Search Results for "image analysis" - Page 3
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Showing 72 open source projects for "image analysis"
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Marvin is an image processing framework that provides features for image and video frame manipulation, multithreading image processing, image filtering and analysis, unit testing, performance analysis and addition of new features via plug-in.
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Gamera is a framework for the creation of structured document analysis applications by domain experts. It combines a programming library with GUI tools for the training and interactive development of recognition systems.
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libcrn is document image processing library written in C++11 for Linux, Windows, Mac OsX and Google Android. It is a toolbox that allows to create easily software such as OCRs and layout analysis tools.
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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-... -
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Awesome Recurrent Neural Networks
A curated list of resources dedicated to RNN
A curated list of resources dedicated to recurrent neural networks (closely related to deep learning). Provides a wide range of works and resources such as a Recurrent Neural Network Tutorial, a Sequence-to-Sequence Model Tutorial, Tutorials by nlintz, Notebook examples by aymericdamien, Scikit Flow (skflow) - Simplified Scikit-learn like Interface for TensorFlow, Keras (Tensorflow / Theano)-based modular deep learning library similar to Torch, char-rnn-tensorflow by sherjilozair, char-rnn... -
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IMAGINE
Biological image viewer and processor
Detection, enumeration, and sizing of biological organisms by image analysis. -
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...The algorithm used for connected component labeling is: Chang, F. (2004). A linear-time component-labeling algorithm using contour tracing technique. Computer Vision and Image Understanding, 93(2), 206–220. doi:10.1016/j.cviu.2003.09.002 The ImageJ *Shape Filter Plugin* (see downloads) uses this library for flitering the blobs by its shape. If you are using IJBlob in a scientific publication, please cite: Wagner, T and Lipinski, H 2013. IJBlob: An ImageJ Library for Connected Component Analysis and Shape Analysis. ...
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ivantk
Image-based Vascular Analysis Toolkit
The Image-based Vascular Analysis Toolkit is a set of multiplatform C++ libraries for vascular analysis of (3D) medical images, typically CT or MRI. It can be considered as an extension of the Insight Toolkit (ITK) for vascular image analysis, with methods for detection, extraction and modeling of vascular structures. -
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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). ... -
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SURF-nanodots
Very basic computer vision program
This was my first big programming project. I'd like to clean it up and improve the back end design when time permits. See the wiki for known issues and more information. A computer vision program for analysis of magnetic data collected by a scanning probe microscope. Originated in summer 2007 as a collection of C compiled for Matlab (MEX) files and was eventually ported to a standalone C++ application with a GUI created in Qt. This program takes atomic and magnetic force microscope (AFM/MFM) image pairs as input and uses threshold segmentation to identify magnetic nanodots by intensity in the AFM image. ... -
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A toolkit for the optical recognition of Psaltiki 19th century music notation. It is based on and requires the Gamera document image analysis framework (http://gamera.sf.net).
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The purpose of this program is to teach a computer to classify plants via their leaves. You just need to input the image of a leaf(acquired from scanner or camera), then the computer can tell you what kind of plant it is.
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JAVA based tool for easy and realtime monitoring (RTM) of data streams or groups of data streams. This tool makes it easy to monitor inherent distributed or multi-threaded applications like e.g. agent systems, financial data, logistics, RFID Systems
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CVSharp (aka Computer Vision in C#) is a Computer Vision project. Until the present day just one part of the whole project was actually developed. It's called CVSharp Lab, an Image Processing Tool.
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This Java project creates a testing environment application to analyze an image at its low level features and suggest tags to clasify it using an ontology search based on the tags of similar images.
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A collection of Matlab functions and scripts for computing the saliency map for an image, for determining the extent of a proto-object, and for serially scanning the image with the focus of attention.
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An omnifont OCR software for KDE. Due to the fact that each step of the OCR process can be visualized you can get a quick idea of how OCR works and where the problems lie. However the program may be of minor/no use for end users in its current state.
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This library implements self-organizing neural networks, also called Kohonen Nets. They can be used for high dimensional data analysis. Example: content based image recognition ( CBIR ).
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Devstral Small 2
Lightweight 24B agentic coding model with vision and long context
Devstral Small 2 is a compact agentic language model designed for software engineering workflows, excelling at tool usage, codebase exploration, and multi-file editing. With 24B parameters and FP8 instruct tuning, it delivers strong instruction following while remaining lightweight enough for local and on-device deployment. The model achieves competitive performance on SWE-bench, validating its effectiveness for real-world coding and automation tasks. It introduces vision capabilities,... -
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Ministral 3 3B Base 2512
Small 3B-base multimodal model ideal for custom AI on edge hardware
Ministral 3 3B Base 2512 is the smallest model in the Ministral 3 family, offering a compact yet capable multimodal architecture suited for lightweight AI applications. It combines a 3.4B-parameter language model with a 0.4B vision encoder, enabling both text and image understanding in a tiny footprint. As the base pretrained model, it is not fine-tuned for instructions or reasoning, making it the ideal foundation for custom post-training, domain adaptation, or specialized downstream tasks.... -
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Ministral 3 3B Reasoning 2512
Compact 3B-param multimodal model for efficient on-device reasoning
Ministral 3 3B Reasoning 2512 is the smallest reasoning-capable model in the Ministal-3 family, yet delivers a surprisingly capable multimodal and multilingual base for lightweight AI applications. It pairs a 3.4B-parameter language model with a 0.4B-parameter vision encoder, enabling it to understand both text and image inputs. This reasoning-tuned variant is optimized for tasks like math, coding, and other STEM-related problem solving, making it suitable for applications that require logical reasoning, analysis, or structured thinking. Despite its modest size, the model is designed for edge deployment and can run locally, fitting in ~16 GB of VRAM in BF16 or under 8 GB of RAM/VRAM when quantized. ... -
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Ministral 3 14B Base 2512
Powerful 14B-base multimodal model — flexible base for fine-tuning
Ministral 3 14B Base 2512 is the largest model in the Ministral 3 line, offering state-of-the-art language and vision capabilities in a dense, base-pretrained form. It combines a 13.5B-parameter language model with a 0.4B-parameter vision encoder, enabling both high-quality text understanding/generation and image-aware tasks. As a “base” model (i.e. not fine-tuned for instruction or reasoning), it provides a flexible foundation ideal for custom fine-tuning or downstream specialization. The...
