Open Source Image Processing Software - Page 13

Image Processing Software

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  • 1
    MatrixUser

    MatrixUser

    A Multi-functional GUI-based Program for Image Processing and Analysis

    The MatrixUser project is moving to GitHub, the latest version can be obtained from https://leoliuf.github.io/MatrixUser/ Most of the medical images (e.g. CT, MRI, PET, etc.) comprises multiple frames which represent slices, phases, timing etc. from the same imaging object. Those images can be saved as multidimensional matrices in Matlab thanks to Matlab's powerful support of multidimensional data representation. However, within Matlab, most of image manipulation functions are limited or tailored for processing two-dimensional matrix. The MatrixUser is a software package which features functions designed and optimized specifically for manipulating multidimensional real or complex data matrix. MatrixUser provides a nice graphical environment for easily performing image analysis tasks including multidimensional image display, matrix (image stack) processing and rendering etc. MatrixUser is a great lightweight tool for users who are working in image processing field under Matlab.
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    An extension to the MIPAV (Medical Image Processing, Analysis, and Visualization) plug-in framework that allows the user to design and execute pipelines, which are multi-stage processing tasks.
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    MedImage

    Medical Image Processing SW Development

    Medical Image Processing SW Development
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    Medical Data Segmentation Toolkit
    MDSTk is a collection of 2D/3D image processing tools aimed at medical images. It contains routines for volume data processing (3D filtering, segmentation, etc.) as well as fast low-level vector graphics library for surface and tetrahedral meshing. MDSTk has been forked by 3Dim Laboratory s.r.o. to provide better support and further push its development forward. Please check VPL (Voxel Processing Library) project available on Bitbucket (https://bitbucket.org/3dimlab/vpl).
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  • 5
    A application with simple image processing functions which are used for medical image analysis / image analysis in general as a part of my academic project. The functions are written from scratch from various image processing algorithms.
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  • 6
    Land Mines detection using Image Processing
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  • 7

    Miracle

    image processing project

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    Written in Python and the Numeric package. It supports the examples of the book: Dougherty and Lotufo, Hands-on Morphological Image Proc., SPIE, 2003, ISSN=0-8194-4720-X. This toolbox has been continued at www.adessowiki.org under name ia870.
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  • 9
    The aim of this project is to offer an image processing library dedicated to Mathematical Morphology
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  • 11
    MyDiary1000

    MyDiary1000

    Its A Diary/TextEditor that encrypts your text data using images

    This is a diary that allows you to digitally write entries and view them. What makes this a masterpiece from its counterparts is how user data is created and abstracted. User data is protected and stored in PNG images, the image it self is not encrypted but the data inside the image is encrypted. If you open any one of the created PNG images, they look like blank images!. These "normal" looking images protect your information from prying eyes. The simplicity and elegance of my program stores your data(password and entry) locally and hidden from everyone.
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  • 12

    NEtCAT NanoSurface Analyzer

    NanoSurface Analyzertool is designed for the STM image analysis

    NanoSurface Analyzer tool is designed for the analysis and measurement of surface data at the nanoscale. This application is developed to support researchers working with surface data by providing tools for reading, processing, and analyzing data.
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    A gathering of state-of-the-art tools for GPU based image processing. They are the sourcecode for related research articles, and provide the basis for own experimentation. All are being implemented forNVidia GPUs in Linux, hence the name "nvision" ;)
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  • 14
    NicPic

    NicPic

    This software can be used to process digital image.

    This software can be used to process digital image.
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  • 15
    A suite of basic image processing applets for typical Nuclear Medicine studies.
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  • 16
    3D medical image platform for visualization and image processing. Segmentation with Levels sets. Surface reconstruction with marching Cubes, texture Mapping and Raycasting,DICOM support. Integration environment for VTK, ITK and vtkInria3D under wxWidgets
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  • 17
    A extensible suite of tools for image capture and processing in a multi platform enviornment.
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  • 18
    Open Cezeri Library

    Open Cezeri Library

    Effective Linear Algebra and Computer Vision Library with JAVA

    OCL stands for Open Cezeri Library (yet another linear algebra and matrix library). This library provides rapid coding as matlab ease of use. To learn for library please try to use test examples at OpenCezeriLibrary\test\test. It is originally developed at el-cezeri laboratory of Siirt University, in order to establish generic framework of reusable components and software tools for machine vision, machine learning, AI and robotic applications. Currently, it holds following main concepts 1- Vision: It can access web cams, imaging source industrial cameras for manuel settings and advanced issues. Studies on accesing Leapmotion and Kinect is still under-development. 2- Machine learning: It uses Weka Software tool and some personel coded ML algorithms 3- CMatrix: Special matrix library called as CMatrix meaning Cezeri Maztrix Class. Actually it is regarded as the core of the OCL. CMatrix supports fluent interface and method chaining.
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  • 19
    OpenCMISS-Zinc

    OpenCMISS-Zinc

    A Library for Interactive Modelling and Visualisation

    The OpenCMISS-Zinc Library (opencmiss.org/zinc), or Zinc, is a software library for building interactive graphical modelling and visualisation applications. Models are represented in Zinc as mathematical fields defined over domains, including finite elements with support for high-order basis functions, complex parameter mappings and time variation, and image-based fields. Further fields can be defined by mathematical expressions and algorithms on existing fields, including image processing filters. Zinc model data structures are dynamic, supporting interactive applications which programmatically create, destroy and modify parts of models.
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  • 20
    Libraries for rapid development of Augmented Environment / Reality systems. Cross-platform, though hardware interfacing is currently provided mainly for Windows. Provides encapsulated video capture, image processing, multi-threading and OpenGL renderer.
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  • 21
    OpenPR
    OpenPR stands for Open Pattern Recognition project and is intended to be an open source library for algorithms of image processing, computer vision, natural language processing, pattern recognition, machine learning and the related fields.
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  • 22
    A Photoshop-like open source pixel-level image processing software. Due to the extremely slow access from my city to sourceforge, this project has been moved to CodePlex.
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  • 23
    OpenPollution is an open distributed system based on CORBA providing air-pollution measurement, data mining and image processing. It uses C++ and Java languages. and supports heterogenous systems such as Linux, Windows, and Windows CE.
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  • 24
    PHP Identicons
    A lightweight PHP implementation of Don Park's original identicon code for visual representation of MD5 hash values. The program uses the standard PHP GD library for image processing.
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    PI-Based Image Encoder / Converter

    PI-Based Image Encoder / Converter

    Python code able to convert / compress image to PI (3.14, π) Indexes

    Image processing tool that encodes pixel data as indices within the first 16.7 million digits of PI (π). Features high-performance Numba-accelerated search and a signature 'film-grain' aesthetic upon reconstruction. ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro (23H2/24H2) Software: Python 3.14.3 + Numba JIT (latest) Results (Lower is better) Platform / OS CPU Time (Seconds) macOS (Native) Apple M2 52.151311 s (in default setup) Fedora Linux Intel Core Ultra 5 225F 58.536457 s (in default Power Management: Balanced) Windows 11 Intel Core Ultra 5 225F 59.681427 s (important! Power Management set as High Performance .. in Balanced is slow)
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