drzhangreid

drzhangreid's Stars

• tensorflow/models

  Models and examples built with TensorFlow

  Language:Python76.9k2.7k7.3k45.8k

• huihut/interview

  📚 C/C++ 技术面试基础知识总结，包括语言、程序库、数据结构、算法、系统、网络、链接装载库等知识及面试经验、招聘、内推等信息。This repository is a summary of the basic knowledge of recruiting job seekers and beginners in the direction of C/C++ technology, including language, program library, data structure, algorithm, system, network, link loading library, interview experience, recruitment, recommendation, etc.

  Language:C++34.4k866637.9k

• TheAlgorithms/C

  Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.

  Language:C18.8k5201384.3k

• greyireland/algorithm-pattern

  算法模板，最科学的刷题方式，最快速的刷题路径，你值得拥有~

  Language:Go15.2k353332.6k

• yangkun19921001/Blog

  Android 面试宝典、数据结构和算法、音视频 (FFmpeg、AAC、x264、MediaCodec)、 C/C++ 、OpenCV、跨平台等学习记录。【0基础音视频进阶学习路线】

  Language:HTML2k611451

• greyireland/awesome-programming-books-1

  计算机经典书籍📚，保留书单

  1.4k325357

• interviewandroid/AndroidInterView

  Android面试2019年最新版（每日更新），音视频，Android高级，性能优化，算法，Flutter技术，FFmpeg OppenGl，资源混淆,插件化，组件化，OkHttp，Rxjava,架构师，Android架构

  866404241

• OpenHEVC/openHEVC

  HEVC decoder

  Language:C5315657192

• Turbo87/utm

  Bidirectional UTM-WGS84 converter for python

  Language:Python4851451101

• dstl/Stone-Soup

  A software project to provide the target tracking community with a framework for the development and testing of tracking algorithms.

  Language:Python40337262134

• JirongZhang/DeepHomography

  Content-Aware Unsupervised Deep Homography Estimation

  Language:Python335255757

• YeRen123455/Infrared-Small-Target-Detection

  Language:Python32576048

• tynguyen/unsupervisedDeepHomographyRAL2018

  Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model

  Language:Python273123281

• antonilo/unsupervised_detection

  An Unsupervised Learning Framework for Moving Object Detection From Videos

  Language:Python26762447

• Tianfang-Zhang/awesome-infrared-small-targets

  List of awesome infrared small targets detection methods!

  2246238

• YimianDai/sirst

  A dataset constructed for single-frame infrared small target detection

  1693731

• wanghuanphd/MDvsFA_cGAN

  The tensorflow and pytorch implementations of the MDvsFA_cGAN model which is proposed in ICCV2019 paper "Huan Wang, Luping Zhou and Lei Wang. Miss Detection vs. False Alarm: Adversarial Learing for Small Object Segmentation in Infrared Images. International Conference on Computer Vision, Oct.27-Nov.2,2019. Seoul, Republic of Korea".

  Language:Python10511226

• ucas-vg/Anti-UAV

  Served as a large-scale multi-modal benchmark, Anti-UAV drives the future research on the frontiers of tracking UAVs in the wild. With the above innovations and contributions, we have organized the CVPR 2020 Workshop on the 1st Anti-UAV Challenge. These contributions together significantly benefit the community.

  787610

• bes-dev/VIBE

  VIBE Background Subtractior

  Language:C++678242

• Gosivn/H264Analysis

  Language:C473018

• vision4robotics/DCFTracking4UAV

  426

• YimianDai/DENTIST

  Language:MATLAB423513

• breadcake/unsupervisedDeepHomography-pytorch

  Pytorch implementation of Unsupervised Deep Homography.

  Language:Python36265

• uoip/SpectralResidualSaliency

  C++/Python implementation of spectral residual saliency detection algorithm

  Language:C++303311

• ZhexuanZhou/MDvsFA

  PyTorch implementation of ICCV2019 paper Miss Detection vs. False Alarm: Adversarial Learing for Small Object Segmentation in Infrared Images.

  Language:Python246

• wrdoct/mul_radar_fus

  多雷达航迹关联

  Language:C++144

• yuanyc06/rr

  Source code of the CVPR 2015 paper "Robust saliency detection via regularized random walks ranking"

  Language:MATLAB12201

• Flocculus/Saliency-Detection-Algorithm-via-Multi-Level-Graph-Structure-and-Accurate-Background-Queries-Select

  In the field of saliency detection, many graph-based algorithms use boundary pixels as background seeds to estimate the background and foreground saliency,which leads to significant errors in some of pictures. In addition, local context with high contrast will mislead the algorithms. In this paper, we propose a novel multilevel bottom-up saliency detection approach that accurately utilizes the boundary information and takes advantage of both region-based features and local image details. To provide more accurate saliency estimations, we build a three-level graph model to capture both region-based features and local image details. By using superpixels of all four boundaries, we first roughly figure out the foreground superpixels. After calculating the RGB distances between the average of foreground superpixels and every boundary superpixel, we discard the boundary superpixels with the longest distance to get a set of accurate background boundary queries. Finally, we propose the regularized random walks ranking to formulate pixel-wise saliency maps. Experiment results on two public datasets indicate the significantly promoted accuracy and robustness of our proposed algorithm in comparison with 7 state-of-the-art saliency detection approaches.

  Language:MATLAB6202

• pzins/Parallel_ViBe

  ViBe : background substraction algorithm

  Language:C++5304

• ostadabbas/fRMC

  Background Subtraction via Fast Robust Matrix Completion (ICCVW2017)

  Language:Matlab45