Posts about SLIP

2017-10-06 Improving calls to the LogGabor library

To code image as edges, for instance in the SparseEdges sparse coding scheme, we use a model of edges in images. A good model for these edges are bidimensional Log Gabor filter. This is implemented for instance in the LogGabor library. The library was designed to be precise, but not particularly for efficiency. In order to improve its speed, we demonstrate here the use of a cache to avoid redundant computations.

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2016-11-29 Resizing a bunch of files using the command-line interface

generating databases

A set of bash code to resize images to a fixed size.

Problem statement: we have a set of images with heterogeneous sizes and we want to homogenize the database to avoid problems when classifying them. Solution: ImageMagick.

We first identify the size and type of images in the database. The database is a collection of folders containing each a collection of files. We thus do a nested recursive loop:

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2015-05-21 A simple pre-processing filter for image processing

When processing images, it is useful to avoid artifacts, in particular when you try to understand biological processes. In the past, I have used natural images (found on internet, grabbed from holiday pictures, ...) without controlling for possible problems.

In particular, digital pictures are taken on pixels which are most often placed on a rectangular grid. It means that if you rotate that image, you may lose information and distort it and thus get wrong results (even for the right algorithm!). Moreover, pictures have a border while natural scenes do not, unless you are looking at it through an aperture. Intuitively, this means that large objects would not fit on the screen and are less informative.

In computer vision, it is easier to handle these problems in Fourier space. There, an image (that we suppose square for simplicity) is transformed in a matrix of coefficients of the same size as the image. If you rotate the image, the Fourier spectrum is also rotated. But as you rotate the image, the information that was in the corners of the original spectrum may span outside the spectrum of the rotated image. Also, the information in the center of the spectrum (around low frequencies) is less relevant than the rest.

Here, we will try to keep as much information about the image as possible, while removing the artifacts related to the process of digitalizing the picture.

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