capture and OCR (SmartDoc-2015), we aim at automatically detecting documents in video captured by smartphones. The dataset covers different document layout (textual and/or having graphical content) and realistic scene analysis problems (change of illumination, motion blur, change of perspectives, partial occlusions...).

Grain filters for document layout extraction

Interactive object segmentation

Classification of hyperspectral images