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)
Document detection 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.
More examples are available in this archive.
Interactive object segmentation
We Introduce a method for interactive image segmentation using the MToS. Given a set of markers for the background and foreground classes, we aim at classifying the other pixels to one of these classes. The method performs the classification of the tree nodes and is free of statistical modeling.
Classification of hyperspectral images
We extend the method proposed by Dalla Mura et al. using the morphological attribute profiles computed on the MToS to perform the classification of hyperspectral images acquired by Quickbird. We have compared the classification with attributes profiles (AP), the marginal self-dual attribute profiles (MSDAP) and the vectorial self-dual attribute profiles using the MToS(VSDAP). For each method, the same attribute (moment of inertia), the same filtering parameters (0.1, 0.2, . . . , 1.0), and the same classifier (Random Forest) are used.