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

From LRDE

Line 67: Line 67:
   
 
=== Interactive object segmentation ===
 
=== Interactive object segmentation ===
  +
  +
<gallery>
  +
File:carlinet.15.tip-grave.png
  +
File:carlinet.15.tip-banana1.png
  +
File:carlinet.15.tip-flower.png
  +
File:carlinet.15.tip-espagne.png
  +
File:carlinet.15.tip-petct.png
  +
File:carlinet.15.tip-person5.png
  +
File:carlinet.15.tip-person5_2.png
  +
File:carlinet.15.tip-41033.png
  +
</gallery>
   
 
=== Classification of hyperspectral images ===
 
=== Classification of hyperspectral images ===

Revision as of 12:42, 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

We use a grain filter to extract text boxes and graphical parts of documents. Indeed, text parts are composed of letters which are supposed to be small components if the MToS is well-formed. On the contrary, text boxes and grahical contents are large components that should remain after the filtering.

Examples:

More examples are available in this archive.

Interactive object segmentation

Classification of hyperspectral images