Open Source Mac Image Processing Software - Page 5
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Image Processing Software for Mac
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Premier Construction SoftwareRated #1 Construction Accounting Software by Forbes Advisor in 2022 & 2023. Our modern SAAS solution is designed to meet the needs of General Contractors, Developers/Owners, Homebuilders & Specialty Contractors.
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Bluetooth MeyMouse Accelerometer camera
J2me Accelerometer Camera light based over Bluettooth over mobile
J2me Accelerometer Camera light based over Bluettooth over mobile phone (Turn Your Old j2me Phone become slick accelerometer camera based MOUSE over bluetooth great for develope accelerometer camera based game Zzzzzzzz -
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CSSgram
CSS library for Instagram filters
Simply put, CSSgram is a library for editing your images with Instagram-like filters directly using CSS. What we're doing is adding filters to the images, as well as applying color and/or gradient overlays via various blending techniques to mimic filter effects. This means less manual image processing and more fun filter effects on the web! We are using pseudo-elements (i.e. :before and :after) to create the filter effects, so you must apply these filters on a containing element (i.e. not a content-block like <img>. The recommendation is to wrap your images in a <figure> tag. If you use custom naming in your CSS architecture, you can add the .scss files for the provided styles within your project and then @extend the filter effects within your style definitions. Mixins allow for multiple filter arguments to be passed into your classes. This is useful for if you want to add filters in addition to the ones provided (i.e. add a blur). -
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CT4AM
Tools for 3D image analysis of additively manufactured parts
CT4AM is a cross-platform toolkit for 3D-image processing in the domain of additive manufacturing for toolpath visualisation or correlation to tomographic data and related materials science. It is written in C++ and provides small, problem-specific applications for viewing, analysing and visualising of volumetric data from micro-CT scans with respect to the complex process parameters and characteristics of additive manufacturing. Moreover, data analysis and processing of G-code are key assets of the project. The main objective is not to provide yet another complex application for volume data visualization and medical image processing. Instead, CT4AM is a collection of small and simple apps that have already been successfully used to solve various problems in materials science and process visualization. You can either execute these tools directly or use their source codes to develop your own application for toolpath analysis or correlation. -
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ColorI-DT
A tool for the quantitative evaluation of differences in color images
ColorI-DT (Color Image Difference Tool): Colori-DT is an open-source tool developed in MATLAB and designed to measure color differences in images. It is intended to provide researchers and professionals with an accurate and user-friendly means of assessing color variation and discrepancies in digital images according to different metrics. Contacts: Filippo Piccinini (emails: filippo.piccinini@irst.emr.it; f.piccinini@unibo.it) Repository: https://sourceforge.net/p/colori-dt/ How to cite us when using the tool: Filippo Piccinini et al. “Colori-DT: an open-source tool for the quantitative evaluation of differences in microscopy color images”. -
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CompressPng
Java Groovy library plugin
CompressPng is a Java/Groovy library/plugin aimed at automating the compression of PNG images in an Android / JVM-based build environment. It is intended to help with automatically compressing images (e.g., for Android apps) during the build process, possibly integrating with Gradle or build scripts, thereby reducing the size of PNG assets integrated into apps. Support for usage via Gradle or other JVM build tools (via dependencies). Automatic PNG compression integrated into build workflows. Transparent integration (developers may not need manual steps). Minimal configuration surface (simple plugin / API). -
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The DIPS, \"Digital Image Processing with Sound\", is the set of Max objects that handles the real-time visual image processing events and the OpenGL functions in the jMax GUI programming environment.
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DThresh
Get perfect threshold value for image processing
With help of active trackbars you can change the values of RGB and HSV to threshold the live image feed from your camera! -
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The Data Fusion Peer is a multitier computer vision internet application. The system provides image processing, motion tracking, and visualization information. Application will convert data into 3-Deminsional and other digital environments.
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Skillfully - The future of skills based hiringSkillfully transforms hiring through AI-powered skill simulations that show you how candidates actually perform before you hire them. Our platform helps companies cut through AI-generated resumes and rehearsed interviews by validating real capabilities in action. Through dynamic job specific simulations and skill-based assessments, companies like Bloomberg and McKinsey have cut screening time by 50% while dramatically improving hire quality.
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The main objective of the proposed software solution is to improve the performance in dental diagnosis using infrared lock-in thermography and complex image processing algorithms for the determination of defective zones of teeth. The recorded amplitude profile of the thermal wave shows a maximum corresponding to a heat accumulation in the region where the open micro-gap is found, whereas a minimum in the phase profile is detected. These maxima/minima are convenient indicators that allow to locate surface breaking defects on teeth. For the purpose of emphasizing very small temperature variations surrounding the thin flaws, different image processing algorithms were developed and included within the DCD software solution. Authors: Bogdan Belean and Mihaela Streza Acknowledgement The software solution was supported by the Romanian UEFISCDI research grant PNII-RU-TE-2014-4-1507 (IMAGEDENT).
