Displaying 1 to 15 from 15 results


  •    Javascript

Sifter is a client and server-side library (via UMD) for textually searching arrays and hashes of objects by property – or multiple properties. It's designed specifically for autocomplete. The process is three-step: score, filter, sort. Seaching will provide back meta information and an "items" array that contains objects with the index (or key, if searching a hash) and a score that represents how good of a match the item was. Items that did not match will not be returned.

OpenPano - OpenPano: Automatic Panorama Stitching From Scratch

  •    C++

OpenPano is a panorama stitching program written in C++ from scratch (without any vision libraries). It mainly follows the routine described in the paper Automatic Panoramic Image Stitching using Invariant Features, which is also the one used by AutoStitch. Eigen, CImg and FLANN are header-only, to simplify the compilation on different platforms. CImg and libjpeg are only used to read and write images, so you can easily get rid of them.

vim-grepper - :space_invader: Helps you win at grep.

  •    Vim

Use your favorite grep tool (ag, ack, git grep, ripgrep, pt, sift, findstr, grep) to start an asynchronous search. All matches will be put in a quickfix or location list. This plugin works with Vim and Neovim on Unix-like systems. It's mostly working on Windows as well.

CudaSift - A CUDA implementation of SIFT for NVidia GPUs (1.6 ms on a GTX 1060)

  •    Cuda

This is the fourth version of a SIFT (Scale Invariant Feature Transform) implementation using CUDA for GPUs from NVidia. The first version is from 2007 and GPUs have evolved since then. This version is slightly more precise and considerably faster than the previous versions and has been optimized for Kepler and later generations of GPUs. On a GTX 1060 GPU the code takes about 1.6 ms on a 1280x960 pixel image and 2.4 ms on a 1920x1080 pixel image. There is also code for brute-force matching of features that takes about 2.2 ms for two sets of around 1900 SIFT features each.

sift.el - A front-end for sift

  •    Emacs

sift.el allows you to search using sift from inside Emacs.

socyl - The emacs frontend for several search tools (ag, pt, sift, ripgrep, ...)

  •    Emacs

The emacs frontend for several search tools (ag, pt, sift, ripgrep, ...)

Reconhecimento-Facial - Repositório utilizado para armazenar algoritmos de reconhecimento facial

  •    Python

Este repositório foi criado com a finalidade de compartilhar os algoritmos desenvolvidos durante o trabalho de mestrado intitulado "Comparação de Técnicas de Reconhecimento Facial para Identificação de Presença em um Ambiente Real e Semicontrolado", com o objetivo de facilitar e tornar possíveis futuras replicações dos experimentos realizados. O trabalho desenvolvido faz parte da linha de pesquisa de "Monitoramento de Presença" do projeto "Ensino e Monitoramento de Atividades Físicas via Técnicas de Inteligência Artificial" (Processo 2014.1.923.86.4, publicado no DOE 125(45), em 10/03/2015), realizado conjuntamente pela Universidade de São Paulo, Faculdade Campo Limpo Paulista e Academia Central Kungfu-Wushu.

popsift - PopSift is an implementation of the SIFT algorithm in CUDA.

  •    Cuda

PopSift is an implementation of the SIFT algorithm in CUDA. PopSift tries to stick as closely as possible to David Lowe's famous paper (Lowe, D. G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2), 91–110. doi:10.1023/B:VISI.0000029664.99615.94), while extracting features from an image in real-time at least on an NVidia GTX 980 Ti GPU. PopSift has been developed and tested on Linux machines, mostly a variant of Ubuntu, but compiles on MacOSX as well. It comes as a CMake project and requires at least CUDA 7.0 and Boost >= 1.55. It is known to compile and work with NVidia cards of compute capability 3.0 (including the GT 650M), but the code is developed with the compute capability 5.2 card GTX 980 Ti in mind.

ezSIFT - ezSIFT: An easy-to-use standalone SIFT library written in C/C++

  •    C++

The SIFT (scale-invariant feature transform) algorithm is considered to be one of the most robust local feature detector and description methods. Most of the open-source SIFT implementations rely on some 3rd-party libraries. Some of them even rely on a few different large libraries. These dependencies make the installation, compilation and usage not easy. The ezSIFT library provides a standalone and lightweight SIFT implementation written in C/C++. The ezSIFT is self-contained, and does not require any other libraries. So it is easy to use and modify. Besides, the implementation of the ezSIFT is straightforward and easy to read.

mods-light-zmq - MODS with external deep descriptors/detectors

  •    C++

This is MODS version, which allows you using state-of-the-art deep descriptors like HardNet without linking MODS to any of deep learning library. It contains very small number of detectors and descriptors implemented inside -- for easier compilation. Instead it uses zeromq library for communication with separately run CNN daemons. Examples with python PyTorch AffNet and HardNet++ descriptors is provided, but you can use any language and any DL package you like, just modify corresponding scripts. Don`t forget to kill server process after work done.

pytorch-sift - PyTorch implementation of SIFT descriptor

  •    Jupyter

This is an differentiable pytorch implementation of SIFT patch descriptor. It is very slow for describing one patch, but quite fast for batch. It can be used for descriptop-based learning shape of affine feature.

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