Joint parameterization and fitting of stroke clusters

Overview

StrokeStrip: Joint Parameterization and Fitting of Stroke Clusters

Dave Pagurek van Mossel1, Chenxi Liu1, Nicholas Vining1,2, Mikhail Bessmeltsev3, Alla Sheffer1

1University of British Columbia, 2NVIDIA, 3Université de Montréal

@article{strokestrip,
	title = {StrokeStrip: Joint Parameterization and Fitting of Stroke Clusters},
	author = {Pagurek van Mossel, Dave and Liu, Chenxi and Vining, Nicholas and Bessmeltsev, Mikhail and Sheffer, Alla},
	year = 2021,
	journal = {ACM Transactions on Graphics},
	publisher = {ACM},
	address = {New York, NY, USA},
	volume = 40,
	number = 4,
	doi = {10.1145/3450626.3459777}
}

StrokeStrip jointly parameterizes clusters of strokes (a) that, together, represent strips following a single intended curve (b). We compute the parameterization of this strip (c) restricted to the domain of the input strokes (d), which we then use to produce the parameterized intended curve (d).

Usage

./strokestrip input.scap [...args]

Additional optional arguments:

  • --cut: If your input strokes include sharp back-and-forth turns, this flag will use the Cornucopia library to detect and cut such strokes.
  • --debug: Generate extra SVG outputs to introspect the algorithm
  • --rainbow: Generate an SVG showing parameterized strokes coloured with a rainbow gradient (default is red-to-blue)
  • --widths: Generate fitted widths along with centerlines
  • --taper: Force fitted widths to taper to 0 at endpoints

Input format

Drawings are inputted as .scap files, which encode strokes as polylines. Strokes are contained in pairs of braces { ... }. Each stroke has a unique stroke id and a cluster id shared by all strokes that colleectively make up one intended curve. Polyline samples can omit pressure by setting it to a default value of 0.

#[width]	[height]
@[thickness]
{
	#[stroke_id]	[cluster_id]
	[x1]	[y1]	[pressure1]
	[x2]	[y2]	[pressure2]
	[x3]	[y3]	[pressure3]
	[...etc]
}
[...etc]

Example .scap inputs are found in the examples/ directory.

Stroke clusters for new .scap files can be generated using the StrokeAggregator ground truth labeling program.

Development

Dependencies

Gurobi

This package relies on the Gurobi optimization library, which must be installed and licensed on your machine. If you are at a university, a free academic license can be obtained. This project was build with Gurobi 9.0; if you are using a newer version of Gurobi, update FindGUROBI.cmake to reference your installed version (e.g. change gurobi90 to gurobi91 for version 9.1.)

Eigen 3

Ensure that Eigen is installed and that its directory is included in $CMAKE_PREFIX_PATH.

Building

StrokeStrip is configured with Cmake:

mkdir build
cd build
cmake ..
make
Owner
Dave Pagurek
Programmer and digital artist. MSc from UBC CS '21, UWaterloo Software Engineering '19.
Dave Pagurek
Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Christopher T. Chubb 35 Dec 21, 2022
Scalable machine learning based time series forecasting

mlforecast Scalable machine learning based time series forecasting. Install PyPI pip install mlforecast Optional dependencies If you want more functio

Nixtla 145 Dec 24, 2022
Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal, multi-exposure and multi-focus image fusion.

U2Fusion Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal (VIS-IR, medical), multi

Han Xu 129 Dec 11, 2022
Explainable Medical ImageSegmentation via GenerativeAdversarial Networks andLayer-wise Relevance Propagation

MedAI: Transparency in Medical Image Segmentation What is this repo This repo contains the code and experiments that are implemented to contribute in

Awadelrahman M. A. Ahmed 1 Nov 22, 2021
PyTorch Implementation for Deep Metric Learning Pipelines

Easily Extendable Basic Deep Metric Learning Pipeline Karsten Roth ([email 

Karsten Roth 543 Jan 04, 2023
Pytorch implementation of CVPR2021 paper "MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation"

MUST-GAN Code | paper The Pytorch implementation of our CVPR2021 paper "MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generat

TianxiangMa 46 Dec 26, 2022
Repository for open research on optimizers.

Open Optimizers Repository for open research on optimizers. This is a test in sharing research/exploration as it happens. If you use anything from thi

Ariel Ekgren 6 Jun 24, 2022
SAFL: A Self-Attention Scene Text Recognizer with Focal Loss

SAFL: A Self-Attention Scene Text Recognizer with Focal Loss This repository implements the SAFL in pytorch. Installation conda env create -f environm

6 Aug 24, 2022
Python and C++ implementation of "MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation". Accepted at LXCV @ CVPR 2021.

MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation This is a PyTorch and LibTorch implementation of MarkerPose: a

Jhacson Meza 47 Nov 18, 2022
Official PyTorch Implementation of Hypercorrelation Squeeze for Few-Shot Segmentation, arXiv 2021

Hypercorrelation Squeeze for Few-Shot Segmentation This is the implementation of the paper "Hypercorrelation Squeeze for Few-Shot Segmentation" by Juh

Juhong Min 165 Dec 28, 2022
Official repository for the paper "GN-Transformer: Fusing AST and Source Code information in Graph Networks".

GN-Transformer AST This is the official repository for the paper "GN-Transformer: Fusing AST and Source Code information in Graph Networks". Data Prep

Cheng Jun-Yan 10 Nov 26, 2022
Official code for "Mean Shift for Self-Supervised Learning"

MSF Official code for "Mean Shift for Self-Supervised Learning" Requirements Python = 3.7.6 PyTorch = 1.4 torchvision = 0.5.0 faiss-gpu = 1.6.1 In

UMBC Vision 44 Nov 21, 2022
MPI Interest Group on Algorithms on 1st semester 2021

MPI Algorithms Interest Group Introduction Lecturer: Steve Yan Location: TBA Time Schedule: TBA Semester: 1 Useful URLs Typora: https://typora.io Goog

Ex10si0n 13 Sep 08, 2022
PIKA: a lightweight speech processing toolkit based on Pytorch and (Py)Kaldi

PIKA: a lightweight speech processing toolkit based on Pytorch and (Py)Kaldi PIKA is a lightweight speech processing toolkit based on Pytorch and (Py)

336 Nov 25, 2022
A learning-based data collection tool for human segmentation

FullBodyFilter A Learning-Based Data Collection Tool For Human Segmentation Contents Documentation Source Code and Scripts Overview of Project Usage O

Robert Jiang 4 Jun 24, 2022
Python Actor concurrency library

Thespian Actor Library This library provides the framework of an Actor model for use by applications implementing Actors. Thespian Site with Documenta

Kevin Quick 177 Dec 11, 2022
Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks

Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks Stable Neural ODE with Lyapunov-Stable Equilibrium

Kang Qiyu 8 Dec 12, 2022
Object Detection with YOLOv3

Object Detection with YOLOv3 Bu projede YOLOv3-608 modeli kullanılmıştır. Requirements Python 3.8 OpenCV Numpy Documentation Yolo ile ilgili detaylı b

Ayşe Konuş 0 Mar 27, 2022
Neural Cellular Automata + CLIP

🧠 Text-2-Cellular Automata Using Neural Cellular Automata + OpenAI CLIP (Work in progress) Examples Text Prompt: Cthulu is watching cthulu_is_watchin

Mainak Deb 21 Dec 19, 2022
Prediction of MBA refinance Index (Mortgage prepayment)

Prediction of MBA refinance Index (Mortgage prepayment) Deep Neural Network based Model The ability to predict mortgage prepayment is of critical use

Ruchil Barya 1 Jan 16, 2022