Search Results for "morphological image processing" - Page 29
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Showing 725 open source projects for "morphological image processing"
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Prolog Image Processing is a (initially) MacOS based extension to OpenProlog which allows to use image processing function in the context of Prolog.
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An image processing environment, based in Java, and developed on the concept of filters applied to an input. Maybe it will work out, maybe it won't. But it's better then wasting space on SourceForge for an old game...
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C++ library that implements basic data structures and algorithms for computer vision and image processing applications
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This program will traverse a directory tree, processing jpeg image files to produce neat and easily navigatable HTML image galleries. qgallery will automatically generate thumbnails, ando can also ptionally downsize images to ensure that they fit the
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Hylafax Job Control Langauge (HJCL) is a batch processing perl script front end for Hylafax using PERL MAGICK and IMAGE MAGICK. Scans a directory for fax jobs and submits them to Hylafax.
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The IPX Gimp Plugins package contains many plugins for the Gimp that are geared towards image analysis, such as: image sharpening, edge detection, region growing, and a number of morphological algorithms.
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Foresight is a LGPLed wrapper that currently brings together the Intel IPL, Intel OpenCV, ImageMagick, and the Matrox MIL. The ultimate goal is to provide a standard interface for any number of acquisition and image processing libraries.
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golib
C++ collection mostly for image processing
libGo is a C++ class library containing all kinds of things that proved useful to me. Included are: - Linear algebra, using LAPACK and CBLAS - V4L(1) image grabber - Multithreading - Image containers (up to 3D) - Some simple optimisation code - Python embedding helper - Matlab interface - .. and other things, have a look at the HTML documentation! golib grew over many years, things I had use for have been added now and then. Some parts are better taken care of than others. If... -
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openlpr is a open source project to implement license plate recognition in Win32 and Linux platform. It will provide the basic image processing to locate plate and character split and a neural network design to recognize character.
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Various projects from Digital Image Processing and Computer Graphics club of Warsaw University of Technology.
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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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Volumetric is a 3-D image processing tool. It reads point cloud data, expands it to a volumetric space with highly effective memory usage, and filter out necessary points.
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geoscipy
Python-accessible toolkit for Geoscience & Remote Sensing Applications
...There is also a Python API that provides a procedural as well as an object-oriented interface to these functions. A Python GUI for interacting with such datasets is also part of the project. While there are other open-source GIS, and image-processing packages available, this one is designed to be comprehensive, work on 3 major platforms, user-extensible, fast, and able to handle huge datasets. Click the Blog tab for more info. -
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This project aims to development a universal media codecs for video/audio/image processing. It use same interfaces for all codecs. And it target system is embedded system. The codecs will be include: MPEG2, MPEG4, H264, AAC, VC-1, AVS.
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NDia aims to provide users with a way to watch animated videos in simulated 3d by creating a multi-layered anaglyph (Red/Blue glases-style image) of a playing movie. This will allow users to watch any animated video file as if it had been created in 3d
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KinectStreamer
Streams data from 3D cameras over a network.
This is an application that streams data from the Microsoft Kinect or cameras like it over a network. The program is Intended to be used in robotics applications where the controller cannot use such cameras directly due to hardware/software limitations--such as lacking usb ports or appropriate drivers--or in situations where the camera is not in close proximity to the device that needs to access it. Given that the controller can accept data from over the network, another embedded controller... -
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Ministral 3 3B Base 2512
Small 3B-base multimodal model ideal for custom AI on edge hardware
...It supports dozens of languages, making it practical for multilingual, global, or distributed environments. With a large 256k token context window, it can handle long documents, extended inputs, or multi-step processing workflows even at its small size. -
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MiMo-V2.5
Omnimodal AI model for agents, coding, and long-context tasks
MiMo-V2.5 is a native omnimodal large language model developed by Xiaomi, designed for advanced agentic workflows, multimodal reasoning, and long-context processing. Built on a Mixture-of-Experts architecture with approximately 309B total parameters and around 15B activated per inference, it balances high capability with efficient execution. The model natively processes text, images, video, and audio within a unified system, enabling cross-modal understanding and complex task execution in a... -
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Ministral 3 8B Instruct 2512
Compact 8B multimodal instruct model optimized for edge deployment
Ministral 3 8B Instruct 2512 is a balanced, efficient model in the Ministral 3 family, offering strong multimodal capabilities within a compact footprint. It combines an 8.4B-parameter language model with a 0.4B vision encoder, enabling both text reasoning and image understanding. This FP8 instruct-fine-tuned variant is optimized for chat, instruction following, and structured outputs, making it ideal for daily assistant tasks and lightweight agentic workflows. Designed for edge deployment,... -
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Ministral 3 14B Instruct 2512
Efficient 14B multimodal instruct model with edge deployment and FP8
Ministral 3 14B Instruct 2512 is the largest model in the Ministral 3 family, delivering frontier performance comparable to much larger systems while remaining optimized for edge-level deployment. It combines a 13.5B-parameter language model with a 0.4B-parameter vision encoder, enabling strong multimodal understanding in both text and image tasks. This FP8 instruct-tuned variant is designed specifically for chat, instruction following, and agentic workflows with robust system-prompt...
