library

The OpenCV CUDA module is a set of classes and functions to utilize CUDA computational capabilities. It is implemented using NVIDIA* CUDA* Runtime API and supports only NVIDIA GPUs. The OpenCV CUDA module includes utility functions, low-level vision primitives, and high-level algorithms. The utility functions and low-level primitives provide a powerful infrastructure for developing fast vision algorithms taking advantage of CUDA whereas the high-level functionality includes some state-of-the-art algorithms (such as stereo correspondence, face and people detectors, and others) ready to be used by the application developers.

The CUDA module is designed as a host-level API. This means that if you have pre-compiled OpenCV CUDA binaries, you are not required to have the CUDA Toolkit installed or write any extra code to make use of the CUDA.

The module provides biological visual systems models (human visual system and others). It also provides derivated objects that take advantage of those bio-inspired models.

EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data.

EBImage is available through the Bioconductor software project (www.bioconductor.org). Strengths Lightweight Suitable for automated, scripted analyses All functions are documented with examples Modular links to R and Bioconductor software, notably imageHTS and cellHTS2 Community support via the Bioconductor mailing list Reproducible (image) analysis using the Sweave report-writing system

CellProfiler is free, open-source software for quantitative analysis of biological images.

CellProfiler is designed to enable biologists without training in computer vision or programming to quantitatively measure cell or whole-organism phenotypes from thousands of images automatically. The researcher creates an analysis pipeline from modules that find cells and cell compartments, measure features of those cells to form a rich, quantitative dataset that characterizes the imaged site in all of its heterogeneity. CellProfiler is structured so that the most general and successful methods and strategies are the ones that are automatically suggested, but the user can override these defaults and pull from many of the basic algorithms and techniques of image analysis to solve harder problems. CellProfiler is extensible through plugins written in Python or for ImageJ. Strengths: Cells, Neurons, C. Elegans, 2D Fluorescent images, high-throughput screening, phenotype classification, multiple stains/site, interoperability, extensibility, machine learning, segmentation Limitations: largely limited to 2D, not well suited to manually-guided analysis or content review, image size limitations