Open Source Computer Vision Libraries - Page 6

This repository is only used for the purpose of dataset storage for Team IGVC, IITK. For the relevant code, see our GitHub repositories. (https://github.com/igvc-iitk). The recorded data is used for testing various algorithms related to Computer Vision, SLAM, Motion Planning etc.

Computer Vision Project

this is a project for the computer vision class. We intend to track multi-targets while preserving the identity of the targets. We propose to represent this as a Flow optimization problem and solve it using K-shortest path algorithm.

Igovision is an experimental project investigating applications of computer vision technology to the board game Go.

The program for image classification,processing and computer vision.

i3D-converter creates a 3D representation from a couple of images (or a pair of stereo images). This program also performs other Computer Vision operations such as, edge and corner detection, image filtering, getting geometric shapes,...

This repository is a survey / code collection centered on deep learning–based image fusion (e.g. fusing infrared + visible light images, multi-modal fusion) methods. It catalogs many fusion algorithms (e.g. DenseFuse, FusionGAN, NestFuse, etc.), links to code implementations, and describes evaluation metrics. The repository includes a “General Evaluation Metric” subfolder containing objective fusion metrics. It is not a single monolithic tool, but rather a curated reference and aggregation of methods, code and performance comparisons in the domain of image fusion. Survey style description of method taxonomy, architectures, loss types. Compilation of many state-of-the-art image fusion methods (infrared + visible, multi-focus, multi-exposure). Survey style description of method taxonomy, architectures, loss types.

The project is aimed at automatic target following using a camera , a computer vision system and a microcontroller that moves the cam. The project should mainly work under linux and it might be ported into windows,

The present invention is an application for Android platform that recognizes Jakim’s Halal logo and acknowledge users whether certain logo is Jakim’s Halal logo or not using embedded system technology, real-time processing, computer vision, pattern recognition, and machine learning algorithm just by taking the picture of any Halal logo.

The present invention is an application for Android platform that recognizes Jakim’s Halal logo and acknowledge users whether certain logo is Jakim’s Halal logo or not using embedded system technology, real-time processing, computer vision, pattern recognition, and machine learning algorithm just by taking the picture of any Halal logo.

JOptical is a free library for generic computer vision applications. It will include most well known algorithms around this subject and expand on that.

This Java native library wraps OpenCV (Computer Vision Lib.) function cvMatchTemplate and implements methods for utilities result visualization. It allows efficient images template matching using Normalized Cross-Correlation (NCC) and others algorithms.

KLT-IV v1.0 has been designed to offer a user-friendly graphical user interface for the determination of river flow velocity and river discharge using videos acquired using a variety of fixed and moving platforms. KLT-IV employs optical flow and computer vision techniques for the tracking of features present on the river surface with pixel displacements being converted to real-world velocities using an optimised camera model approach. Movement of the platform can be accounted for when ground control points and/or stable features are present, or where the platform is equipped with GPS and IMU sensors. Please direct queries or comments to: https://groups.google.com/forum/#!forum/klt-iv-image-velocimetry-software

Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors. With Kornia we fill the gap between classical and deep computer vision that implements standard and advanced vision algorithms for AI. Our libraries and initiatives are always according to the community needs.

The Local Binary Pattern (LBP) is a texture operator that is used in several different computer vision applications and implemented in a variety of platforms. When selecting a suitable LBP implementation platform, the specific application and its requirements in terms of performance, size, energy efficiency, cost and developing time has to be carefully considered. This is a software toolbox that collects software implementations of the Local Binary Pattern operator in several platforms: - OpenCL for CPU & GPU - OpenCL for GPU (branchless) - C code optimized for ARM - OpenGL ES 2.0 shaders mobile GPUs - C code for TI C64x DSP core (branchless) - C code for TTA processor synthesis If you use the code somewhere, please cite: Bordallo López M., Nieto A., Boutellier J., Hannuksela J., and Silvén O. "Evaluation of real-time LBP computing in multiple architectures," Journal of Real Time Image Processing, 2014

Visual instruction tuning towards large language and vision models with GPT-4 level capabilities.

LTI-Lib is an object oriented computer vision library written in C++ for Windows/MS-VC++ and Linux/gcc. It provides lots of functionality to solve mathematical problems, many image processing algorithms, some classification tools and much more...

Computer Vision Library for LabVIEW Based on OpenCV

Web-based software to label objects in digital images for creating datasets for computer vision research.

According to the CBS news report, "if you use a computer more than two hours a day, you could be suffering from Computer Vision Syndrome (CVS)". The project's main objective is making a software that will help us protect our eyes from CVS.

This software allows to label events in videos (e.g. phoning, hand-shaking,...) and objects in images (e.g. person, car,...). The annotation of events and objects are saved into XML files and can be used for training and/or testing computer vision algorithms. In particular, LEVO can visualize and write PASCAL VOC-compatible annotation files. Some annotations from LABEL ME can be read as well. It has been tested on MS Windows. However, since it is Java-based code, it should works on Unix-like systems as well. VLC player is required to handle videos.

MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX delivers highly optimized open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions along with Convolution Neural Net Model Compiler & Optimizer supporting ONNX, and Khronos NNEF™ exchange formats. The toolkit allows for rapid prototyping and deployment of optimized computer vision and machine learning inference workloads on a wide range of computer hardware, including small embedded x86 CPUs, APUs, discrete GPUs, and heterogeneous servers. AMD OpenVX is a highly optimized open-source implementation of the Khronos OpenVX™ 1.3 computer vision specification. It allows for rapid prototyping as well as fast execution on a wide range of computer hardware, including small embedded x86 CPUs and large workstation discrete GPUs.

A Java and C++ collection of tools and source code for computer graphics and computer vision.

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. MMF is designed from ground up to let you focus on what matters, your model, by providing boilerplate code for distributed training, common datasets and state-of-the-art pre-trained baselines out-of-the-box. MMF is built on top of PyTorch that brings all of its power in your hands. MMF is not strongly opinionated. So you can use all of your PyTorch knowledge here. MMF is created to be easily extensible and composable. Through our modular design, you can use specific components from MMF that you care about. Our configuration system allows MMF to easily adapt to your needs.