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arl

lists of most popular repositories for most favoured programming languages (according to StackOverflow)

bci-project

bmvc10

Recognizing human actions in still images: a study of bag-of-features and part-based representations

componentspd

Paper published in Computational Visual Media. \\ Title: Component SPD matrices: A low-dimensional discriminative data descriptor for image set classification

covariance_objectdetector

Implementation of Tuzel's Fast Covariance Object Detector

cvc

icovds

Paper accepted by ICCV Workshop (ICCVW) 2019. \\ Title : More About Covariance Descriptors for Image Set Coding: Log-Euclidean Framework based Kernel Matrix Representation.

learning

Learning Things Matlab OpenCV Armadillo OpenMP

machine-learning-for-computer-vision

Matlab projects : SVM, Adaboost, regression...

manifolds

marinhalucas

msr3d

The Most Infomative Joints

psssd

A Robust Distance Measure for Similarity-based classification on the SPD Manifold

regioncovariancedescriptor

Program using region covariance descriptor

repairingintrianglemesh

三角网格模型修补

research_tool_boxs

shipsear

sound_reconstruction

Sound reconstruction of ridge detected tonals

spcm-crp

Toolbox for spectral non-parametric clustering of SPD matices (covariance matrices and ellipsoids). Also contains code for EM-based GMM learning and inference for Bayesian non-parametric CRP-GMM.

stwp-master

matlab code for 3D skeleton action recognition

svm-using-gaussian-kernel-without-using-inbuild-functions-

Implementation of support vector machines(SVM) algorithm using Gaussian Kernel in MATLAB from scratch (without using in-build functions)

tfi-features_kbs_v.1.0

This repository contains the supplementary material for the Time frequency Image article in the journal Knowledge based systems

underwater-dataset

Dataset of Underwater images

underwater-hl

MATLAB code for color restoration of underwater images

underwater_datasets

Pointers to a collection of underwater datasets and relevant resources.

vaessl-doa

VAESSL: Train a computer to locate acoustic sources in reverberant environments with only few labels, using semi-supervised learning with variational autoncoders.