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addressbook

bison

GNU Bison

boost-1.73-prebuilts

boost 1.73.0 msvc-14.2-64

bscontrol

clang

Mirror of official clang git repository located at http://llvm.org/git/clang. Updated every five minutes.

clanganalysis

clang's frontend analysis in action

cpp

deepcl

OpenCL library to train deep convolutional neural networks

design-patterns

All design patterns in modern C++

dns-reset-checker

Tools to assess the DNS security of web applications

face-recognition-using-opencv-in-python

Face is most commonly used biometric to recognize people. Face recognition has received substantial attention from researchers due to human activities found in various applications of security like airport, criminal detection, face tracking, forensic etc. Compared to other biometric traits like palm print, Iris, finger print etc., face biometrics can be non-intrusive. They can be taken even without user’s knowledge and further can be used for security based applications like criminal detection, face tracking, airport security, and forensic surveillance systems. Face recognition involves capturing face image from a video or from a surveillance camera. They are compared with the stored database. Face biometrics involves training known images, classify them with known classes and then they are stored in the database. When a test image is given to the system it is classified and compared with stored database. Face biometrics is a challenging field of research with various limitations imposed for a machine face recognition like variations in head pose, change in illumination, facial expression, aging, occlusion due to accessories etc.,. Various approaches were suggested by researchers in overcoming the limitations stated. 72 Automatic face recognition involves face detection, feature extraction and face recognition. Face recognition algorithms are broadly classified into two classes as image template based and geometric feature based. The template based methods compute correlation between face and one or more model templates to find the face identity. Principal component analysis, linear discriminate analysis, kernel methods etc. are used to construct face templates. The geometric feature based methods are used to analyze explicit local features and their geometric relations (elastic bung graph method). Multi resolution tools such as contour lets, ridge lets were found to be useful for analyzing information content of images and found its application in image processing, pattern recognition, and computer vision. Curvelets transform is used for texture classification and image de-noising. Application of Curvelets transform for feature extraction in image processing is still under research.

flex

The Fast Lexical Analyzer - scanner generator for lexing in C and C++

glowing-octo-cork

imgui_e3ed9ef1b

ImGui @ e3ed9ef1bf115532762e90a621d9a3b5c5b39e25 (v1.78-110-ge3ed9ef1b)

j--

Modified j-- Compiler

llvm

Mirror of official llvm git repository located at http://llvm.org/git/llvm. Updated every five minutes.

netdevkernels

NetDev manipulation

opencl-caffe

This is a Experimental version of OpenCL by AMD Research, we now recommend you to use The official BVLC Caffe OpenCL branch is over at Caffe branch now at https://github.com/BVLC/caffe/tree/opencl

openms

The codebase of the OpenMS project

openms-contrib-prebuilts

OpenMS contrib prebuilts - MSVC 14.2

quantile-filter

sereneborg-tf

smartpeak

Fast and Accurate CE-, GC- and LC-MS(/MS) Data Processing

smartpeak-docker

Docker files and helper scripts to build and run SmartPeak Computational Core Servers (SCCS).

smartpeak-hosted

Helm chart for the smartpeak-hosted service in Kubernetes.

test-tlb

Stupid memory latency and TLB tester

xilinx-tiny-cnn