jvision Goto Github PK

a-light-and-fast-face-detector-for-edge-devices

A light and fast one class detection framework for edge devices. We provide face detector, head detector, pedestrian detector, vehicle detector......

bgslibrary

A Background Subtraction Library

burst-cvpr-2019

Iterative Residual CNNs for Burst Photography Applications

caffe-android-opencl-fp16

Optimised Caffe with OpenCL supporting for less powerful devices such as mobile phones

characterness

Code of "Characterness: An Indicator of Text in the Wild", IEEE Transcations on Image Processing

cmcode

Ming-Ming Cheng's open source projects

cpm

Coarse-to-fine PatchMatch for dense correspondence

cvblob

Automatically exported from code.google.com/p/cvblob

demosaicnet

An implementation of Deep Joint Demosaicking and Denoising - SiGGRAPH Asia 2016

easydarwin

Open Source Streaming Server

easypr

An open source project for chinese plate recognition. It aims to be Easy, Flexible, and Accurate. Welcome to contribute your expertise !

easytrader

提供券银河／华泰客户端/雪球的基金、股票自动程序化交易以及自动打新，支持跟踪 joinquant /ricequant 模拟交易 和 实盘雪球组合, 量化交易组件

esvmtestcpp

Testing pipeline for Exemplar-SVM in C++

fast-neural-style

Feedforward style transfer

ffld

Charles Dubout's Accelerated Deformable Part Model

flandmark

Open-source implementation of facial landmark detector

flowonthego

Fast, accurate optical flows on mobile GPUs.

fsrcnn-tensorflow

An implementation of the Fast Super-Resolution Convolutional Neural Network in TensorFlow

gogh-figure

Fast, Lightweight Style Transfer using Deep Learning

handheld-multi-frame-super-resolution

An implementation of the paper Handheld Multi-Frame Super-Resolution

hdr-plus

HDR+ Implementation

hwgq

hyperlpr

基于深度学习高性能中文车牌识别 High Performance Chinese License Plate Recognition Framework.

image_deblur_code

patch selection 1st

jalali-lab-implementation-of-raisr

Implementation of RAISR (Rapid and Accurate Image Super Resolution) algorithm in Python 3.x by Jalali Laboratory at UCLA. The implementation presented here achieved performance results that are comparable to that presented in Google's research paper (with less than ± 0.1 dB in PSNR). Just-in-time (JIT) compilation employing JIT numba is used to speed up the Python code. A very parallelized Python code employing multi-processing capabilities is used to speed up the testing process. The code has been tested on GNU/Linux and Mac OS X 10.13.2 platforms.

jvisionflandmark

play with

lapsrn-paper

a tensorflow implementation of lapSRN close to the PAMI version

lcnn_jdd

Code for Lightweight Deep Residue Learning for Joint Color Image Demosaicking and Denoising (ICPR2018 Oral)

librtprocess

A project to make RawTherapee's processing algorithms more readily available.

live555-for-win32

Automatically exported from code.google.com/p/live555-for-win32