Linux

Description

This is a software toolbox that extends the original BSIF code allowing the utilization of a GPU in Matlab to compute the features. It contains: -Matlab function to calculate BSIF in CPU -Matlab function extension to calculate BSIF in GPU -Pre-learnt filters -Usage instructions

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Description

This is a Java content-based image retrieval software components. It can be runned independantly or connected to a Cytomine server. Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. Content-based" means that the search algorithm analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. The CBIRetrieval library is: Incremental: You can add new images all over the time. Scalable: Run as many server as you want. Client performs search on all servers. Flexible: Run as a simple app (command line) or use the JAR in your own JVM app/server (java import) Opensource/Free: Apache 2.0 CBIRetrieval is a java library for CBIR, CBIRest is a server with a REST HTTP API with CBIRetrieval embedded. If you want to connect a software/webapp with a CBIR engine, you should use CBIRest. This is a fast multi-threaded and noSQL implementation of the algorithm published in: Incremental Indexing and Distributed Image Search using Shared Randomized Vocabularies Marée, Raphaël; Denis, Philippe; Wehenkel, Louis; Geurts, Pierre,in ACM Proceedings MIR 2010 (2010, March). It was applied on histology images and radiology images.

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Description

Matlab implementation (2014) of Local Binary Pattern. Used for texture image analysis with insensitivity to local average value. Good explanation here: http://www.ee.oulu.fi/research/imag/mvg/files/pdf/ICCV2009_tutorial.pdf

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Description

This package contains some MatLab tools for multi-scale image processing. Briefly, the tools include: - Recursive multi-scale image decompositions (pyramids), including Laplacian pyramids, QMFs, Wavelets, and steerable pyramids. These operate on 1D or 2D signals of arbitrary dimension. Data structures are compatible with the MatLab wavelet toolbox. - Fast 2D convolution routines, with subsampling and boundary-handling. - Fast point-operations, histograms, histogram-matching. - Fast synthetic image generation: sine gratings, zone plates, fractals, etc. - Display routines for images and pyramids. These include several auto-scaling options, rounding to integer zoom factors to avoid resampling artifacts, and useful labeling (dimensions and gray-range).

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Description

A workflow in Python to measure muscule fibers corresponding to the method used in Keefe, A.C. et al. Muscle stem cells contribute to myofibres in sedentary adult mice. Nat. Commun. 6:7087 doi: 10.1038/ncomms8087 (2015).

 

Example image:

 

muscleQNT/15536_2032_0.tif ...