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EMGU Face Recognition
Using EMGU to perform Principle Component Analysis (PCA)
This article is designed to be the first in several to explain the use of the EMGU image processing wrapper. For more information on the EMGU wrapper please visit the EMGU website. You will start with 3 warnings for the references not being found. Expand the References folder within the solution explorer delete the 3 with yellow warning icons and Add fresh references to them. Face Recognition has always been a popular subject for image processing and this article builds upon the good work by Sergio Andrés Gutiérrez Rojas and his original article (codeproject). The reason that face recognition is so popular is not only it’s real world application but also the common use of principle component analysis (PCA). PCA is an ideal method for recognising statistical patterns in data. The popularity of face recognition is the fact a user can apply a method easily and see if it is working without needing to know to much about how the process is working. -
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EMGU Kalman Filter
Kalman Filter for EMGU Image Processing Applications
The Kalman filter is an algorithm which operates recursively on streams of noisy input data to produce a statistically optimal estimate of the underlying system state (Original Paper). The filter is named for Rudolf (Rudy) E. Kálmán, one of the primary developers of its theory. More information is available at Wikipedia, the Kalmn Filter was derived to solve the Wiener filter problem. The Wiener filter problem is to reduce the amount of noise present in a signal by comparison with an estimation of the desired noiseless signal. The discrete-time equivalent of Wiener's work was derived independently by Kolmogorov and published in 1941. Hence the theory is often called the Wiener-Kolmogorov filtering theory. In these examples the signal processes is the movement of the mouse and random data. While a simple application the algorithm can have numerous applications including image smoothing, edge tracking and optical flow to name a few. -
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Image Processing software for combining information from images of the same scene, but with illumination variations or small changes. This work is the result of the coursework of an Image Processing regular course at IMPA.
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Exposure
Learning infinite-resolution image processing with GAN and RL
Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model. ACM Transactions on Graphics (presented at SIGGRAPH 2018) Exposure is originally designed for RAW photos, which assumes 12+ bit color depth and linear "RGB" color space (or whatever we get after demosaicing). jpg and png images typically have only 8-bit color depth (except 16-bit pngs) and the lack of information (dynamic range/activation resolution) may lead to suboptimal results such as posterization. Moreover, jpg and most pngs assume an sRGB color space, which contains a roughly 1/2.2 Gamma correction, making the data distribution different from training images (which are linear). Exposure is just a prototype (proof-of-concept) of our latest research, and there are definitely a lot of engineering efforts required to make it suitable for a real product. -
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Calculating 2D-Gray-scale bitmaps Calculation of a 3D-rotation-body form a detected area by using different image processing algorithms. Developed for the German Aerospace Center (DLR). Used Projects: Substance (GUI)
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Fork PeRcolate
A collection of synthesis, signal processing, and image processing
A collection of synthesis, signal processing, and image processing objects originally authored by Dan Trueman and Luke Dubois. Pd port made by Olaf Matthes and help files ported by Martin Dupras. Fixed by Antoine Le Bras for APO33. http://apo33.org The official PeRColate web page can be found at: http://music.columbia.edu/PeRColate -
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FreeROI is a versatile image processing software developed for neuroimaging data. Its goal is to provide a user-friendly interface for neuroimaging researchers to visualize and analyze their data, especially in defining region of interest (ROI) for ROI analysis.
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GLIP-LIB
OpenGL Image Processing LIBrary
GLIP-Lib is an OpenGL image processing library written in C++. It features all the necessary tools to quickly build texture filters and pipelines and operate them on the GPU. -
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GPUImage 2
Framework for GPU-accelerated video and image processing
GPUImage 2 is the second generation of the GPUImage framework, an open source project for performing GPU-accelerated image and video processing on Mac, iOS, and now Linux. The original GPUImage framework was written in Objective-C and targeted Mac and iOS, but this latest version is written entirely in Swift and can also target Linux and future platforms that support Swift code. The objective of the framework is to make it as easy as possible to set up and perform realtime video processing or machine vision against image or video sources. By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS. -
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GPUVision is a framework for creating GPU based general purpose programs, image processing programs, and computer vision programs in C++. Supported libraries include matrix operations, graph partitioning, kernels, corner detection, edge detection etc.
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Gimp Gradient Extrapolate
fill a selection according to neighboring gradient area
Gimp Gradient Extrapolate is a plugin for Gimp 2.8. It fills gaps or removes objects, both of which are surrounded by a gradient like smooth image structure (e.g. sky). Refer to the wiki to see how to use it. Note, since this plug-in is written in python and involves optimization problems and non-filter operations, this script's runtime can be rather long, depending on the size of your image selection. Consider donating to this project: https://sourceforge.net/projects/gimp-g-e/donate -
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HIPAcc
Heterogeneous Image Processing Acceleration (HIPACC) Framework
HIPAcc development has moved to github: https://github.com/hipacc HIPAcc allows to design image processing kernels and algorithms in a domain-specific language (DSL). From this high-level description, low-level target code for GPU accelerators is generated using source-to-source translation. As back ends, the framework supports CUDA, OpenCL, and Renderscript. HIPAcc allows programmers to develop imaging applications while providing high productivity, flexibility and portability as well as competitive performance: the same algorithm description serves as basis for targeting different GPU accelerators and low-level languages. -
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A handwritten number recognition system was developed by using image processing and neural network technique. The system was developed in Java. Other applications which make use of image processing and neural network technique will be published too.
