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lie_utils

Matlab Lie group utilities: flows and differential operators on GL(n), SE(2), SE(3), SO(3), S^3

lieopt

Ego-motion technique based on the optimization of SE(3) transformations on a manifold by using Lie groups and algebras

lifeclef

Task Overview Following the success of the four previous plant identification tasks (ImageCLEF 2011-13 ; LifeCLEF 2014), we are glad to organize this year a new challenge dedicated to botanical data. The task will be focused on tree, herbs and ferns species identification based on different types of images. Its main novelties compared to the last years will by : -"use of external resources" : it will be possible to use more external online resources (but strictly forbidden to used data from Tela Botanica website), as training data to enrich the provided one, - "species number" : the number of species (about 1 000 species), which is an important step towards covering the entire flora of a given region. Multi-image query The motivation of the task is to fit better with a real scenario where one user tries to identify a plant by observing its different organs, such as it has been demonstrated in [MAED2012]. Indeed, botanists usually observe simultaneously several organs like the leaves and the fruits or the flowers in order to disambiguate species which could be confused if only one organ were observed. Moreover, if only one organ is observed, such as the bark of a deciduous plant during winter where nothing else is observable, then the observation of this organ with several photos related to different point of views could be more informative than only one point of view. Thus, contrary to the 3 first years, the species identification task won't be image-centered but OBSERVATION-centered. The aim of the task is be to produce a list of relevant species for each observation of a plant of the test dataset, i.e. one or a set of several pictures related to a same event: one same person photographing several detailed views on various organs the same day with the same device with the same lightening conditions observing one same plant.

lift

Code release for the ECCV 2016 paper (placeholder)

lifting-from-the-deep-release

Implementation of "Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image"

light-field-video

Light field video applications (e.g. video refocusing, focus tracking, changing aperture and view)

light-matrix

A Light-weight and Fast Template Matrix Library

light-simd

A light weight library for SIMD based computation

light_field_blind_motion_deblurring

CVPR 2017 Paper: Light Field Blind Motion Deblurring

lightfield

Light field viewer and virtual light field camera

lightfield_sfr

Matlab code for MS thesis on SFR (spatial frequency response) measure for light field cameras

lightfieldsaliency_ijcai15

to compute saliency maps from light-field data

lightgbm

LightGBM is a fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithm, used for ranking (learning to rank), classification and many other machine learning tasks.

lighthouse

Open source Vive Lighthouse tracking

lighthouse-1

auditing and performance metrics for Progressive Web Apps

lightlda

Scalable, fast, and lightweight system for large-scale topic modeling

lightlda-1

Distributed LDA, takes raw text as input and outputs topic word table.

lightmetrica-v2

A modern, research-oriented renderer

lightnet

deep learning in hundreds of lines of code

ligra

Ligra: A Lightweight Graph Processing Framework for Shared Memory

lillicon

Research Prototype SVG Editor for Transient Widget idea

lime

lime (Library for Image Editing)

lime-1

Lime: Explaining the predictions of any machine learning classifier

linalg

line

LINE: Large-scale information network embedding

line3d

Line3D - Multi-View Stereo using Line Segments

line3dpp

Line3D++ - Multi-View Stereo using Line Segments

linear-svm-and-hog

Implementation of linear SVM and HOG

linear-svm-on-top-of-cnn-example

Simple example showing how to use intermediate CNN layer activations as feature vectors for training a linear SVM, to create a custom image classifier

linearizedgp

Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation.