Search Results for "image analysis algorithm" - Page 22

Perl application to split a jpeg file or a disk image containing jpeg data into jpeg marker blocks for later analysis and/or reassembly.

Image analysis tools using wxwindows (http://206.101.232.131/) Programme de traitements d'images utilisant la bibliotheque wxwindows

R based genetic algorithm for optimization, variable selection and other machine learning and statistical analysis approaches.

An elevator Algorithm analysis and design tool. The main goal of the project is to provide a platform to compare elevator control algorithms.

Barcode API to scan image files and acquire bar codes in 1-D format (EAN 13, Code39 and Code128)

A numerical library designed for n-dimensional image-processing written in Java.

Horos™ is a free, open source medical image viewer. The goal of the Horos Project is to develop a fully functional, 64-bit medical image viewer for OS X. Horos is based upon OsiriX and other open source medical imaging libraries. Horos is made available under the GNU General Public License, Version 3 (GPL-3.0).

MicMac is a software for solving image matching problems, specially those arising in geographic context. It is highly customizable at the algorithmic level and for the data input (image format and geo-localization).

A software application for the optical recognition, the superimposition and the collation of early music prints

DEAL (Domain Extraction ALgorithm) is an application for domain analysis. Its main purpose is to extract domain information from existing Java applications and to create different kinds of models from the extracted data, including domain-specific languages.

Purpose of SAWS is to facilitate process of web scraping by - 1) providing a pattern specification mechanism on top of normal regular expressions 2) and implementation of common matching algorithm to run specified pattern on given source for any matches.

A framework for image analysis and data analysis, aimed towards microscopy and the needs of current research.

Gemma 4 12B is Google DeepMind’s unified open-weight multimodal model designed for efficient local reasoning, coding, and multimodal understanding. Unlike other Gemma 4 models that rely on separate encoders, the 12B Unified model uses an encoder-free architecture that projects raw image patches and audio waveforms directly into the language model’s embedding space, reducing multimodal latency and simplifying fine-tuning. It supports text, image, audio, and video inputs with text output, making it useful for transcription, image understanding, video analysis, coding, and agentic workflows. The model has 11.95B parameters, 48 layers, a 256K-token context window, and support for over 140 languages. ...

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,...

Gemma 4 is Google DeepMind’s flagship dense open-weight multimodal model, designed for high-end reasoning, coding, agentic workflows, and multimodal understanding. The model contains approximately 30.7B parameters and supports text and image inputs with text generation output, while also processing video as image-frame sequences. Built as the most capable model in the Gemma 4 family, it combines strong reasoning performance with a large 256K-token context window and configurable thinking modes. Gemma 4 31B supports native function calling, structured outputs, and more than 140 languages, making it suitable for enterprise assistants, coding agents, document analysis, and multilingual applications. ...

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....

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. ...

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...